Merge remote-tracking branch 'origin/master' into norm_norm_norm

pull/1014/head
Ross Wightman 3 years ago
commit 95cfc9b3e8

@ -23,6 +23,18 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
## What's New
### Jan 14, 2022
* Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
* Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features
* Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way...
* `mnasnet_small` - 65.6 top-1
* `mobilenetv2_050` - 65.9
* `lcnet_100/075/050` - 72.1 / 68.8 / 63.1
* `semnasnet_075` - 73
* `fbnetv3_b/d/g` - 79.1 / 79.7 / 82.0
* TinyNet models added by [rsomani95](https://github.com/rsomani95)
* LCNet added via MobileNetV3 architecture
### Nov 22, 2021
* A number of updated weights anew new model defs
* `eca_halonext26ts` - 79.5 @ 256
@ -255,10 +267,12 @@ All model architecture families include variants with pretrained weights. There
A full version of the list below with source links can be found in the [documentation](https://rwightman.github.io/pytorch-image-models/models/).
* Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
* BEiT - https://arxiv.org/abs/2106.08254
* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
* Bottleneck Transformers - https://arxiv.org/abs/2101.11605
* CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
* CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
* ConvNeXt - https://arxiv.org/abs/2201.03545
* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
* CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
* DeiT (Vision Transformer) - https://arxiv.org/abs/2012.12877
@ -276,11 +290,11 @@ A full version of the list below with source links can be found in the [document
* MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
* MobileNet-V2 - https://arxiv.org/abs/1801.04381
* Single-Path NAS - https://arxiv.org/abs/1904.02877
* TinyNet - https://arxiv.org/abs/2010.14819
* GhostNet - https://arxiv.org/abs/1911.11907
* gMLP - https://arxiv.org/abs/2105.08050
* GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
* Halo Nets - https://arxiv.org/abs/2103.12731
* HardCoRe-NAS - https://arxiv.org/abs/2102.11646
* HRNet - https://arxiv.org/abs/1908.07919
* Inception-V3 - https://arxiv.org/abs/1512.00567
* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
@ -288,7 +302,11 @@ A full version of the list below with source links can be found in the [document
* LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
* MLP-Mixer - https://arxiv.org/abs/2105.01601
* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
* FBNet-V3 - https://arxiv.org/abs/2006.02049
* HardCoRe-NAS - https://arxiv.org/abs/2102.11646
* LCNet - https://arxiv.org/abs/2109.15099
* NASNet-A - https://arxiv.org/abs/1707.07012
* NesT - https://arxiv.org/abs/2105.12723
* NFNet-F - https://arxiv.org/abs/2102.06171
* NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
* PNasNet - https://arxiv.org/abs/1712.00559
@ -314,6 +332,7 @@ A full version of the list below with source links can be found in the [document
* Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
* TResNet - https://arxiv.org/abs/2003.13630
* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
* Visformer - https://arxiv.org/abs/2104.12533
* Vision Transformer - https://arxiv.org/abs/2010.11929
* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
* Xception - https://arxiv.org/abs/1610.02357

@ -21,7 +21,7 @@ from functools import partial
from timm.models import create_model, is_model, list_models
from timm.optim import create_optimizer_v2
from timm.data import resolve_data_config
from timm.utils import AverageMeter, setup_default_logging
from timm.utils import setup_default_logging, set_jit_fuser
has_apex = False
@ -95,7 +95,8 @@ parser.add_argument('--precision', default='float32', type=str,
help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)')
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
help='convert model torchscript for inference')
parser.add_argument('--fuser', default='', type=str,
help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
# train optimizer parameters
@ -186,7 +187,7 @@ def profile_fvcore(model, input_size=(3, 224, 224), batch_size=1, detailed=False
class BenchmarkRunner:
def __init__(
self, model_name, detail=False, device='cuda', torchscript=False, precision='float32',
num_warm_iter=10, num_bench_iter=50, use_train_size=False, **kwargs):
fuser='', num_warm_iter=10, num_bench_iter=50, use_train_size=False, **kwargs):
self.model_name = model_name
self.detail = detail
self.device = device
@ -194,6 +195,8 @@ class BenchmarkRunner:
self.channels_last = kwargs.pop('channels_last', False)
self.amp_autocast = torch.cuda.amp.autocast if self.use_amp else suppress
if fuser:
set_jit_fuser(fuser)
self.model = create_model(
model_name,
num_classes=kwargs.pop('num_classes', None),
@ -311,10 +314,7 @@ class TrainBenchmarkRunner(BenchmarkRunner):
super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
self.model.train()
if kwargs.pop('smoothing', 0) > 0:
self.loss = nn.CrossEntropyLoss().to(self.device)
else:
self.loss = nn.CrossEntropyLoss().to(self.device)
self.loss = nn.CrossEntropyLoss().to(self.device)
self.target_shape = tuple()
self.optimizer = create_optimizer_v2(
@ -477,6 +477,7 @@ def decay_batch_exp(batch_size, factor=0.5, divisor=16):
def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
batch_size = initial_batch_size
results = dict()
error_str = 'Unknown'
while batch_size >= 1:
torch.cuda.empty_cache()
try:
@ -484,13 +485,13 @@ def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
results = bench.run()
return results
except RuntimeError as e:
e_str = str(e)
print(e_str)
if 'channels_last' in e_str:
print(f'Error: {model_name} not supported in channels_last, skipping.')
error_str = str(e)
if 'channels_last' in error_str:
_logger.error(f'{model_name} not supported in channels_last, skipping.')
break
print(f'Error: "{e_str}" while running benchmark. Reducing batch size to {batch_size} for retry.')
_logger.warning(f'"{error_str}" while running benchmark. Reducing batch size to {batch_size} for retry.')
batch_size = decay_batch_exp(batch_size)
results['error'] = error_str
return results
@ -532,13 +533,14 @@ def benchmark(args):
model_results = OrderedDict(model=model)
for prefix, bench_fn in zip(prefixes, bench_fns):
run_results = _try_run(model, bench_fn, initial_batch_size=batch_size, bench_kwargs=bench_kwargs)
if prefix:
if prefix and 'error' not in run_results:
run_results = {'_'.join([prefix, k]): v for k, v in run_results.items()}
model_results.update(run_results)
param_count = model_results.pop('infer_param_count', model_results.pop('train_param_count', 0))
model_results.setdefault('param_count', param_count)
model_results.pop('train_param_count', 0)
return model_results if model_results['param_count'] else dict()
if 'error' not in model_results:
param_count = model_results.pop('infer_param_count', model_results.pop('train_param_count', 0))
model_results.setdefault('param_count', param_count)
model_results.pop('train_param_count', 0)
return model_results
def main():
@ -582,13 +584,15 @@ def main():
sort_key = 'train_samples_per_sec'
elif 'profile' in args.bench:
sort_key = 'infer_gmacs'
results = filter(lambda x: sort_key in x, results)
results = sorted(results, key=lambda x: x[sort_key], reverse=True)
if len(results):
write_results(results_file, results)
else:
results = benchmark(args)
json_str = json.dumps(results, indent=4)
print(json_str)
# output results in JSON to stdout w/ delimiter for runner script
print(f'--result\n{json.dumps(results, indent=4)}')
def write_results(results_file, results):

@ -3,12 +3,12 @@
## EfficientNet-B2 with RandAugment - 80.4 top-1, 95.1 top-5
These params are for dual Titan RTX cards with NVIDIA Apex installed:
`./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .016`
`./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .016`
## MixNet-XL with RandAugment - 80.5 top-1, 94.9 top-5
This params are for dual Titan RTX cards with NVIDIA Apex installed:
`./distributed_train.sh 2 /imagenet/ --model mixnet_xl -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .969 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.3 --amp --lr .016 --dist-bn reduce`
`./distributed_train.sh 2 /imagenet/ --model mixnet_xl -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .969 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.3 --amp --lr .016 --dist-bn reduce`
## SE-ResNeXt-26-D and SE-ResNeXt-26-T
These hparams (or similar) work well for a wide range of ResNet architecture, generally a good idea to increase the epoch # as the model size increases... ie approx 180-200 for ResNe(X)t50, and 220+ for larger. Increase batch size and LR proportionally for better GPUs or with AMP enabled. These params were for 2 1080Ti cards:
@ -21,7 +21,7 @@ The training of this model started with the same command line as EfficientNet-B2
## EfficientNet-B0 with RandAugment - 77.7 top-1, 95.3 top-5
[Michael Klachko](https://github.com/michaelklachko) achieved these results with the command line for B2 adapted for larger batch size, with the recommended B0 dropout rate of 0.2.
`./distributed_train.sh 2 /imagenet/ --model efficientnet_b0 -b 384 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .048`
`./distributed_train.sh 2 /imagenet/ --model efficientnet_b0 -b 384 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .048`
## ResNet50 with JSD loss and RandAugment (clean + 2x RA augs) - 79.04 top-1, 94.39 top-5
@ -32,11 +32,11 @@ Trained on two older 1080Ti cards, this took a while. Only slightly, non statist
## EfficientNet-ES (EdgeTPU-Small) with RandAugment - 78.066 top-1, 93.926 top-5
Trained by [Andrew Lavin](https://github.com/andravin) with 8 V100 cards. Model EMA was not used, final checkpoint is the average of 8 best checkpoints during training.
`./distributed_train.sh 8 /imagenet --model efficientnet_es -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-connect 0.2 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064`
`./distributed_train.sh 8 /imagenet --model efficientnet_es -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064`
## MobileNetV3-Large-100 - 75.766 top-1, 92,542 top-5
`./distributed_train.sh 2 /imagenet/ --model mobilenetv3_large_100 -b 512 --sched step --epochs 600 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 --lr-noise 0.42 0.9`
`./distributed_train.sh 2 /imagenet/ --model mobilenetv3_large_100 -b 512 --sched step --epochs 600 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 --lr-noise 0.42 0.9`
## ResNeXt-50 32x4d w/ RandAugment - 79.762 top-1, 94.60 top-5

@ -0,0 +1,704 @@
model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,38522.1,6.633,256,106,0.03,0.69,2.04
mobilenetv3_small_050,32214.84,7.935,256,224,0.03,0.92,1.59
lcnet_035,31434.42,8.133,256,224,0.03,1.04,1.64
lcnet_050,27990.74,9.135,256,224,0.05,1.26,1.88
tf_mobilenetv3_small_minimal_100,24613.65,10.389,256,224,0.06,1.41,2.04
mobilenetv3_small_075,23970.62,10.668,256,224,0.05,1.3,2.04
tinynet_d,22226.54,11.506,256,152,0.05,1.42,2.34
mobilenetv3_small_100,20961.66,12.202,256,224,0.06,1.42,2.54
tf_mobilenetv3_small_075,20346.43,12.57,256,224,0.05,1.3,2.04
tf_mobilenetv3_small_100,18514.72,13.814,256,224,0.06,1.42,2.54
lcnet_075,18250.57,14.016,256,224,0.1,1.99,2.36
levit_128s,17514.74,14.605,256,224,0.31,1.88,7.78
mnasnet_small,16174.2,15.817,256,224,0.07,2.16,2.03
mobilenetv2_035,16065.18,15.924,256,224,0.07,2.86,1.68
regnetx_002,15056.58,16.991,256,224,0.2,2.16,2.68
ghostnet_050,14915.33,17.152,256,224,0.05,1.77,2.59
lcnet_100,14745.93,17.35,256,224,0.16,2.52,2.95
regnety_002,13600.49,18.812,256,224,0.2,2.17,3.16
mobilenetv2_050,12921.59,19.8,256,224,0.1,3.64,1.97
mnasnet_050,12170.95,21.022,256,224,0.11,3.07,2.22
levit_128,12112.09,21.125,256,224,0.41,2.71,9.21
semnasnet_050,11653.92,21.956,256,224,0.11,3.44,2.08
tinynet_c,11441.66,22.362,256,184,0.11,2.87,2.46
levit_192,10512.15,24.341,256,224,0.66,3.2,10.95
gernet_s,10252.23,24.96,256,224,0.75,2.65,8.17
mixer_s32_224,9991.39,25.611,256,224,1.0,2.28,19.1
mobilenetv3_large_075,9848.53,25.983,256,224,0.16,4.0,3.99
ese_vovnet19b_slim_dw,9567.57,26.746,256,224,0.4,5.28,1.9
lcnet_150,9561.66,26.763,256,224,0.34,3.79,4.5
vit_small_patch32_224,9406.05,27.206,256,224,1.15,2.5,22.88
regnetx_004,9291.08,27.542,256,224,0.4,3.14,5.16
tf_mobilenetv3_large_minimal_100,9242.84,27.684,256,224,0.22,4.4,3.92
tf_mobilenetv3_large_075,8812.22,29.037,256,224,0.16,4.0,3.99
mobilenetv3_rw,8698.97,29.417,256,224,0.23,4.41,5.48
vit_tiny_r_s16_p8_224,8636.97,29.629,256,224,0.44,2.06,6.34
mobilenetv3_large_100,8617.52,29.696,256,224,0.23,4.41,5.48
mobilenetv3_large_100_miil,8613.51,29.709,256,224,0.23,4.41,5.48
ssl_resnet18,8520.11,30.036,256,224,1.82,2.48,11.69
gluon_resnet18_v1b,8514.08,30.057,256,224,1.82,2.48,11.69
resnet18,8481.72,30.171,256,224,1.82,2.48,11.69
swsl_resnet18,8383.36,30.526,256,224,1.82,2.48,11.69
ghostnet_100,8302.41,30.823,256,224,0.15,3.55,5.18
mobilenetv2_075,8284.54,30.89,256,224,0.22,5.86,2.64
mnasnet_075,8193.74,31.233,256,224,0.23,4.77,3.17
seresnet18,7935.21,32.25,256,224,1.82,2.49,11.78
levit_256,7830.25,32.682,256,224,1.13,4.23,18.89
tf_mobilenetv3_large_100,7782.64,32.881,256,224,0.23,4.41,5.48
legacy_seresnet18,7750.12,33.02,256,224,1.82,2.49,11.78
regnetx_006,7679.18,33.326,256,224,0.61,3.98,6.2
semnasnet_075,7673.81,33.35,256,224,0.23,5.54,2.91
mobilenetv2_100,7580.19,33.761,256,224,0.31,6.68,3.5
regnety_004,7520.0,34.031,256,224,0.41,3.89,4.34
tinynet_b,7462.13,34.294,256,188,0.21,4.44,3.73
hardcorenas_a,7310.63,35.006,256,224,0.23,4.38,5.26
hardcorenas_b,7243.65,35.33,256,224,0.26,5.09,5.18
mnasnet_100,7211.43,35.488,256,224,0.33,5.46,4.38
mnasnet_b1,7197.57,35.556,256,224,0.33,5.46,4.38
resnet18d,7139.78,35.845,256,224,2.06,3.29,11.71
mnasnet_a1,6803.23,37.618,256,224,0.32,6.23,3.89
ghostnet_130,6799.96,37.636,256,224,0.24,4.6,7.36
semnasnet_100,6798.37,37.645,256,224,0.32,6.23,3.89
hardcorenas_c,6791.21,37.684,256,224,0.28,5.01,5.52
spnasnet_100,6742.99,37.955,256,224,0.35,6.03,4.42
regnety_006,6741.6,37.962,256,224,0.61,4.33,6.06
ese_vovnet19b_slim,6727.44,38.04,256,224,1.69,3.52,3.17
hardcorenas_d,6616.74,38.678,256,224,0.3,4.93,7.5
tf_efficientnetv2_b0,6404.53,39.959,256,224,0.73,4.77,7.14
regnetx_008,6126.62,41.774,256,224,0.81,5.15,7.26
efficientnet_lite0,5820.53,43.971,256,224,0.4,6.74,4.65
dla46_c,5815.5,44.006,256,224,0.58,4.5,1.3
resnetblur18,5731.2,44.657,256,224,2.34,3.39,11.69
mobilenetv2_110d,5728.31,44.679,256,224,0.45,8.71,4.52
tinynet_a,5715.04,44.781,256,192,0.35,5.41,6.19
rexnet_100,5705.81,44.855,256,224,0.41,7.44,4.8
rexnetr_100,5702.38,44.882,256,224,0.43,7.72,4.88
hardcorenas_f,5633.4,45.432,256,224,0.35,5.57,8.2
regnety_008,5562.41,46.011,256,224,0.81,5.25,6.26
hardcorenas_e,5407.35,47.331,256,224,0.35,5.65,8.07
fbnetc_100,5335.41,47.97,256,224,0.4,6.51,5.57
skresnet18,5314.32,48.161,256,224,1.82,3.24,11.96
tf_efficientnet_lite0,5277.93,48.492,256,224,0.4,6.74,4.65
efficientnet_b0,5118.94,50.0,256,224,0.4,6.75,5.29
mobilenetv2_140,5111.46,50.073,256,224,0.6,9.57,6.11
ese_vovnet19b_dw,5070.62,50.475,256,224,1.34,8.25,6.54
mnasnet_140,5006.18,51.125,256,224,0.6,7.71,7.12
efficientnet_b1_pruned,5001.01,51.178,256,240,0.4,6.21,6.33
resnet34,4971.16,51.486,256,224,3.67,3.74,21.8
gluon_resnet34_v1b,4968.4,51.515,256,224,3.67,3.74,21.8
tv_resnet34,4947.3,51.734,256,224,3.67,3.74,21.8
visformer_tiny,4922.23,51.997,256,224,1.27,5.72,10.32
hrnet_w18_small,4823.01,53.067,256,224,1.61,5.72,13.19
semnasnet_140,4792.63,53.404,256,224,0.6,8.87,6.11
levit_384,4732.88,54.078,256,224,2.36,6.26,39.13
tf_efficientnet_b0_ns,4689.2,54.583,256,224,0.4,6.75,5.29
tf_efficientnet_b0_ap,4677.78,54.715,256,224,0.4,6.75,5.29
tf_efficientnet_b0,4677.33,54.72,256,224,0.4,6.75,5.29
seresnet34,4638.93,55.174,256,224,3.67,3.74,21.96
selecsls42b,4636.17,55.206,256,224,2.98,4.62,32.46
dla46x_c,4631.52,55.26,256,224,0.54,5.66,1.07
selecsls42,4604.3,55.589,256,224,2.94,4.62,30.35
deit_tiny_patch16_224,4580.12,55.882,256,224,1.26,5.97,5.72
vit_tiny_patch16_224,4575.68,55.937,256,224,1.26,5.97,5.72
gernet_m,4537.32,56.41,256,224,3.02,5.24,21.14
deit_tiny_distilled_patch16_224,4513.87,56.702,256,224,1.27,6.01,5.91
nf_regnet_b0,4512.73,56.717,256,256,0.64,5.58,8.76
legacy_seresnet34,4498.05,56.902,256,224,3.67,3.74,21.96
resnet34d,4471.13,57.245,256,224,3.91,4.54,21.82
rexnetr_130,4425.1,57.84,256,224,0.68,9.81,7.61
pit_ti_distilled_224,4357.18,58.742,256,224,0.71,6.23,5.1
pit_ti_224,4345.5,58.9,256,224,0.7,6.19,4.85
mixnet_s,4322.59,59.212,256,224,0.25,6.25,4.13
dla60x_c,4316.36,59.296,256,224,0.59,6.01,1.32
tf_efficientnetv2_b1,4297.02,59.563,256,240,1.21,7.34,8.14
rexnet_130,4232.23,60.476,256,224,0.68,9.71,7.56
xcit_nano_12_p16_224_dist,4222.67,60.614,256,224,0.56,4.17,3.05
xcit_nano_12_p16_224,4207.85,60.827,256,224,0.56,4.17,3.05
resnet26,4180.52,61.224,256,224,2.36,7.35,16.0
resmlp_12_distilled_224,4174.74,61.31,256,224,3.01,5.5,15.35
resmlp_12_224,4169.39,61.389,256,224,3.01,5.5,15.35
mobilenetv2_120d,4149.08,61.689,256,224,0.69,11.97,5.83
vit_base_patch32_224_sam,4128.66,61.994,256,224,4.41,5.01,88.22
vit_base_patch32_224,4107.27,62.317,256,224,4.41,5.01,88.22
tf_mixnet_s,4070.89,62.873,256,224,0.25,6.25,4.13
repvgg_b0,4034.35,63.443,256,224,3.41,6.15,15.82
mixer_b32_224,3992.12,64.115,256,224,3.24,6.29,60.29
selecsls60,3976.19,64.371,256,224,3.59,5.52,30.67
selecsls60b,3972.94,64.425,256,224,3.63,5.52,32.77
rexnetr_150,3836.3,66.719,256,224,0.89,11.13,9.78
efficientnet_lite1,3836.28,66.719,256,240,0.62,10.14,5.42
resnet26d,3798.91,67.377,256,224,2.6,8.15,16.01
dla34,3769.64,67.899,256,224,3.07,5.02,15.74
rexnet_150,3681.94,69.516,256,224,0.9,11.21,9.73
ecaresnet50d_pruned,3648.06,70.162,256,224,2.53,6.43,19.94
nf_resnet26,3644.75,70.227,256,224,2.41,7.35,16.0
tf_efficientnet_lite1,3554.59,72.006,256,240,0.62,10.14,5.42
pit_xs_224,3526.31,72.586,256,224,1.4,7.71,10.62
regnetx_016,3513.85,72.844,256,224,1.62,7.93,9.19
pit_xs_distilled_224,3500.42,73.123,256,224,1.41,7.76,11.0
fbnetv3_d,3391.58,75.47,256,256,0.68,11.1,10.31
efficientnet_es_pruned,3370.79,75.934,256,224,1.81,8.73,5.44
fbnetv3_b,3370.57,75.939,256,256,0.55,9.1,8.6
efficientnet_es,3367.33,76.013,256,224,1.81,8.73,5.44
efficientnet_b2_pruned,3361.3,76.15,256,260,0.73,9.13,8.31
efficientnet_cc_b0_8e,3277.21,78.103,256,224,0.42,9.42,24.01
mixer_s16_224,3266.14,78.369,256,224,3.79,5.97,18.53
efficientnet_cc_b0_4e,3265.3,78.389,256,224,0.41,9.42,13.31
tf_efficientnet_es,3259.95,78.516,256,224,1.81,8.73,5.44
tf_efficientnetv2_b2,3255.37,78.626,256,260,1.72,9.84,10.1
resnest14d,3217.37,79.556,256,224,2.76,7.33,10.61
nf_seresnet26,3202.97,79.914,256,224,2.41,7.36,17.4
nf_ecaresnet26,3189.01,80.265,256,224,2.41,7.36,16.0
gernet_l,3181.93,80.443,256,256,4.57,8.0,31.08
regnety_016,3102.97,82.489,256,224,1.63,8.04,11.2
tf_efficientnet_cc_b0_8e,3093.44,82.743,256,224,0.42,9.42,24.01
tf_efficientnet_cc_b0_4e,3085.55,82.954,256,224,0.41,9.42,13.31
mixnet_m,3056.44,83.746,256,224,0.36,8.19,5.01
skresnet34,3030.56,84.459,256,224,3.67,5.13,22.28
resnext26ts,3023.16,84.668,256,256,2.43,10.52,10.3
repvgg_a2,3004.59,85.192,256,224,5.7,6.26,28.21
vit_tiny_r_s16_p8_384,3002.92,85.238,256,384,1.34,6.49,6.36
legacy_seresnext26_32x4d,3001.06,85.291,256,224,2.49,9.39,16.79
vit_small_patch32_384,2997.9,85.382,256,384,3.45,8.25,22.92
xcit_tiny_12_p16_224_dist,2978.68,85.929,256,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,2968.51,86.225,256,224,1.24,6.29,6.72
resnet26t,2964.54,86.343,256,256,3.35,10.52,16.01
gmixer_12_224,2952.25,86.702,256,224,2.67,7.26,12.7
seresnext26ts,2943.83,86.951,256,256,2.43,10.52,10.39
eca_resnext26ts,2943.35,86.964,256,256,2.43,10.52,10.3
efficientnet_lite2,2941.13,87.03,256,260,0.89,12.9,6.09
tf_efficientnet_b1_ap,2928.7,87.399,256,240,0.71,10.88,7.79
tf_efficientnet_b1,2924.32,87.53,256,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,2924.08,87.537,256,240,0.71,10.88,7.79
tf_mixnet_m,2922.35,87.587,256,224,0.36,8.19,5.01
gcresnext26ts,2878.37,88.928,256,256,2.43,10.53,10.48
efficientnet_b1,2842.35,90.055,256,256,0.77,12.22,7.79
ecaresnet101d_pruned,2827.84,90.516,256,224,3.48,7.69,24.88
seresnext26tn_32x4d,2809.03,91.123,256,224,2.7,10.09,16.81
ecaresnext50t_32x4d,2808.18,91.151,256,224,2.7,10.09,15.41
seresnext26t_32x4d,2807.76,91.164,256,224,2.7,10.09,16.81
ecaresnext26t_32x4d,2799.26,91.441,256,224,2.7,10.09,15.41
seresnext26d_32x4d,2790.46,91.729,256,224,2.73,10.19,16.81
ecaresnetlight,2759.56,92.757,256,224,4.11,8.42,30.16
rexnetr_200,2747.9,93.151,256,224,1.59,15.11,16.52
nf_regnet_b2,2743.38,93.303,256,272,1.22,9.27,14.31
tf_efficientnet_lite2,2742.42,93.334,256,260,0.89,12.9,6.09
nf_regnet_b1,2740.87,93.389,256,288,1.02,9.2,10.22
crossvit_tiny_240,2729.62,93.771,256,240,1.57,9.08,7.01
resnetv2_50,2688.33,95.214,256,224,4.11,11.11,25.55
rexnet_200,2686.43,95.282,256,224,1.56,14.91,16.37
crossvit_9_240,2684.97,95.332,256,240,1.85,9.52,8.55
eca_botnext26ts_256,2675.06,95.687,256,256,2.46,11.6,10.59
vgg11,2663.05,96.12,256,224,7.61,7.44,132.86
botnet26t_256,2660.83,96.197,256,256,3.32,11.98,12.49
tresnet_m,2657.08,96.334,256,224,5.74,7.31,31.39
eca_halonext26ts,2619.52,97.716,256,256,2.44,11.46,10.76
halonet26t,2612.17,97.992,256,256,3.19,11.69,12.48
crossvit_9_dagger_240,2602.86,98.339,256,240,1.99,9.97,8.78
efficientnet_b3_pruned,2602.84,98.342,256,300,1.04,11.86,9.86
resnet50,2602.03,98.373,256,224,4.11,11.11,25.56
tv_resnet50,2597.64,98.539,256,224,4.11,11.11,25.56
gluon_resnet50_v1b,2593.37,98.702,256,224,4.11,11.11,25.56
ssl_resnet50,2588.68,98.881,256,224,4.11,11.11,25.56
swsl_resnet50,2588.5,98.887,256,224,4.11,11.11,25.56
convit_tiny,2557.96,100.066,256,224,1.26,7.94,5.71
vovnet39a,2552.48,100.283,256,224,7.09,6.73,22.6
resnetv2_50t,2536.73,100.905,256,224,4.32,11.82,25.57
resnetv2_50d,2523.31,101.442,256,224,4.35,11.92,25.57
resnet32ts,2520.72,101.547,256,256,4.63,11.58,17.96
resnet33ts,2487.04,102.921,256,256,4.76,11.66,19.68
bat_resnext26ts,2486.58,102.938,256,256,2.53,12.51,10.73
vit_small_resnet26d_224,2484.66,103.019,256,224,5.07,11.12,63.61
ese_vovnet39b,2483.84,103.055,256,224,7.09,6.74,24.57
cspresnet50,2476.24,103.369,256,256,4.54,11.5,21.62
eca_vovnet39b,2474.83,103.43,256,224,7.09,6.74,22.6
hrnet_w18_small_v2,2473.49,103.485,256,224,2.62,9.65,15.6
gluon_resnet50_v1c,2461.53,103.989,256,224,4.35,11.92,25.58
cspresnext50,2459.33,104.08,256,224,3.1,12.14,20.57
resnet50t,2452.68,104.364,256,224,4.32,11.82,25.57
gluon_resnet50_v1d,2447.81,104.572,256,224,4.35,11.92,25.58
resnet50d,2444.17,104.728,256,224,4.35,11.92,25.58
dpn68b,2437.01,105.034,256,224,2.35,10.47,12.61
legacy_seresnet50,2425.37,105.54,256,224,3.88,10.6,28.09
gmlp_ti16_224,2422.83,105.65,256,224,1.34,7.55,5.87
seresnet33ts,2418.32,105.847,256,256,4.76,11.66,19.78
eca_resnet33ts,2414.21,106.028,256,256,4.76,11.66,19.68
dpn68,2412.98,106.079,256,224,2.35,10.47,12.61
selecsls84,2409.11,106.251,256,224,5.9,7.57,50.95
vgg11_bn,2392.35,106.996,256,224,7.62,7.44,132.87
mixnet_l,2360.8,108.426,256,224,0.58,10.84,7.33
gcresnet33ts,2355.59,108.666,256,256,4.76,11.68,19.88
lambda_resnet26t,2353.44,108.765,256,256,3.02,11.87,10.96
pit_s_224,2338.74,109.449,256,224,2.88,11.56,23.46
dla60,2331.19,109.802,256,224,4.26,10.16,22.04
cspresnet50w,2330.53,109.833,256,256,5.04,12.19,28.12
seresnet50,2324.39,110.125,256,224,4.11,11.13,28.09
resnest26d,2321.38,110.267,256,224,3.64,9.97,17.07
pit_s_distilled_224,2320.78,110.295,256,224,2.9,11.64,24.04
cspresnet50d,2313.66,110.634,256,256,4.86,12.55,21.64
deit_small_patch16_224,2312.17,110.706,256,224,4.61,11.95,22.05
vit_small_patch16_224,2301.45,111.22,256,224,4.61,11.95,22.05
deit_small_distilled_patch16_224,2273.38,112.595,256,224,4.63,12.02,22.44
tf_efficientnet_b2_ap,2266.39,112.943,256,260,1.02,13.83,9.11
tf_efficientnet_b2,2265.35,112.996,256,260,1.02,13.83,9.11
tf_efficientnet_b2_ns,2264.55,113.035,256,260,1.02,13.83,9.11
tf_mixnet_l,2263.46,113.087,256,224,0.58,10.84,7.33
densenet121,2251.23,113.702,256,224,2.87,6.9,7.98
res2net50_48w_2s,2244.59,114.041,256,224,4.18,11.72,25.29
tv_densenet121,2243.68,114.087,256,224,2.87,6.9,7.98
resnetaa50d,2224.41,115.074,256,224,5.39,12.44,25.58
seresnet50t,2204.01,116.141,256,224,4.32,11.83,28.1
resnetblur50,2197.45,116.486,256,224,5.16,12.02,25.56
haloregnetz_b,2191.27,116.814,256,224,1.97,11.94,11.68
ecaresnet50d,2187.04,117.042,256,224,4.35,11.93,25.58
resnetrs50,2155.43,118.757,256,224,4.48,12.14,35.69
densenet121d,2152.18,118.936,256,224,3.11,7.7,8.0
gluon_resnet50_v1s,2129.0,120.233,256,224,5.47,13.52,25.68
visformer_small,2128.36,120.269,256,224,4.88,11.43,40.22
resmlp_24_224,2126.97,120.348,256,224,5.96,10.91,30.02
efficientnet_b2a,2125.57,120.427,256,288,1.12,16.2,9.11
resmlp_24_distilled_224,2124.32,120.497,256,224,5.96,10.91,30.02
efficientnet_b2,2121.96,120.632,256,288,1.12,16.2,9.11
regnetx_032,2121.9,120.635,256,224,3.2,11.37,15.3
adv_inception_v3,2116.52,120.939,256,299,5.73,8.97,23.83
gluon_inception_v3,2110.53,121.285,256,299,5.73,8.97,23.83
inception_v3,2106.47,121.518,256,299,5.73,8.97,23.83
tf_inception_v3,2102.15,121.765,256,299,5.73,8.97,23.83
vovnet57a,2101.83,121.786,256,224,8.95,7.52,36.64
resnetblur50d,2086.08,122.707,256,224,5.4,12.82,25.58
efficientnet_em,2080.04,123.062,256,240,3.04,14.34,6.9
efficientnet_cc_b1_8e,2060.35,124.238,256,240,0.75,15.44,39.72
cspresnext50_iabn,2046.67,125.068,256,256,4.02,15.86,20.57
densenetblur121d,2045.46,125.142,256,224,3.11,7.9,8.0
tf_efficientnet_em,2031.54,125.998,256,240,3.04,14.34,6.9
vit_base_resnet26d_224,2020.03,126.719,256,224,6.97,13.16,101.4
ese_vovnet57b,2017.2,126.896,256,224,8.95,7.52,38.61
ssl_resnext50_32x4d,2016.48,126.942,256,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2014.9,127.042,256,224,4.26,14.4,25.03
seresnetaa50d,2014.43,127.071,256,224,5.4,12.46,28.11
gluon_resnext50_32x4d,2013.28,127.144,256,224,4.26,14.4,25.03
tv_resnext50_32x4d,2010.77,127.303,256,224,4.26,14.4,25.03
resnext50_32x4d,2010.4,127.325,256,224,4.26,14.4,25.03
tf_efficientnet_cc_b1_8e,1970.45,129.905,256,240,0.75,15.44,39.72
cspdarknet53_iabn,1958.85,130.675,256,256,6.53,16.81,27.64
dla60x,1947.14,131.46,256,224,3.54,13.8,17.35
regnetx_040,1940.97,131.881,256,224,3.99,12.2,22.12
skresnet50,1940.94,131.882,256,224,4.11,12.5,25.8
nf_seresnet50,1937.18,132.139,256,224,4.21,11.13,28.09
nf_ecaresnet50,1931.32,132.54,256,224,4.21,11.13,25.56
res2net50_26w_4s,1927.67,132.79,256,224,4.28,12.61,25.7
tf_efficientnetv2_b3,1921.68,133.203,256,300,3.04,15.74,14.36
resnext50d_32x4d,1917.13,133.521,256,224,4.5,15.2,25.05
regnety_040,1911.7,133.9,256,224,4.0,12.29,20.65
gcresnet50t,1897.82,134.88,256,256,5.42,14.67,25.9
efficientnetv2_rw_t,1893.97,135.153,256,288,3.19,16.42,13.65
sehalonet33ts,1881.49,136.051,256,256,3.55,14.7,13.69
lambda_resnet26rpt_256,1854.82,138.007,256,256,3.16,11.87,10.99
skresnet50d,1849.16,138.429,256,224,4.36,13.31,25.82
gcresnext50ts,1842.39,138.938,256,256,3.75,15.46,15.67
seresnext50_32x4d,1842.11,138.958,256,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,1836.43,139.388,256,224,4.26,14.42,27.56
dla60_res2net,1834.89,139.503,256,224,4.15,12.34,20.85
legacy_seresnext50_32x4d,1833.54,139.608,256,224,4.26,14.42,27.56
resnest50d_1s4x24d,1830.82,139.816,256,224,4.43,13.57,25.68
repvgg_b1g4,1821.65,140.52,256,224,8.15,10.64,39.97
darknet53,1820.66,140.594,256,256,9.31,12.39,41.61
densenet169,1814.76,141.051,256,224,3.4,7.3,14.15
gc_efficientnetv2_rw_t,1812.58,141.223,256,288,3.2,16.45,13.68
res2net50_14w_8s,1809.09,141.495,256,224,4.21,13.28,25.06
coat_lite_tiny,1803.47,141.935,256,224,1.6,11.65,5.72
res2next50,1798.23,142.351,256,224,4.2,13.71,24.67
cspdarknet53,1759.27,145.502,256,256,6.57,16.81,27.64
ecaresnet26t,1753.56,145.977,256,320,5.24,16.44,16.01
dla60_res2next,1751.16,146.174,256,224,3.49,13.17,17.03
efficientnet_lite3,1747.68,146.468,256,300,1.65,21.85,8.2
nf_regnet_b3,1737.85,147.296,256,320,2.05,14.61,18.59
vgg13,1734.82,147.554,256,224,11.31,12.25,133.05
regnetz_b16,1724.03,148.477,256,288,2.39,16.43,9.72
coat_lite_mini,1717.28,149.059,256,224,2.0,12.25,11.01
mixnet_xl,1685.34,151.886,256,224,0.93,14.57,11.9
vit_small_r26_s32_224,1673.77,152.936,256,224,3.56,9.85,36.43
sebotnet33ts_256,1634.05,156.655,256,256,3.89,17.46,13.7
resnetv2_101,1630.31,157.012,256,224,7.83,16.23,44.54
tf_efficientnet_lite3,1628.65,157.171,256,300,1.65,21.85,8.2
convnext_tiny,1599.8,160.006,256,224,4.47,13.44,28.59
tv_resnet101,1599.73,160.015,256,224,7.83,16.23,44.55
gluon_resnet101_v1b,1599.19,160.07,256,224,7.83,16.23,44.55
convnext_tiny_hnf,1598.61,160.125,256,224,4.47,13.44,28.59
resnet101,1594.14,160.576,256,224,7.83,16.23,44.55
xcit_tiny_24_p16_224_dist,1587.39,161.257,256,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,1582.27,161.779,256,224,2.34,11.82,12.12
repvgg_b1,1574.77,162.553,256,224,13.16,10.64,57.42
resnetv2_101d,1569.58,163.088,256,224,8.07,17.04,44.56
xcit_small_12_p16_224_dist,1568.96,163.153,256,224,4.82,12.58,26.25
xcit_small_12_p16_224,1566.49,163.411,256,224,4.82,12.58,26.25
vit_base_resnet50d_224,1557.57,164.346,256,224,8.73,16.92,110.97
resnest50d,1550.54,165.09,256,224,5.4,14.36,27.48
gluon_resnet101_v1c,1550.28,165.12,256,224,8.08,17.04,44.57
lambda_resnet50ts,1550.05,165.145,256,256,5.07,17.48,21.54
vgg13_bn,1545.09,165.675,256,224,11.33,12.25,133.05
gluon_resnet101_v1d,1542.32,165.972,256,224,8.08,17.04,44.57
dla102,1542.08,165.995,256,224,7.19,14.18,33.27
twins_svt_small,1526.51,167.691,256,224,2.94,13.75,24.06
wide_resnet50_2,1515.12,168.952,256,224,11.43,14.4,68.88
gmixer_24_224,1503.65,170.24,256,224,5.28,14.45,24.72
resnetv2_50x1_bit_distilled,1496.53,171.051,256,224,4.23,11.11,25.55
regnetx_080,1491.93,171.578,256,224,8.02,14.06,39.57
xcit_nano_12_p16_384_dist,1489.5,171.858,256,384,1.64,12.15,3.05
crossvit_small_240,1472.6,173.826,256,240,5.63,18.17,26.86
res2net50_26w_6s,1468.77,174.282,256,224,6.33,15.28,37.05
legacy_seresnet101,1466.72,174.527,256,224,7.61,15.74,49.33
halonet50ts,1459.14,175.434,256,256,5.3,19.2,22.73
resnetaa101d,1449.52,176.598,256,224,9.12,17.56,44.57
fbnetv3_g,1435.53,178.32,256,288,1.77,21.09,16.62
seresnet101,1425.31,179.597,256,224,7.84,16.27,49.33
resmlp_36_224,1423.79,179.791,256,224,8.91,16.33,44.69
resmlp_36_distilled_224,1422.98,179.892,256,224,8.91,16.33,44.69
regnetx_064,1422.54,179.948,256,224,6.49,16.37,26.21
resnetv2_50d_gn,1422.29,179.98,256,224,4.38,11.92,25.57
densenet201,1416.58,180.704,256,224,4.34,7.85,20.01
vit_large_patch32_224,1414.43,180.979,256,224,15.39,13.3,306.54
gluon_resnet101_v1s,1407.4,181.883,256,224,9.19,18.64,44.67
vgg16,1399.33,182.931,256,224,15.47,13.56,138.36
lamhalobotnet50ts_256,1392.5,183.829,256,256,5.02,18.44,22.57
resnetblur101d,1390.53,184.088,256,224,9.12,17.94,44.57
nf_resnet50,1387.13,184.542,256,288,6.88,18.37,25.56
nf_resnet101,1387.03,184.555,256,224,8.01,16.23,44.55
ecaresnet101d,1375.85,186.054,256,224,8.08,17.07,44.57
vit_base_r26_s32_224,1374.54,186.232,256,224,6.81,12.36,101.38
regnety_032,1353.73,189.095,256,288,5.29,18.61,19.44
crossvit_15_240,1351.08,189.465,256,240,5.81,19.77,27.53
tresnet_l,1350.23,189.586,256,224,10.88,11.9,55.99
gmlp_s16_224,1343.26,190.569,256,224,4.42,15.1,19.42
tf_efficientnet_b3,1341.98,190.75,256,300,1.87,23.83,12.23
tf_efficientnet_b3_ns,1340.42,190.973,256,300,1.87,23.83,12.23
tf_efficientnet_b3_ap,1339.36,191.124,256,300,1.87,23.83,12.23
resnet51q,1338.83,191.2,256,288,8.07,20.94,35.7
hrnet_w18,1335.83,191.629,256,224,4.32,16.31,21.3
vit_base_patch32_384,1328.33,192.713,256,384,13.06,16.5,88.3
resnet50_gn,1327.88,192.777,256,224,4.14,11.11,25.56
mixer_l32_224,1318.62,194.131,256,224,11.27,19.86,206.94
xception,1316.52,194.441,256,299,8.4,35.83,22.86
crossvit_15_dagger_240,1308.42,195.641,256,240,6.13,20.43,28.21
dla102x,1302.91,196.468,256,224,5.89,19.42,26.31
efficientnet_b3a,1302.53,196.528,256,320,2.01,26.52,12.23
efficientnet_b3,1301.42,196.696,256,320,2.01,26.52,12.23
botnet50ts_256,1288.18,198.716,256,256,5.54,22.23,22.74
cait_xxs24_224,1287.06,198.889,256,224,2.53,20.29,11.96
mixer_b16_224_miil,1283.42,199.455,256,224,12.62,14.53,59.88
mixer_b16_224,1283.22,199.487,256,224,12.62,14.53,59.88
skresnext50_32x4d,1276.26,200.574,256,224,4.5,17.18,27.48
regnety_064,1272.95,201.095,256,224,6.39,16.41,30.58
swsl_resnext101_32x4d,1266.33,202.147,256,224,8.01,21.23,44.18
ssl_resnext101_32x4d,1265.67,202.252,256,224,8.01,21.23,44.18
gluon_resnext101_32x4d,1265.25,202.319,256,224,8.01,21.23,44.18
resnext101_32x4d,1264.74,202.401,256,224,8.01,21.23,44.18
vgg16_bn,1261.15,202.977,256,224,15.5,13.56,138.37
repvgg_b2g4,1251.52,204.539,256,224,12.63,12.9,61.76
halo2botnet50ts_256,1248.54,205.028,256,256,5.02,21.78,22.64
swin_tiny_patch4_window7_224,1248.01,205.115,256,224,4.51,17.06,28.29
twins_pcpvt_small,1232.89,207.63,256,224,3.83,18.08,24.11
regnety_080,1232.57,207.684,256,224,8.0,17.97,39.18
resnest50d_4s2x40d,1216.47,210.433,256,224,4.4,17.94,30.42
resnet61q,1209.86,211.583,256,288,9.87,21.52,36.85
nf_seresnet101,1194.71,214.265,256,224,8.02,16.27,49.33
ese_vovnet99b_iabn,1193.74,214.438,256,224,16.49,11.27,63.2
nf_ecaresnet101,1193.13,214.55,256,224,8.01,16.27,44.55
res2net50_26w_8s,1192.34,214.692,256,224,8.37,17.95,48.4
eca_nfnet_l0,1189.26,215.246,256,288,7.12,17.29,24.14
res2net101_26w_4s,1189.12,215.272,256,224,8.1,18.45,45.21
nfnet_l0,1185.85,215.867,256,288,7.13,17.29,35.07
dpn92,1179.85,216.965,256,224,6.54,18.21,37.67
vit_tiny_patch16_384,1177.29,217.437,256,384,4.7,25.39,5.79
convit_small,1173.71,218.096,256,224,5.76,17.87,27.78
ese_vovnet99b,1158.79,220.908,256,224,16.51,11.27,63.2
vgg19,1152.83,222.048,256,224,19.63,14.86,143.67
seresnext101_32x4d,1152.16,222.18,256,224,8.02,21.26,48.96
resnetv2_50d_evob,1150.91,222.42,256,224,4.33,11.92,25.59
gluon_seresnext101_32x4d,1150.75,222.451,256,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1150.68,222.464,256,224,8.02,21.26,48.96
ese_vovnet39b_evos,1144.38,223.689,256,224,7.07,6.74,24.58
hrnet_w32,1140.5,224.45,256,224,8.97,22.02,41.23
resnetv2_152,1132.73,225.989,256,224,11.55,22.56,60.19
xcit_nano_12_p8_224_dist,1129.67,226.602,256,224,2.16,15.71,3.05
xcit_nano_12_p8_224,1124.18,227.71,256,224,2.16,15.71,3.05
hrnet_w30,1121.05,228.345,256,224,8.15,21.21,37.71
tv_resnet152,1117.27,229.117,256,224,11.56,22.56,60.19
resnet152,1114.93,229.598,256,224,11.56,22.56,60.19
gluon_resnet152_v1b,1112.41,230.118,256,224,11.56,22.56,60.19
ecaresnet50t,1109.44,230.735,256,320,8.82,24.13,25.57
regnetz_c16,1104.72,231.72,256,320,3.92,25.88,13.46
resnetv2_152d,1101.83,232.328,256,224,11.8,23.36,60.2
gluon_resnet152_v1c,1092.12,234.395,256,224,11.8,23.36,60.21
repvgg_b2,1089.82,234.89,256,224,20.45,12.9,89.02
gluon_resnet152_v1d,1082.98,236.373,256,224,11.8,23.36,60.21
vgg19_bn,1079.7,237.091,256,224,19.66,14.86,143.68
densenet161,1075.45,238.026,256,224,7.79,11.06,28.68
xception41,1067.64,239.769,256,299,9.28,39.86,26.97
mixnet_xxl,1054.35,242.792,256,224,2.04,23.43,23.96
inception_v4,1054.27,242.81,256,299,12.28,15.09,42.68
vit_small_resnet50d_s16_224,1053.44,243.001,256,224,13.48,24.82,57.53
convmixer_1024_20_ks9_p14,1045.16,244.926,256,224,5.55,5.51,24.38
dla169,1043.58,245.295,256,224,11.6,20.2,53.39
nfnet_f0s,1043.56,245.302,256,256,12.62,18.05,71.49
xcit_tiny_12_p16_384_dist,1043.38,245.342,256,384,3.64,18.26,6.72
convnext_small,1038.48,246.501,256,224,8.7,21.56,50.22
nfnet_f0,1038.01,246.613,256,256,12.62,18.05,71.49
regnetx_120,1028.4,248.918,256,224,12.13,21.37,46.11
nest_tiny,1021.17,250.679,256,224,5.83,25.48,17.06
gluon_resnet152_v1s,1020.83,250.763,256,224,12.92,24.96,60.32
coat_lite_small,1016.77,251.763,256,224,3.96,22.09,19.84
legacy_seresnet152,1013.05,252.688,256,224,11.33,22.08,66.82
repvgg_b3g4,1008.62,253.799,256,224,17.89,15.1,83.83
jx_nest_tiny,1008.04,253.946,256,224,5.83,25.48,17.06
vit_base_patch16_224_miil,1007.66,254.043,256,224,17.58,23.9,86.54
crossvit_18_240,995.59,257.12,256,240,9.05,26.26,43.27
seresnet152,990.73,258.382,256,224,11.57,22.61,66.82
resnetv2_50d_evos,987.09,259.336,256,224,4.33,11.92,25.59
vit_base_patch16_224_sam,982.02,260.673,256,224,17.58,23.9,86.57
tresnet_xl,981.28,260.87,256,224,15.17,15.34,78.44
regnety_120,981.1,260.919,256,224,12.14,21.38,51.82
deit_base_patch16_224,980.91,260.97,256,224,17.58,23.9,86.57
vit_base_patch16_224,978.17,261.701,256,224,17.58,23.9,86.57
deit_base_distilled_patch16_224,975.08,262.528,256,224,17.68,24.05,87.34
crossvit_18_dagger_240,969.31,264.09,256,240,9.5,27.03,44.27
efficientnet_el,926.41,276.325,256,300,8.0,30.7,10.59
efficientnet_el_pruned,925.7,276.536,256,300,8.0,30.7,10.59
tf_efficientnet_el,907.79,281.986,256,300,8.0,30.7,10.59
dm_nfnet_f0,904.97,282.867,256,256,12.62,18.05,71.49
beit_base_patch16_224,904.15,283.123,256,224,17.58,23.9,86.53
twins_pcpvt_base,902.77,283.561,256,224,6.68,25.25,43.83
dla102x2,892.56,286.801,256,224,9.34,29.91,41.28
efficientnetv2_s,884.22,289.51,256,384,8.44,35.77,21.46
twins_svt_base,881.96,290.252,256,224,8.59,26.33,56.07
wide_resnet101_2,875.33,292.449,256,224,22.8,21.23,126.89
tf_efficientnetv2_s_in21ft1k,864.32,296.172,256,384,8.44,35.77,21.46
cait_xxs36_224,863.8,296.351,256,224,3.77,30.34,17.3
tf_efficientnetv2_s,863.76,296.365,256,384,8.44,35.77,21.46
resnetrs101,852.6,300.247,256,288,13.56,28.53,63.62
repvgg_b3,844.81,303.013,256,224,29.16,15.1,123.09
efficientnetv2_rw_s,841.56,304.183,256,384,8.72,38.03,23.94
dpn98,838.93,305.139,256,224,11.73,25.2,61.57
pit_b_distilled_224,837.59,305.628,256,224,12.5,33.07,74.79
pit_b_224,836.04,306.193,256,224,12.42,32.94,73.76
regnetx_160,833.81,307.013,256,224,15.99,25.52,54.28
xcit_small_24_p16_224_dist,833.22,307.225,256,224,9.1,23.64,47.67
xcit_small_24_p16_224,831.56,307.841,256,224,9.1,23.64,47.67
inception_resnet_v2,830.74,308.147,256,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,830.17,308.358,256,299,13.18,25.06,55.84
regnetz_d8,827.41,309.386,256,320,6.19,37.08,23.37
nf_regnet_b4,822.49,311.237,256,384,4.7,28.61,30.21
swin_small_patch4_window7_224,808.44,316.649,256,224,8.77,27.47,49.61
efficientnet_lite4,804.5,318.199,256,380,4.04,45.66,13.01
gluon_resnext101_64x4d,802.95,318.813,256,224,15.52,31.21,83.46
resnext101_64x4d,801.6,319.349,256,224,15.52,31.21,83.46
resnet200,795.01,321.996,256,224,15.07,32.19,64.67
xcit_tiny_12_p8_224_dist,792.9,322.851,256,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,791.66,323.355,256,224,4.81,23.6,6.71
gluon_xception65,782.06,327.33,256,299,13.96,52.48,39.92
convnext_base_in22ft1k,778.5,328.824,256,224,15.38,28.75,88.59
convnext_base,777.8,329.119,256,224,15.38,28.75,88.59
xception65,776.49,329.678,256,299,13.96,52.48,39.92
ssl_resnext101_32x8d,773.95,330.757,256,224,16.48,31.21,88.79
swsl_resnext101_32x8d,773.24,331.062,256,224,16.48,31.21,88.79
resnext101_32x8d,772.71,331.288,256,224,16.48,31.21,88.79
ig_resnext101_32x8d,772.07,331.562,256,224,16.48,31.21,88.79
resnet101d,769.32,332.747,256,320,16.48,34.77,44.57
hrnet_w40,767.4,333.582,256,224,12.75,25.29,57.56
tf_efficientnet_lite4,762.16,335.871,256,380,4.04,45.66,13.01
resnest101e,757.06,338.138,256,256,13.38,28.66,48.28
gluon_seresnext101_64x4d,754.82,339.139,256,224,15.53,31.25,88.23
seresnext101_32x8d,729.3,351.006,256,224,16.48,31.25,93.57
cait_s24_224,717.85,356.607,256,224,9.35,40.58,46.92
hrnet_w48,714.5,358.278,256,224,17.34,28.56,77.47
hrnet_w44,713.57,358.746,256,224,14.94,26.92,67.06
tresnet_m_448,711.59,359.746,256,448,22.94,29.21,31.39
regnetz_d32,706.63,362.269,256,320,9.33,37.08,27.58
coat_tiny,706.1,362.539,256,224,4.35,27.2,5.5
nest_small,681.35,375.712,256,224,10.35,40.04,38.35
vit_large_r50_s32_224,679.9,376.512,256,224,19.58,24.41,328.99
jx_nest_small,675.59,378.914,256,224,10.35,40.04,38.35
twins_svt_large,672.39,380.718,256,224,15.15,35.1,99.27
crossvit_base_240,669.47,382.378,256,240,21.22,36.33,105.03
efficientnet_b4,667.37,383.583,256,384,4.51,50.04,19.34
twins_pcpvt_large,655.12,390.752,256,224,9.84,35.82,60.99
gmlp_b16_224,643.01,398.116,256,224,15.78,30.21,73.08
densenet264d_iabn,638.28,401.062,256,224,13.47,14.0,72.74
tf_efficientnet_b4,637.3,401.68,256,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,636.89,401.944,256,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,636.28,402.323,256,380,4.49,49.49,19.34
convit_base,621.54,411.866,256,224,17.52,31.77,86.54
densenet264,620.68,412.433,256,224,12.95,12.8,72.69
dpn131,617.53,414.539,256,224,16.09,32.97,79.25
swin_base_patch4_window7_224,616.27,415.39,256,224,15.47,36.63,87.77
xcit_medium_24_p16_224_dist,612.37,418.035,256,224,16.13,31.71,84.4
xcit_medium_24_p16_224,612.25,418.117,256,224,16.13,31.71,84.4
vit_small_patch16_384,593.83,431.086,256,384,15.52,50.78,22.2
coat_mini,591.16,433.03,256,224,6.82,33.68,10.34
xception71,588.93,434.671,256,299,18.09,69.92,42.34
vit_small_r26_s32_384,574.79,445.365,256,384,10.43,29.85,36.47
hrnet_w64,567.27,451.268,256,224,28.97,35.09,128.06
dpn107,564.61,453.398,256,224,18.38,33.46,86.92
eca_nfnet_l1,558.87,458.051,256,320,14.92,34.42,41.41
senet154,557.21,459.42,256,224,20.77,38.69,115.09
vit_base_r50_s16_224,556.92,459.655,256,224,21.66,35.29,98.66
gluon_senet154,556.63,459.899,256,224,20.77,38.69,115.09
legacy_senet154,554.67,461.525,256,224,20.77,38.69,115.09
xcit_tiny_24_p16_384_dist,554.63,461.554,256,384,6.87,34.29,12.12
xcit_small_12_p16_384_dist,546.42,468.488,256,384,14.14,36.51,26.25
resnet152d,540.83,473.331,256,320,24.08,47.67,60.21
seresnet200d,537.6,476.175,256,256,20.01,43.15,71.86
ecaresnet200d,537.5,476.265,256,256,20.0,43.15,64.69
regnety_320,520.96,491.382,256,224,32.34,30.26,145.05
nest_base,514.48,497.58,256,224,17.96,53.39,67.72
regnety_160,513.33,498.688,256,288,26.37,38.07,83.59
jx_nest_base,510.61,501.344,256,224,17.96,53.39,67.72
tnt_s_patch16_224,491.14,521.218,256,224,5.24,24.37,23.76
seresnet152d,484.32,528.565,256,320,24.09,47.72,66.84
resnetrs152,479.63,533.736,256,320,24.34,48.14,86.62
convnext_large,469.79,544.914,256,224,34.4,43.13,197.77
convnext_large_in22ft1k,468.89,545.96,256,224,34.4,43.13,197.77
regnetx_320,458.91,557.836,256,224,31.81,36.3,107.81
halonet_h1,456.22,561.121,256,256,3.0,51.17,8.1
vit_large_patch32_384,455.24,562.325,256,384,45.31,43.86,306.63
efficientnetv2_m,448.05,571.353,256,416,18.6,67.5,54.14
regnetz_e8,441.39,579.972,256,320,15.46,63.94,57.7
mixer_l16_224,428.45,597.494,256,224,44.6,41.69,208.2
seresnet269d,419.5,610.23,256,256,26.59,53.6,113.67
nf_regnet_b5,418.97,611.004,256,456,11.7,61.95,49.74
xcit_tiny_24_p8_224,417.22,613.573,256,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,416.09,615.239,256,224,9.21,45.39,12.11
xcit_small_12_p8_224,415.7,615.812,256,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,414.47,617.648,256,224,18.69,47.21,26.21
efficientnetv2_rw_m,401.01,638.372,256,416,21.49,79.62,53.24
resnet200d,390.11,656.216,256,320,31.25,67.33,64.69
resnetv2_50x1_bitm,388.79,658.439,256,448,16.62,44.46,25.55
tnt_b_patch16_224,387.0,661.483,256,224,14.09,39.01,65.41
xcit_nano_12_p8_384_dist,385.68,663.749,256,384,6.34,46.08,3.05
swin_large_patch4_window7_224,383.12,668.188,256,224,34.53,54.94,196.53
nfnet_f1s,376.13,680.6,256,320,35.97,46.77,132.63
nfnet_f1,374.75,683.103,256,320,35.97,46.77,132.63
xcit_large_24_p16_224,373.32,685.729,256,224,35.86,47.27,189.1
xcit_large_24_p16_224_dist,372.9,686.493,256,224,35.86,47.27,189.1
ssl_resnext101_32x16d,364.85,701.653,256,224,36.27,51.18,194.03
ig_resnext101_32x16d,364.28,702.733,256,224,36.27,51.18,194.03
swsl_resnext101_32x16d,364.28,702.739,256,224,36.27,51.18,194.03
tresnet_l_448,351.48,728.329,256,448,43.5,47.56,55.99
resnetrs200,347.71,736.231,256,320,31.51,67.81,93.21
tf_efficientnetv2_m,339.44,754.17,256,480,24.76,89.84,54.14
tf_efficientnetv2_m_in21ft1k,338.69,755.846,256,480,24.76,89.84,54.14
regnetz_d8_evob,337.57,758.355,256,320,6.12,37.08,23.41
efficientnet_b5,330.43,774.739,256,456,10.46,98.86,30.39
vit_large_patch16_224,328.99,778.119,256,224,61.6,63.52,304.33
dm_nfnet_f1,328.44,779.437,256,320,35.97,46.77,132.63
convnext_xlarge_in22ft1k,319.16,802.079,256,224,60.97,57.5,350.2
tf_efficientnet_b5,318.33,804.192,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,317.92,805.231,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ns,317.77,805.609,256,456,10.46,98.86,30.39
crossvit_15_dagger_408,307.3,833.049,256,408,21.45,95.05,28.5
beit_large_patch16_224,302.42,846.497,256,224,61.6,63.52,304.43
xcit_small_24_p16_384_dist,290.22,882.071,256,384,26.72,68.58,47.67
convmixer_768_32,283.01,904.552,256,224,19.55,25.95,21.11
eca_nfnet_l2,276.43,926.087,256,384,30.05,68.28,56.72
regnetz_d8_evos,272.53,939.333,256,320,7.03,38.92,23.46
resnetv2_152x2_bit_teacher,270.79,945.382,256,224,46.95,45.11,236.34
xcit_tiny_12_p8_384_dist,269.38,950.319,256,384,14.13,69.14,6.71
convnext_base_384_in22ft1k,268.35,953.955,256,384,45.2,84.49,88.59
tresnet_xl_448,264.06,969.471,256,448,60.65,61.31,78.44
deit_base_patch16_384,258.82,989.071,256,384,55.54,101.56,86.86
vit_base_patch16_384,258.72,989.47,256,384,55.54,101.56,86.86
resnest200e,257.74,993.222,256,320,35.69,82.78,70.2
deit_base_distilled_patch16_384,255.57,1001.681,256,384,55.65,101.82,87.63
resnetv2_101x1_bitm,242.52,1055.587,256,448,31.65,64.93,44.54
cait_xxs24_384,236.82,1080.978,256,384,9.63,122.66,12.03
crossvit_18_dagger_408,228.68,559.729,128,408,32.47,124.87,44.61
ecaresnet269d,228.16,1122.012,256,352,50.25,101.25,102.09
vit_large_r50_s32_384,228.16,1121.984,256,384,57.43,76.52,329.09
nasnetalarge,224.77,1138.915,256,331,23.89,90.56,88.75
pnasnet5large,224.27,1141.456,256,331,25.04,92.89,86.06
beit_base_patch16_384,222.85,1148.718,256,384,55.54,101.56,86.74
resnetrs270,221.37,1156.398,256,352,51.13,105.48,129.86
xcit_small_24_p8_224,217.0,1179.688,256,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,216.72,1181.234,256,224,35.81,90.78,47.63
nfnet_f2s,212.63,1203.934,256,352,63.22,79.06,193.78
nfnet_f2,211.38,1211.098,256,352,63.22,79.06,193.78
xcit_medium_24_p16_384_dist,210.86,1214.056,256,384,47.39,91.64,84.4
resmlp_big_24_224,198.74,1288.096,256,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,197.93,1293.386,256,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,197.54,1295.901,256,224,100.23,87.31,129.14
efficientnetv2_l,196.77,1301.027,256,480,56.4,157.99,118.52
tf_efficientnetv2_l,194.85,1313.812,256,480,56.4,157.99,118.52
tf_efficientnetv2_l_in21ft1k,194.51,1316.142,256,480,56.4,157.99,118.52
efficientnet_b6,189.94,673.894,128,528,19.4,167.39,43.04
dm_nfnet_f2,186.3,1374.105,256,352,63.22,79.06,193.78
tf_efficientnet_b6_ns,183.84,696.254,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ap,183.67,696.899,128,528,19.4,167.39,43.04
tf_efficientnet_b6,183.44,697.757,128,528,19.4,167.39,43.04
swin_base_patch4_window12_384,171.35,746.981,128,384,47.19,134.78,87.9
vit_base_patch8_224,170.3,1503.253,256,224,78.22,161.69,86.58
cait_xs24_384,168.32,1520.881,256,384,19.28,183.98,26.67
vit_base_r50_s16_384,163.47,1566.011,256,384,67.43,135.03,98.95
vit_base_resnet50_384,163.26,1568.003,256,384,67.43,135.03,98.95
convmixer_1536_20,162.04,1579.822,256,224,48.68,33.03,51.63
convnext_large_384_in22ft1k,160.02,1599.775,256,384,101.09,126.74,197.77
cait_xxs36_384,158.26,1617.553,256,384,14.35,183.7,17.37
xcit_medium_24_p8_224,157.17,1628.769,256,224,63.53,121.23,84.32
xcit_medium_24_p8_224_dist,157.13,1629.205,256,224,63.53,121.23,84.32
eca_nfnet_l3,156.17,1639.21,256,448,52.55,118.4,72.04
resnetrs350,147.0,1741.501,256,384,77.59,154.74,163.96
ig_resnext101_32x32d,144.49,1771.686,256,224,87.29,91.12,468.53
xcit_tiny_24_p8_384_dist,141.04,1815.079,256,384,27.05,132.95,12.11
xcit_small_12_p8_384_dist,137.26,1865.112,256,384,54.92,138.29,26.21
vit_huge_patch14_224,131.97,1939.888,256,224,167.4,139.41,632.05
cait_s24_384,129.91,1970.595,256,384,32.17,245.31,47.06
xcit_large_24_p16_384_dist,127.3,2011.004,256,384,105.35,137.17,189.1
efficientnetv2_xl,125.14,2045.696,256,512,93.85,247.32,208.12
tf_efficientnetv2_xl_in21ft1k,124.18,2061.565,256,512,93.85,247.32,208.12
resnest269e,119.71,2138.483,256,416,77.69,171.98,110.93
nfnet_f3s,116.49,2197.633,256,416,115.58,141.78,254.92
nfnet_f3,115.68,2213.012,256,416,115.58,141.78,254.92
convnext_xlarge_384_in22ft1k,109.41,1169.883,128,384,179.18,168.99,350.2
efficientnet_b7,108.34,590.697,64,600,38.33,289.94,66.35
swin_large_patch4_window12_384,107.91,1186.161,128,384,104.08,202.16,196.74
resnetrs420,106.92,2394.315,256,416,108.45,213.79,191.89
tf_efficientnet_b7_ap,105.47,606.819,64,600,38.33,289.94,66.35
tf_efficientnet_b7_ns,105.47,606.797,64,600,38.33,289.94,66.35
tf_efficientnet_b7,105.43,607.002,64,600,38.33,289.94,66.35
dm_nfnet_f3,102.2,2504.865,256,416,115.58,141.78,254.92
xcit_large_24_p8_224_dist,94.99,2695.138,256,224,141.23,181.56,188.93
xcit_large_24_p8_224,94.97,2695.444,256,224,141.23,181.56,188.93
resnetv2_152x2_bit_teacher_384,94.51,2708.696,256,384,136.16,132.56,236.34
resnetv2_50x3_bitm,93.18,1373.716,128,448,145.7,133.37,217.32
vit_large_patch16_384,88.86,2880.826,256,384,191.21,270.24,304.72
vit_giant_patch14_224,87.24,2934.557,256,224,267.18,192.64,1012.61
cait_s36_384,87.06,2940.456,256,384,47.99,367.4,68.37
ig_resnext101_32x48d,85.45,1497.919,128,224,153.57,131.06,828.41
beit_large_patch16_384,77.25,3314.013,256,384,191.21,270.24,305.0
xcit_small_24_p8_384_dist,72.02,3554.648,256,384,105.24,265.91,47.63
resnetv2_152x2_bitm,69.3,1847.055,128,448,184.99,180.43,236.34
efficientnet_b8,67.89,942.729,64,672,63.48,442.89,87.41
tf_efficientnet_b8,66.48,962.617,64,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,66.47,962.833,64,672,63.48,442.89,87.41
nfnet_f4s,65.05,3935.454,256,512,216.26,262.26,316.07
nfnet_f4,64.4,3975.384,256,512,216.26,262.26,316.07
resnetv2_101x3_bitm,57.01,2245.355,128,448,280.33,194.78,387.93
dm_nfnet_f4,56.77,4509.206,256,512,216.26,262.26,316.07
xcit_medium_24_p8_384_dist,53.32,4800.797,256,384,186.67,354.73,84.32
vit_gigantic_patch14_224,53.03,4827.242,256,224,483.95,275.37,1844.44
nfnet_f5s,47.06,5439.402,256,544,290.97,349.71,377.21
nfnet_f5,46.62,5491.767,256,544,290.97,349.71,377.21
dm_nfnet_f5,41.31,6196.83,256,544,290.97,349.71,377.21
tf_efficientnet_l2_ns_475,38.18,1676.113,64,475,172.11,609.89,480.31
nfnet_f6s,33.85,7563.356,256,576,378.69,452.2,438.36
nfnet_f6,33.53,7634.937,256,576,378.69,452.2,438.36
xcit_large_24_p8_384_dist,32.23,7943.828,256,384,415.0,531.82,188.93
beit_large_patch16_512,31.3,2044.665,64,512,362.24,656.39,305.67
cait_m36_384,30.09,8507.481,256,384,173.11,734.81,271.22
dm_nfnet_f6,30.02,8526.734,256,576,378.69,452.2,438.36
nfnet_f7s,26.4,9698.058,256,608,480.39,570.85,499.5
nfnet_f7,26.15,9791.252,256,608,480.39,570.85,499.5
resnetv2_152x4_bitm,18.07,3541.734,64,480,844.84,414.26,936.53
efficientnet_l2,13.68,2266.184,31,800,479.12,1707.39,480.31
tf_efficientnet_l2_ns,13.51,2295.302,31,800,479.12,1707.39,480.31
cait_m48_448,13.05,9810.688,128,448,329.41,1708.23,356.46
1 model infer_samples_per_sec infer_step_time infer_batch_size infer_img_size infer_gmacs infer_macts param_count
2 tinynet_e 38522.1 6.633 256 106 0.03 0.69 2.04
3 mobilenetv3_small_050 32214.84 7.935 256 224 0.03 0.92 1.59
4 lcnet_035 31434.42 8.133 256 224 0.03 1.04 1.64
5 lcnet_050 27990.74 9.135 256 224 0.05 1.26 1.88
6 tf_mobilenetv3_small_minimal_100 24613.65 10.389 256 224 0.06 1.41 2.04
7 mobilenetv3_small_075 23970.62 10.668 256 224 0.05 1.3 2.04
8 tinynet_d 22226.54 11.506 256 152 0.05 1.42 2.34
9 mobilenetv3_small_100 20961.66 12.202 256 224 0.06 1.42 2.54
10 tf_mobilenetv3_small_075 20346.43 12.57 256 224 0.05 1.3 2.04
11 tf_mobilenetv3_small_100 18514.72 13.814 256 224 0.06 1.42 2.54
12 lcnet_075 18250.57 14.016 256 224 0.1 1.99 2.36
13 levit_128s 17514.74 14.605 256 224 0.31 1.88 7.78
14 mnasnet_small 16174.2 15.817 256 224 0.07 2.16 2.03
15 mobilenetv2_035 16065.18 15.924 256 224 0.07 2.86 1.68
16 regnetx_002 15056.58 16.991 256 224 0.2 2.16 2.68
17 ghostnet_050 14915.33 17.152 256 224 0.05 1.77 2.59
18 lcnet_100 14745.93 17.35 256 224 0.16 2.52 2.95
19 regnety_002 13600.49 18.812 256 224 0.2 2.17 3.16
20 mobilenetv2_050 12921.59 19.8 256 224 0.1 3.64 1.97
21 mnasnet_050 12170.95 21.022 256 224 0.11 3.07 2.22
22 levit_128 12112.09 21.125 256 224 0.41 2.71 9.21
23 semnasnet_050 11653.92 21.956 256 224 0.11 3.44 2.08
24 tinynet_c 11441.66 22.362 256 184 0.11 2.87 2.46
25 levit_192 10512.15 24.341 256 224 0.66 3.2 10.95
26 gernet_s 10252.23 24.96 256 224 0.75 2.65 8.17
27 mixer_s32_224 9991.39 25.611 256 224 1.0 2.28 19.1
28 mobilenetv3_large_075 9848.53 25.983 256 224 0.16 4.0 3.99
29 ese_vovnet19b_slim_dw 9567.57 26.746 256 224 0.4 5.28 1.9
30 lcnet_150 9561.66 26.763 256 224 0.34 3.79 4.5
31 vit_small_patch32_224 9406.05 27.206 256 224 1.15 2.5 22.88
32 regnetx_004 9291.08 27.542 256 224 0.4 3.14 5.16
33 tf_mobilenetv3_large_minimal_100 9242.84 27.684 256 224 0.22 4.4 3.92
34 tf_mobilenetv3_large_075 8812.22 29.037 256 224 0.16 4.0 3.99
35 mobilenetv3_rw 8698.97 29.417 256 224 0.23 4.41 5.48
36 vit_tiny_r_s16_p8_224 8636.97 29.629 256 224 0.44 2.06 6.34
37 mobilenetv3_large_100 8617.52 29.696 256 224 0.23 4.41 5.48
38 mobilenetv3_large_100_miil 8613.51 29.709 256 224 0.23 4.41 5.48
39 ssl_resnet18 8520.11 30.036 256 224 1.82 2.48 11.69
40 gluon_resnet18_v1b 8514.08 30.057 256 224 1.82 2.48 11.69
41 resnet18 8481.72 30.171 256 224 1.82 2.48 11.69
42 swsl_resnet18 8383.36 30.526 256 224 1.82 2.48 11.69
43 ghostnet_100 8302.41 30.823 256 224 0.15 3.55 5.18
44 mobilenetv2_075 8284.54 30.89 256 224 0.22 5.86 2.64
45 mnasnet_075 8193.74 31.233 256 224 0.23 4.77 3.17
46 seresnet18 7935.21 32.25 256 224 1.82 2.49 11.78
47 levit_256 7830.25 32.682 256 224 1.13 4.23 18.89
48 tf_mobilenetv3_large_100 7782.64 32.881 256 224 0.23 4.41 5.48
49 legacy_seresnet18 7750.12 33.02 256 224 1.82 2.49 11.78
50 regnetx_006 7679.18 33.326 256 224 0.61 3.98 6.2
51 semnasnet_075 7673.81 33.35 256 224 0.23 5.54 2.91
52 mobilenetv2_100 7580.19 33.761 256 224 0.31 6.68 3.5
53 regnety_004 7520.0 34.031 256 224 0.41 3.89 4.34
54 tinynet_b 7462.13 34.294 256 188 0.21 4.44 3.73
55 hardcorenas_a 7310.63 35.006 256 224 0.23 4.38 5.26
56 hardcorenas_b 7243.65 35.33 256 224 0.26 5.09 5.18
57 mnasnet_100 7211.43 35.488 256 224 0.33 5.46 4.38
58 mnasnet_b1 7197.57 35.556 256 224 0.33 5.46 4.38
59 resnet18d 7139.78 35.845 256 224 2.06 3.29 11.71
60 mnasnet_a1 6803.23 37.618 256 224 0.32 6.23 3.89
61 ghostnet_130 6799.96 37.636 256 224 0.24 4.6 7.36
62 semnasnet_100 6798.37 37.645 256 224 0.32 6.23 3.89
63 hardcorenas_c 6791.21 37.684 256 224 0.28 5.01 5.52
64 spnasnet_100 6742.99 37.955 256 224 0.35 6.03 4.42
65 regnety_006 6741.6 37.962 256 224 0.61 4.33 6.06
66 ese_vovnet19b_slim 6727.44 38.04 256 224 1.69 3.52 3.17
67 hardcorenas_d 6616.74 38.678 256 224 0.3 4.93 7.5
68 tf_efficientnetv2_b0 6404.53 39.959 256 224 0.73 4.77 7.14
69 regnetx_008 6126.62 41.774 256 224 0.81 5.15 7.26
70 efficientnet_lite0 5820.53 43.971 256 224 0.4 6.74 4.65
71 dla46_c 5815.5 44.006 256 224 0.58 4.5 1.3
72 resnetblur18 5731.2 44.657 256 224 2.34 3.39 11.69
73 mobilenetv2_110d 5728.31 44.679 256 224 0.45 8.71 4.52
74 tinynet_a 5715.04 44.781 256 192 0.35 5.41 6.19
75 rexnet_100 5705.81 44.855 256 224 0.41 7.44 4.8
76 rexnetr_100 5702.38 44.882 256 224 0.43 7.72 4.88
77 hardcorenas_f 5633.4 45.432 256 224 0.35 5.57 8.2
78 regnety_008 5562.41 46.011 256 224 0.81 5.25 6.26
79 hardcorenas_e 5407.35 47.331 256 224 0.35 5.65 8.07
80 fbnetc_100 5335.41 47.97 256 224 0.4 6.51 5.57
81 skresnet18 5314.32 48.161 256 224 1.82 3.24 11.96
82 tf_efficientnet_lite0 5277.93 48.492 256 224 0.4 6.74 4.65
83 efficientnet_b0 5118.94 50.0 256 224 0.4 6.75 5.29
84 mobilenetv2_140 5111.46 50.073 256 224 0.6 9.57 6.11
85 ese_vovnet19b_dw 5070.62 50.475 256 224 1.34 8.25 6.54
86 mnasnet_140 5006.18 51.125 256 224 0.6 7.71 7.12
87 efficientnet_b1_pruned 5001.01 51.178 256 240 0.4 6.21 6.33
88 resnet34 4971.16 51.486 256 224 3.67 3.74 21.8
89 gluon_resnet34_v1b 4968.4 51.515 256 224 3.67 3.74 21.8
90 tv_resnet34 4947.3 51.734 256 224 3.67 3.74 21.8
91 visformer_tiny 4922.23 51.997 256 224 1.27 5.72 10.32
92 hrnet_w18_small 4823.01 53.067 256 224 1.61 5.72 13.19
93 semnasnet_140 4792.63 53.404 256 224 0.6 8.87 6.11
94 levit_384 4732.88 54.078 256 224 2.36 6.26 39.13
95 tf_efficientnet_b0_ns 4689.2 54.583 256 224 0.4 6.75 5.29
96 tf_efficientnet_b0_ap 4677.78 54.715 256 224 0.4 6.75 5.29
97 tf_efficientnet_b0 4677.33 54.72 256 224 0.4 6.75 5.29
98 seresnet34 4638.93 55.174 256 224 3.67 3.74 21.96
99 selecsls42b 4636.17 55.206 256 224 2.98 4.62 32.46
100 dla46x_c 4631.52 55.26 256 224 0.54 5.66 1.07
101 selecsls42 4604.3 55.589 256 224 2.94 4.62 30.35
102 deit_tiny_patch16_224 4580.12 55.882 256 224 1.26 5.97 5.72
103 vit_tiny_patch16_224 4575.68 55.937 256 224 1.26 5.97 5.72
104 gernet_m 4537.32 56.41 256 224 3.02 5.24 21.14
105 deit_tiny_distilled_patch16_224 4513.87 56.702 256 224 1.27 6.01 5.91
106 nf_regnet_b0 4512.73 56.717 256 256 0.64 5.58 8.76
107 legacy_seresnet34 4498.05 56.902 256 224 3.67 3.74 21.96
108 resnet34d 4471.13 57.245 256 224 3.91 4.54 21.82
109 rexnetr_130 4425.1 57.84 256 224 0.68 9.81 7.61
110 pit_ti_distilled_224 4357.18 58.742 256 224 0.71 6.23 5.1
111 pit_ti_224 4345.5 58.9 256 224 0.7 6.19 4.85
112 mixnet_s 4322.59 59.212 256 224 0.25 6.25 4.13
113 dla60x_c 4316.36 59.296 256 224 0.59 6.01 1.32
114 tf_efficientnetv2_b1 4297.02 59.563 256 240 1.21 7.34 8.14
115 rexnet_130 4232.23 60.476 256 224 0.68 9.71 7.56
116 xcit_nano_12_p16_224_dist 4222.67 60.614 256 224 0.56 4.17 3.05
117 xcit_nano_12_p16_224 4207.85 60.827 256 224 0.56 4.17 3.05
118 resnet26 4180.52 61.224 256 224 2.36 7.35 16.0
119 resmlp_12_distilled_224 4174.74 61.31 256 224 3.01 5.5 15.35
120 resmlp_12_224 4169.39 61.389 256 224 3.01 5.5 15.35
121 mobilenetv2_120d 4149.08 61.689 256 224 0.69 11.97 5.83
122 vit_base_patch32_224_sam 4128.66 61.994 256 224 4.41 5.01 88.22
123 vit_base_patch32_224 4107.27 62.317 256 224 4.41 5.01 88.22
124 tf_mixnet_s 4070.89 62.873 256 224 0.25 6.25 4.13
125 repvgg_b0 4034.35 63.443 256 224 3.41 6.15 15.82
126 mixer_b32_224 3992.12 64.115 256 224 3.24 6.29 60.29
127 selecsls60 3976.19 64.371 256 224 3.59 5.52 30.67
128 selecsls60b 3972.94 64.425 256 224 3.63 5.52 32.77
129 rexnetr_150 3836.3 66.719 256 224 0.89 11.13 9.78
130 efficientnet_lite1 3836.28 66.719 256 240 0.62 10.14 5.42
131 resnet26d 3798.91 67.377 256 224 2.6 8.15 16.01
132 dla34 3769.64 67.899 256 224 3.07 5.02 15.74
133 rexnet_150 3681.94 69.516 256 224 0.9 11.21 9.73
134 ecaresnet50d_pruned 3648.06 70.162 256 224 2.53 6.43 19.94
135 nf_resnet26 3644.75 70.227 256 224 2.41 7.35 16.0
136 tf_efficientnet_lite1 3554.59 72.006 256 240 0.62 10.14 5.42
137 pit_xs_224 3526.31 72.586 256 224 1.4 7.71 10.62
138 regnetx_016 3513.85 72.844 256 224 1.62 7.93 9.19
139 pit_xs_distilled_224 3500.42 73.123 256 224 1.41 7.76 11.0
140 fbnetv3_d 3391.58 75.47 256 256 0.68 11.1 10.31
141 efficientnet_es_pruned 3370.79 75.934 256 224 1.81 8.73 5.44
142 fbnetv3_b 3370.57 75.939 256 256 0.55 9.1 8.6
143 efficientnet_es 3367.33 76.013 256 224 1.81 8.73 5.44
144 efficientnet_b2_pruned 3361.3 76.15 256 260 0.73 9.13 8.31
145 efficientnet_cc_b0_8e 3277.21 78.103 256 224 0.42 9.42 24.01
146 mixer_s16_224 3266.14 78.369 256 224 3.79 5.97 18.53
147 efficientnet_cc_b0_4e 3265.3 78.389 256 224 0.41 9.42 13.31
148 tf_efficientnet_es 3259.95 78.516 256 224 1.81 8.73 5.44
149 tf_efficientnetv2_b2 3255.37 78.626 256 260 1.72 9.84 10.1
150 resnest14d 3217.37 79.556 256 224 2.76 7.33 10.61
151 nf_seresnet26 3202.97 79.914 256 224 2.41 7.36 17.4
152 nf_ecaresnet26 3189.01 80.265 256 224 2.41 7.36 16.0
153 gernet_l 3181.93 80.443 256 256 4.57 8.0 31.08
154 regnety_016 3102.97 82.489 256 224 1.63 8.04 11.2
155 tf_efficientnet_cc_b0_8e 3093.44 82.743 256 224 0.42 9.42 24.01
156 tf_efficientnet_cc_b0_4e 3085.55 82.954 256 224 0.41 9.42 13.31
157 mixnet_m 3056.44 83.746 256 224 0.36 8.19 5.01
158 skresnet34 3030.56 84.459 256 224 3.67 5.13 22.28
159 resnext26ts 3023.16 84.668 256 256 2.43 10.52 10.3
160 repvgg_a2 3004.59 85.192 256 224 5.7 6.26 28.21
161 vit_tiny_r_s16_p8_384 3002.92 85.238 256 384 1.34 6.49 6.36
162 legacy_seresnext26_32x4d 3001.06 85.291 256 224 2.49 9.39 16.79
163 vit_small_patch32_384 2997.9 85.382 256 384 3.45 8.25 22.92
164 xcit_tiny_12_p16_224_dist 2978.68 85.929 256 224 1.24 6.29 6.72
165 xcit_tiny_12_p16_224 2968.51 86.225 256 224 1.24 6.29 6.72
166 resnet26t 2964.54 86.343 256 256 3.35 10.52 16.01
167 gmixer_12_224 2952.25 86.702 256 224 2.67 7.26 12.7
168 seresnext26ts 2943.83 86.951 256 256 2.43 10.52 10.39
169 eca_resnext26ts 2943.35 86.964 256 256 2.43 10.52 10.3
170 efficientnet_lite2 2941.13 87.03 256 260 0.89 12.9 6.09
171 tf_efficientnet_b1_ap 2928.7 87.399 256 240 0.71 10.88 7.79
172 tf_efficientnet_b1 2924.32 87.53 256 240 0.71 10.88 7.79
173 tf_efficientnet_b1_ns 2924.08 87.537 256 240 0.71 10.88 7.79
174 tf_mixnet_m 2922.35 87.587 256 224 0.36 8.19 5.01
175 gcresnext26ts 2878.37 88.928 256 256 2.43 10.53 10.48
176 efficientnet_b1 2842.35 90.055 256 256 0.77 12.22 7.79
177 ecaresnet101d_pruned 2827.84 90.516 256 224 3.48 7.69 24.88
178 seresnext26tn_32x4d 2809.03 91.123 256 224 2.7 10.09 16.81
179 ecaresnext50t_32x4d 2808.18 91.151 256 224 2.7 10.09 15.41
180 seresnext26t_32x4d 2807.76 91.164 256 224 2.7 10.09 16.81
181 ecaresnext26t_32x4d 2799.26 91.441 256 224 2.7 10.09 15.41
182 seresnext26d_32x4d 2790.46 91.729 256 224 2.73 10.19 16.81
183 ecaresnetlight 2759.56 92.757 256 224 4.11 8.42 30.16
184 rexnetr_200 2747.9 93.151 256 224 1.59 15.11 16.52
185 nf_regnet_b2 2743.38 93.303 256 272 1.22 9.27 14.31
186 tf_efficientnet_lite2 2742.42 93.334 256 260 0.89 12.9 6.09
187 nf_regnet_b1 2740.87 93.389 256 288 1.02 9.2 10.22
188 crossvit_tiny_240 2729.62 93.771 256 240 1.57 9.08 7.01
189 resnetv2_50 2688.33 95.214 256 224 4.11 11.11 25.55
190 rexnet_200 2686.43 95.282 256 224 1.56 14.91 16.37
191 crossvit_9_240 2684.97 95.332 256 240 1.85 9.52 8.55
192 eca_botnext26ts_256 2675.06 95.687 256 256 2.46 11.6 10.59
193 vgg11 2663.05 96.12 256 224 7.61 7.44 132.86
194 botnet26t_256 2660.83 96.197 256 256 3.32 11.98 12.49
195 tresnet_m 2657.08 96.334 256 224 5.74 7.31 31.39
196 eca_halonext26ts 2619.52 97.716 256 256 2.44 11.46 10.76
197 halonet26t 2612.17 97.992 256 256 3.19 11.69 12.48
198 crossvit_9_dagger_240 2602.86 98.339 256 240 1.99 9.97 8.78
199 efficientnet_b3_pruned 2602.84 98.342 256 300 1.04 11.86 9.86
200 resnet50 2602.03 98.373 256 224 4.11 11.11 25.56
201 tv_resnet50 2597.64 98.539 256 224 4.11 11.11 25.56
202 gluon_resnet50_v1b 2593.37 98.702 256 224 4.11 11.11 25.56
203 ssl_resnet50 2588.68 98.881 256 224 4.11 11.11 25.56
204 swsl_resnet50 2588.5 98.887 256 224 4.11 11.11 25.56
205 convit_tiny 2557.96 100.066 256 224 1.26 7.94 5.71
206 vovnet39a 2552.48 100.283 256 224 7.09 6.73 22.6
207 resnetv2_50t 2536.73 100.905 256 224 4.32 11.82 25.57
208 resnetv2_50d 2523.31 101.442 256 224 4.35 11.92 25.57
209 resnet32ts 2520.72 101.547 256 256 4.63 11.58 17.96
210 resnet33ts 2487.04 102.921 256 256 4.76 11.66 19.68
211 bat_resnext26ts 2486.58 102.938 256 256 2.53 12.51 10.73
212 vit_small_resnet26d_224 2484.66 103.019 256 224 5.07 11.12 63.61
213 ese_vovnet39b 2483.84 103.055 256 224 7.09 6.74 24.57
214 cspresnet50 2476.24 103.369 256 256 4.54 11.5 21.62
215 eca_vovnet39b 2474.83 103.43 256 224 7.09 6.74 22.6
216 hrnet_w18_small_v2 2473.49 103.485 256 224 2.62 9.65 15.6
217 gluon_resnet50_v1c 2461.53 103.989 256 224 4.35 11.92 25.58
218 cspresnext50 2459.33 104.08 256 224 3.1 12.14 20.57
219 resnet50t 2452.68 104.364 256 224 4.32 11.82 25.57
220 gluon_resnet50_v1d 2447.81 104.572 256 224 4.35 11.92 25.58
221 resnet50d 2444.17 104.728 256 224 4.35 11.92 25.58
222 dpn68b 2437.01 105.034 256 224 2.35 10.47 12.61
223 legacy_seresnet50 2425.37 105.54 256 224 3.88 10.6 28.09
224 gmlp_ti16_224 2422.83 105.65 256 224 1.34 7.55 5.87
225 seresnet33ts 2418.32 105.847 256 256 4.76 11.66 19.78
226 eca_resnet33ts 2414.21 106.028 256 256 4.76 11.66 19.68
227 dpn68 2412.98 106.079 256 224 2.35 10.47 12.61
228 selecsls84 2409.11 106.251 256 224 5.9 7.57 50.95
229 vgg11_bn 2392.35 106.996 256 224 7.62 7.44 132.87
230 mixnet_l 2360.8 108.426 256 224 0.58 10.84 7.33
231 gcresnet33ts 2355.59 108.666 256 256 4.76 11.68 19.88
232 lambda_resnet26t 2353.44 108.765 256 256 3.02 11.87 10.96
233 pit_s_224 2338.74 109.449 256 224 2.88 11.56 23.46
234 dla60 2331.19 109.802 256 224 4.26 10.16 22.04
235 cspresnet50w 2330.53 109.833 256 256 5.04 12.19 28.12
236 seresnet50 2324.39 110.125 256 224 4.11 11.13 28.09
237 resnest26d 2321.38 110.267 256 224 3.64 9.97 17.07
238 pit_s_distilled_224 2320.78 110.295 256 224 2.9 11.64 24.04
239 cspresnet50d 2313.66 110.634 256 256 4.86 12.55 21.64
240 deit_small_patch16_224 2312.17 110.706 256 224 4.61 11.95 22.05
241 vit_small_patch16_224 2301.45 111.22 256 224 4.61 11.95 22.05
242 deit_small_distilled_patch16_224 2273.38 112.595 256 224 4.63 12.02 22.44
243 tf_efficientnet_b2_ap 2266.39 112.943 256 260 1.02 13.83 9.11
244 tf_efficientnet_b2 2265.35 112.996 256 260 1.02 13.83 9.11
245 tf_efficientnet_b2_ns 2264.55 113.035 256 260 1.02 13.83 9.11
246 tf_mixnet_l 2263.46 113.087 256 224 0.58 10.84 7.33
247 densenet121 2251.23 113.702 256 224 2.87 6.9 7.98
248 res2net50_48w_2s 2244.59 114.041 256 224 4.18 11.72 25.29
249 tv_densenet121 2243.68 114.087 256 224 2.87 6.9 7.98
250 resnetaa50d 2224.41 115.074 256 224 5.39 12.44 25.58
251 seresnet50t 2204.01 116.141 256 224 4.32 11.83 28.1
252 resnetblur50 2197.45 116.486 256 224 5.16 12.02 25.56
253 haloregnetz_b 2191.27 116.814 256 224 1.97 11.94 11.68
254 ecaresnet50d 2187.04 117.042 256 224 4.35 11.93 25.58
255 resnetrs50 2155.43 118.757 256 224 4.48 12.14 35.69
256 densenet121d 2152.18 118.936 256 224 3.11 7.7 8.0
257 gluon_resnet50_v1s 2129.0 120.233 256 224 5.47 13.52 25.68
258 visformer_small 2128.36 120.269 256 224 4.88 11.43 40.22
259 resmlp_24_224 2126.97 120.348 256 224 5.96 10.91 30.02
260 efficientnet_b2a 2125.57 120.427 256 288 1.12 16.2 9.11
261 resmlp_24_distilled_224 2124.32 120.497 256 224 5.96 10.91 30.02
262 efficientnet_b2 2121.96 120.632 256 288 1.12 16.2 9.11
263 regnetx_032 2121.9 120.635 256 224 3.2 11.37 15.3
264 adv_inception_v3 2116.52 120.939 256 299 5.73 8.97 23.83
265 gluon_inception_v3 2110.53 121.285 256 299 5.73 8.97 23.83
266 inception_v3 2106.47 121.518 256 299 5.73 8.97 23.83
267 tf_inception_v3 2102.15 121.765 256 299 5.73 8.97 23.83
268 vovnet57a 2101.83 121.786 256 224 8.95 7.52 36.64
269 resnetblur50d 2086.08 122.707 256 224 5.4 12.82 25.58
270 efficientnet_em 2080.04 123.062 256 240 3.04 14.34 6.9
271 efficientnet_cc_b1_8e 2060.35 124.238 256 240 0.75 15.44 39.72
272 cspresnext50_iabn 2046.67 125.068 256 256 4.02 15.86 20.57
273 densenetblur121d 2045.46 125.142 256 224 3.11 7.9 8.0
274 tf_efficientnet_em 2031.54 125.998 256 240 3.04 14.34 6.9
275 vit_base_resnet26d_224 2020.03 126.719 256 224 6.97 13.16 101.4
276 ese_vovnet57b 2017.2 126.896 256 224 8.95 7.52 38.61
277 ssl_resnext50_32x4d 2016.48 126.942 256 224 4.26 14.4 25.03
278 swsl_resnext50_32x4d 2014.9 127.042 256 224 4.26 14.4 25.03
279 seresnetaa50d 2014.43 127.071 256 224 5.4 12.46 28.11
280 gluon_resnext50_32x4d 2013.28 127.144 256 224 4.26 14.4 25.03
281 tv_resnext50_32x4d 2010.77 127.303 256 224 4.26 14.4 25.03
282 resnext50_32x4d 2010.4 127.325 256 224 4.26 14.4 25.03
283 tf_efficientnet_cc_b1_8e 1970.45 129.905 256 240 0.75 15.44 39.72
284 cspdarknet53_iabn 1958.85 130.675 256 256 6.53 16.81 27.64
285 dla60x 1947.14 131.46 256 224 3.54 13.8 17.35
286 regnetx_040 1940.97 131.881 256 224 3.99 12.2 22.12
287 skresnet50 1940.94 131.882 256 224 4.11 12.5 25.8
288 nf_seresnet50 1937.18 132.139 256 224 4.21 11.13 28.09
289 nf_ecaresnet50 1931.32 132.54 256 224 4.21 11.13 25.56
290 res2net50_26w_4s 1927.67 132.79 256 224 4.28 12.61 25.7
291 tf_efficientnetv2_b3 1921.68 133.203 256 300 3.04 15.74 14.36
292 resnext50d_32x4d 1917.13 133.521 256 224 4.5 15.2 25.05
293 regnety_040 1911.7 133.9 256 224 4.0 12.29 20.65
294 gcresnet50t 1897.82 134.88 256 256 5.42 14.67 25.9
295 efficientnetv2_rw_t 1893.97 135.153 256 288 3.19 16.42 13.65
296 sehalonet33ts 1881.49 136.051 256 256 3.55 14.7 13.69
297 lambda_resnet26rpt_256 1854.82 138.007 256 256 3.16 11.87 10.99
298 skresnet50d 1849.16 138.429 256 224 4.36 13.31 25.82
299 gcresnext50ts 1842.39 138.938 256 256 3.75 15.46 15.67
300 seresnext50_32x4d 1842.11 138.958 256 224 4.26 14.42 27.56
301 gluon_seresnext50_32x4d 1836.43 139.388 256 224 4.26 14.42 27.56
302 dla60_res2net 1834.89 139.503 256 224 4.15 12.34 20.85
303 legacy_seresnext50_32x4d 1833.54 139.608 256 224 4.26 14.42 27.56
304 resnest50d_1s4x24d 1830.82 139.816 256 224 4.43 13.57 25.68
305 repvgg_b1g4 1821.65 140.52 256 224 8.15 10.64 39.97
306 darknet53 1820.66 140.594 256 256 9.31 12.39 41.61
307 densenet169 1814.76 141.051 256 224 3.4 7.3 14.15
308 gc_efficientnetv2_rw_t 1812.58 141.223 256 288 3.2 16.45 13.68
309 res2net50_14w_8s 1809.09 141.495 256 224 4.21 13.28 25.06
310 coat_lite_tiny 1803.47 141.935 256 224 1.6 11.65 5.72
311 res2next50 1798.23 142.351 256 224 4.2 13.71 24.67
312 cspdarknet53 1759.27 145.502 256 256 6.57 16.81 27.64
313 ecaresnet26t 1753.56 145.977 256 320 5.24 16.44 16.01
314 dla60_res2next 1751.16 146.174 256 224 3.49 13.17 17.03
315 efficientnet_lite3 1747.68 146.468 256 300 1.65 21.85 8.2
316 nf_regnet_b3 1737.85 147.296 256 320 2.05 14.61 18.59
317 vgg13 1734.82 147.554 256 224 11.31 12.25 133.05
318 regnetz_b16 1724.03 148.477 256 288 2.39 16.43 9.72
319 coat_lite_mini 1717.28 149.059 256 224 2.0 12.25 11.01
320 mixnet_xl 1685.34 151.886 256 224 0.93 14.57 11.9
321 vit_small_r26_s32_224 1673.77 152.936 256 224 3.56 9.85 36.43
322 sebotnet33ts_256 1634.05 156.655 256 256 3.89 17.46 13.7
323 resnetv2_101 1630.31 157.012 256 224 7.83 16.23 44.54
324 tf_efficientnet_lite3 1628.65 157.171 256 300 1.65 21.85 8.2
325 convnext_tiny 1599.8 160.006 256 224 4.47 13.44 28.59
326 tv_resnet101 1599.73 160.015 256 224 7.83 16.23 44.55
327 gluon_resnet101_v1b 1599.19 160.07 256 224 7.83 16.23 44.55
328 convnext_tiny_hnf 1598.61 160.125 256 224 4.47 13.44 28.59
329 resnet101 1594.14 160.576 256 224 7.83 16.23 44.55
330 xcit_tiny_24_p16_224_dist 1587.39 161.257 256 224 2.34 11.82 12.12
331 xcit_tiny_24_p16_224 1582.27 161.779 256 224 2.34 11.82 12.12
332 repvgg_b1 1574.77 162.553 256 224 13.16 10.64 57.42
333 resnetv2_101d 1569.58 163.088 256 224 8.07 17.04 44.56
334 xcit_small_12_p16_224_dist 1568.96 163.153 256 224 4.82 12.58 26.25
335 xcit_small_12_p16_224 1566.49 163.411 256 224 4.82 12.58 26.25
336 vit_base_resnet50d_224 1557.57 164.346 256 224 8.73 16.92 110.97
337 resnest50d 1550.54 165.09 256 224 5.4 14.36 27.48
338 gluon_resnet101_v1c 1550.28 165.12 256 224 8.08 17.04 44.57
339 lambda_resnet50ts 1550.05 165.145 256 256 5.07 17.48 21.54
340 vgg13_bn 1545.09 165.675 256 224 11.33 12.25 133.05
341 gluon_resnet101_v1d 1542.32 165.972 256 224 8.08 17.04 44.57
342 dla102 1542.08 165.995 256 224 7.19 14.18 33.27
343 twins_svt_small 1526.51 167.691 256 224 2.94 13.75 24.06
344 wide_resnet50_2 1515.12 168.952 256 224 11.43 14.4 68.88
345 gmixer_24_224 1503.65 170.24 256 224 5.28 14.45 24.72
346 resnetv2_50x1_bit_distilled 1496.53 171.051 256 224 4.23 11.11 25.55
347 regnetx_080 1491.93 171.578 256 224 8.02 14.06 39.57
348 xcit_nano_12_p16_384_dist 1489.5 171.858 256 384 1.64 12.15 3.05
349 crossvit_small_240 1472.6 173.826 256 240 5.63 18.17 26.86
350 res2net50_26w_6s 1468.77 174.282 256 224 6.33 15.28 37.05
351 legacy_seresnet101 1466.72 174.527 256 224 7.61 15.74 49.33
352 halonet50ts 1459.14 175.434 256 256 5.3 19.2 22.73
353 resnetaa101d 1449.52 176.598 256 224 9.12 17.56 44.57
354 fbnetv3_g 1435.53 178.32 256 288 1.77 21.09 16.62
355 seresnet101 1425.31 179.597 256 224 7.84 16.27 49.33
356 resmlp_36_224 1423.79 179.791 256 224 8.91 16.33 44.69
357 resmlp_36_distilled_224 1422.98 179.892 256 224 8.91 16.33 44.69
358 regnetx_064 1422.54 179.948 256 224 6.49 16.37 26.21
359 resnetv2_50d_gn 1422.29 179.98 256 224 4.38 11.92 25.57
360 densenet201 1416.58 180.704 256 224 4.34 7.85 20.01
361 vit_large_patch32_224 1414.43 180.979 256 224 15.39 13.3 306.54
362 gluon_resnet101_v1s 1407.4 181.883 256 224 9.19 18.64 44.67
363 vgg16 1399.33 182.931 256 224 15.47 13.56 138.36
364 lamhalobotnet50ts_256 1392.5 183.829 256 256 5.02 18.44 22.57
365 resnetblur101d 1390.53 184.088 256 224 9.12 17.94 44.57
366 nf_resnet50 1387.13 184.542 256 288 6.88 18.37 25.56
367 nf_resnet101 1387.03 184.555 256 224 8.01 16.23 44.55
368 ecaresnet101d 1375.85 186.054 256 224 8.08 17.07 44.57
369 vit_base_r26_s32_224 1374.54 186.232 256 224 6.81 12.36 101.38
370 regnety_032 1353.73 189.095 256 288 5.29 18.61 19.44
371 crossvit_15_240 1351.08 189.465 256 240 5.81 19.77 27.53
372 tresnet_l 1350.23 189.586 256 224 10.88 11.9 55.99
373 gmlp_s16_224 1343.26 190.569 256 224 4.42 15.1 19.42
374 tf_efficientnet_b3 1341.98 190.75 256 300 1.87 23.83 12.23
375 tf_efficientnet_b3_ns 1340.42 190.973 256 300 1.87 23.83 12.23
376 tf_efficientnet_b3_ap 1339.36 191.124 256 300 1.87 23.83 12.23
377 resnet51q 1338.83 191.2 256 288 8.07 20.94 35.7
378 hrnet_w18 1335.83 191.629 256 224 4.32 16.31 21.3
379 vit_base_patch32_384 1328.33 192.713 256 384 13.06 16.5 88.3
380 resnet50_gn 1327.88 192.777 256 224 4.14 11.11 25.56
381 mixer_l32_224 1318.62 194.131 256 224 11.27 19.86 206.94
382 xception 1316.52 194.441 256 299 8.4 35.83 22.86
383 crossvit_15_dagger_240 1308.42 195.641 256 240 6.13 20.43 28.21
384 dla102x 1302.91 196.468 256 224 5.89 19.42 26.31
385 efficientnet_b3a 1302.53 196.528 256 320 2.01 26.52 12.23
386 efficientnet_b3 1301.42 196.696 256 320 2.01 26.52 12.23
387 botnet50ts_256 1288.18 198.716 256 256 5.54 22.23 22.74
388 cait_xxs24_224 1287.06 198.889 256 224 2.53 20.29 11.96
389 mixer_b16_224_miil 1283.42 199.455 256 224 12.62 14.53 59.88
390 mixer_b16_224 1283.22 199.487 256 224 12.62 14.53 59.88
391 skresnext50_32x4d 1276.26 200.574 256 224 4.5 17.18 27.48
392 regnety_064 1272.95 201.095 256 224 6.39 16.41 30.58
393 swsl_resnext101_32x4d 1266.33 202.147 256 224 8.01 21.23 44.18
394 ssl_resnext101_32x4d 1265.67 202.252 256 224 8.01 21.23 44.18
395 gluon_resnext101_32x4d 1265.25 202.319 256 224 8.01 21.23 44.18
396 resnext101_32x4d 1264.74 202.401 256 224 8.01 21.23 44.18
397 vgg16_bn 1261.15 202.977 256 224 15.5 13.56 138.37
398 repvgg_b2g4 1251.52 204.539 256 224 12.63 12.9 61.76
399 halo2botnet50ts_256 1248.54 205.028 256 256 5.02 21.78 22.64
400 swin_tiny_patch4_window7_224 1248.01 205.115 256 224 4.51 17.06 28.29
401 twins_pcpvt_small 1232.89 207.63 256 224 3.83 18.08 24.11
402 regnety_080 1232.57 207.684 256 224 8.0 17.97 39.18
403 resnest50d_4s2x40d 1216.47 210.433 256 224 4.4 17.94 30.42
404 resnet61q 1209.86 211.583 256 288 9.87 21.52 36.85
405 nf_seresnet101 1194.71 214.265 256 224 8.02 16.27 49.33
406 ese_vovnet99b_iabn 1193.74 214.438 256 224 16.49 11.27 63.2
407 nf_ecaresnet101 1193.13 214.55 256 224 8.01 16.27 44.55
408 res2net50_26w_8s 1192.34 214.692 256 224 8.37 17.95 48.4
409 eca_nfnet_l0 1189.26 215.246 256 288 7.12 17.29 24.14
410 res2net101_26w_4s 1189.12 215.272 256 224 8.1 18.45 45.21
411 nfnet_l0 1185.85 215.867 256 288 7.13 17.29 35.07
412 dpn92 1179.85 216.965 256 224 6.54 18.21 37.67
413 vit_tiny_patch16_384 1177.29 217.437 256 384 4.7 25.39 5.79
414 convit_small 1173.71 218.096 256 224 5.76 17.87 27.78
415 ese_vovnet99b 1158.79 220.908 256 224 16.51 11.27 63.2
416 vgg19 1152.83 222.048 256 224 19.63 14.86 143.67
417 seresnext101_32x4d 1152.16 222.18 256 224 8.02 21.26 48.96
418 resnetv2_50d_evob 1150.91 222.42 256 224 4.33 11.92 25.59
419 gluon_seresnext101_32x4d 1150.75 222.451 256 224 8.02 21.26 48.96
420 legacy_seresnext101_32x4d 1150.68 222.464 256 224 8.02 21.26 48.96
421 ese_vovnet39b_evos 1144.38 223.689 256 224 7.07 6.74 24.58
422 hrnet_w32 1140.5 224.45 256 224 8.97 22.02 41.23
423 resnetv2_152 1132.73 225.989 256 224 11.55 22.56 60.19
424 xcit_nano_12_p8_224_dist 1129.67 226.602 256 224 2.16 15.71 3.05
425 xcit_nano_12_p8_224 1124.18 227.71 256 224 2.16 15.71 3.05
426 hrnet_w30 1121.05 228.345 256 224 8.15 21.21 37.71
427 tv_resnet152 1117.27 229.117 256 224 11.56 22.56 60.19
428 resnet152 1114.93 229.598 256 224 11.56 22.56 60.19
429 gluon_resnet152_v1b 1112.41 230.118 256 224 11.56 22.56 60.19
430 ecaresnet50t 1109.44 230.735 256 320 8.82 24.13 25.57
431 regnetz_c16 1104.72 231.72 256 320 3.92 25.88 13.46
432 resnetv2_152d 1101.83 232.328 256 224 11.8 23.36 60.2
433 gluon_resnet152_v1c 1092.12 234.395 256 224 11.8 23.36 60.21
434 repvgg_b2 1089.82 234.89 256 224 20.45 12.9 89.02
435 gluon_resnet152_v1d 1082.98 236.373 256 224 11.8 23.36 60.21
436 vgg19_bn 1079.7 237.091 256 224 19.66 14.86 143.68
437 densenet161 1075.45 238.026 256 224 7.79 11.06 28.68
438 xception41 1067.64 239.769 256 299 9.28 39.86 26.97
439 mixnet_xxl 1054.35 242.792 256 224 2.04 23.43 23.96
440 inception_v4 1054.27 242.81 256 299 12.28 15.09 42.68
441 vit_small_resnet50d_s16_224 1053.44 243.001 256 224 13.48 24.82 57.53
442 convmixer_1024_20_ks9_p14 1045.16 244.926 256 224 5.55 5.51 24.38
443 dla169 1043.58 245.295 256 224 11.6 20.2 53.39
444 nfnet_f0s 1043.56 245.302 256 256 12.62 18.05 71.49
445 xcit_tiny_12_p16_384_dist 1043.38 245.342 256 384 3.64 18.26 6.72
446 convnext_small 1038.48 246.501 256 224 8.7 21.56 50.22
447 nfnet_f0 1038.01 246.613 256 256 12.62 18.05 71.49
448 regnetx_120 1028.4 248.918 256 224 12.13 21.37 46.11
449 nest_tiny 1021.17 250.679 256 224 5.83 25.48 17.06
450 gluon_resnet152_v1s 1020.83 250.763 256 224 12.92 24.96 60.32
451 coat_lite_small 1016.77 251.763 256 224 3.96 22.09 19.84
452 legacy_seresnet152 1013.05 252.688 256 224 11.33 22.08 66.82
453 repvgg_b3g4 1008.62 253.799 256 224 17.89 15.1 83.83
454 jx_nest_tiny 1008.04 253.946 256 224 5.83 25.48 17.06
455 vit_base_patch16_224_miil 1007.66 254.043 256 224 17.58 23.9 86.54
456 crossvit_18_240 995.59 257.12 256 240 9.05 26.26 43.27
457 seresnet152 990.73 258.382 256 224 11.57 22.61 66.82
458 resnetv2_50d_evos 987.09 259.336 256 224 4.33 11.92 25.59
459 vit_base_patch16_224_sam 982.02 260.673 256 224 17.58 23.9 86.57
460 tresnet_xl 981.28 260.87 256 224 15.17 15.34 78.44
461 regnety_120 981.1 260.919 256 224 12.14 21.38 51.82
462 deit_base_patch16_224 980.91 260.97 256 224 17.58 23.9 86.57
463 vit_base_patch16_224 978.17 261.701 256 224 17.58 23.9 86.57
464 deit_base_distilled_patch16_224 975.08 262.528 256 224 17.68 24.05 87.34
465 crossvit_18_dagger_240 969.31 264.09 256 240 9.5 27.03 44.27
466 efficientnet_el 926.41 276.325 256 300 8.0 30.7 10.59
467 efficientnet_el_pruned 925.7 276.536 256 300 8.0 30.7 10.59
468 tf_efficientnet_el 907.79 281.986 256 300 8.0 30.7 10.59
469 dm_nfnet_f0 904.97 282.867 256 256 12.62 18.05 71.49
470 beit_base_patch16_224 904.15 283.123 256 224 17.58 23.9 86.53
471 twins_pcpvt_base 902.77 283.561 256 224 6.68 25.25 43.83
472 dla102x2 892.56 286.801 256 224 9.34 29.91 41.28
473 efficientnetv2_s 884.22 289.51 256 384 8.44 35.77 21.46
474 twins_svt_base 881.96 290.252 256 224 8.59 26.33 56.07
475 wide_resnet101_2 875.33 292.449 256 224 22.8 21.23 126.89
476 tf_efficientnetv2_s_in21ft1k 864.32 296.172 256 384 8.44 35.77 21.46
477 cait_xxs36_224 863.8 296.351 256 224 3.77 30.34 17.3
478 tf_efficientnetv2_s 863.76 296.365 256 384 8.44 35.77 21.46
479 resnetrs101 852.6 300.247 256 288 13.56 28.53 63.62
480 repvgg_b3 844.81 303.013 256 224 29.16 15.1 123.09
481 efficientnetv2_rw_s 841.56 304.183 256 384 8.72 38.03 23.94
482 dpn98 838.93 305.139 256 224 11.73 25.2 61.57
483 pit_b_distilled_224 837.59 305.628 256 224 12.5 33.07 74.79
484 pit_b_224 836.04 306.193 256 224 12.42 32.94 73.76
485 regnetx_160 833.81 307.013 256 224 15.99 25.52 54.28
486 xcit_small_24_p16_224_dist 833.22 307.225 256 224 9.1 23.64 47.67
487 xcit_small_24_p16_224 831.56 307.841 256 224 9.1 23.64 47.67
488 inception_resnet_v2 830.74 308.147 256 299 13.18 25.06 55.84
489 ens_adv_inception_resnet_v2 830.17 308.358 256 299 13.18 25.06 55.84
490 regnetz_d8 827.41 309.386 256 320 6.19 37.08 23.37
491 nf_regnet_b4 822.49 311.237 256 384 4.7 28.61 30.21
492 swin_small_patch4_window7_224 808.44 316.649 256 224 8.77 27.47 49.61
493 efficientnet_lite4 804.5 318.199 256 380 4.04 45.66 13.01
494 gluon_resnext101_64x4d 802.95 318.813 256 224 15.52 31.21 83.46
495 resnext101_64x4d 801.6 319.349 256 224 15.52 31.21 83.46
496 resnet200 795.01 321.996 256 224 15.07 32.19 64.67
497 xcit_tiny_12_p8_224_dist 792.9 322.851 256 224 4.81 23.6 6.71
498 xcit_tiny_12_p8_224 791.66 323.355 256 224 4.81 23.6 6.71
499 gluon_xception65 782.06 327.33 256 299 13.96 52.48 39.92
500 convnext_base_in22ft1k 778.5 328.824 256 224 15.38 28.75 88.59
501 convnext_base 777.8 329.119 256 224 15.38 28.75 88.59
502 xception65 776.49 329.678 256 299 13.96 52.48 39.92
503 ssl_resnext101_32x8d 773.95 330.757 256 224 16.48 31.21 88.79
504 swsl_resnext101_32x8d 773.24 331.062 256 224 16.48 31.21 88.79
505 resnext101_32x8d 772.71 331.288 256 224 16.48 31.21 88.79
506 ig_resnext101_32x8d 772.07 331.562 256 224 16.48 31.21 88.79
507 resnet101d 769.32 332.747 256 320 16.48 34.77 44.57
508 hrnet_w40 767.4 333.582 256 224 12.75 25.29 57.56
509 tf_efficientnet_lite4 762.16 335.871 256 380 4.04 45.66 13.01
510 resnest101e 757.06 338.138 256 256 13.38 28.66 48.28
511 gluon_seresnext101_64x4d 754.82 339.139 256 224 15.53 31.25 88.23
512 seresnext101_32x8d 729.3 351.006 256 224 16.48 31.25 93.57
513 cait_s24_224 717.85 356.607 256 224 9.35 40.58 46.92
514 hrnet_w48 714.5 358.278 256 224 17.34 28.56 77.47
515 hrnet_w44 713.57 358.746 256 224 14.94 26.92 67.06
516 tresnet_m_448 711.59 359.746 256 448 22.94 29.21 31.39
517 regnetz_d32 706.63 362.269 256 320 9.33 37.08 27.58
518 coat_tiny 706.1 362.539 256 224 4.35 27.2 5.5
519 nest_small 681.35 375.712 256 224 10.35 40.04 38.35
520 vit_large_r50_s32_224 679.9 376.512 256 224 19.58 24.41 328.99
521 jx_nest_small 675.59 378.914 256 224 10.35 40.04 38.35
522 twins_svt_large 672.39 380.718 256 224 15.15 35.1 99.27
523 crossvit_base_240 669.47 382.378 256 240 21.22 36.33 105.03
524 efficientnet_b4 667.37 383.583 256 384 4.51 50.04 19.34
525 twins_pcpvt_large 655.12 390.752 256 224 9.84 35.82 60.99
526 gmlp_b16_224 643.01 398.116 256 224 15.78 30.21 73.08
527 densenet264d_iabn 638.28 401.062 256 224 13.47 14.0 72.74
528 tf_efficientnet_b4 637.3 401.68 256 380 4.49 49.49 19.34
529 tf_efficientnet_b4_ap 636.89 401.944 256 380 4.49 49.49 19.34
530 tf_efficientnet_b4_ns 636.28 402.323 256 380 4.49 49.49 19.34
531 convit_base 621.54 411.866 256 224 17.52 31.77 86.54
532 densenet264 620.68 412.433 256 224 12.95 12.8 72.69
533 dpn131 617.53 414.539 256 224 16.09 32.97 79.25
534 swin_base_patch4_window7_224 616.27 415.39 256 224 15.47 36.63 87.77
535 xcit_medium_24_p16_224_dist 612.37 418.035 256 224 16.13 31.71 84.4
536 xcit_medium_24_p16_224 612.25 418.117 256 224 16.13 31.71 84.4
537 vit_small_patch16_384 593.83 431.086 256 384 15.52 50.78 22.2
538 coat_mini 591.16 433.03 256 224 6.82 33.68 10.34
539 xception71 588.93 434.671 256 299 18.09 69.92 42.34
540 vit_small_r26_s32_384 574.79 445.365 256 384 10.43 29.85 36.47
541 hrnet_w64 567.27 451.268 256 224 28.97 35.09 128.06
542 dpn107 564.61 453.398 256 224 18.38 33.46 86.92
543 eca_nfnet_l1 558.87 458.051 256 320 14.92 34.42 41.41
544 senet154 557.21 459.42 256 224 20.77 38.69 115.09
545 vit_base_r50_s16_224 556.92 459.655 256 224 21.66 35.29 98.66
546 gluon_senet154 556.63 459.899 256 224 20.77 38.69 115.09
547 legacy_senet154 554.67 461.525 256 224 20.77 38.69 115.09
548 xcit_tiny_24_p16_384_dist 554.63 461.554 256 384 6.87 34.29 12.12
549 xcit_small_12_p16_384_dist 546.42 468.488 256 384 14.14 36.51 26.25
550 resnet152d 540.83 473.331 256 320 24.08 47.67 60.21
551 seresnet200d 537.6 476.175 256 256 20.01 43.15 71.86
552 ecaresnet200d 537.5 476.265 256 256 20.0 43.15 64.69
553 regnety_320 520.96 491.382 256 224 32.34 30.26 145.05
554 nest_base 514.48 497.58 256 224 17.96 53.39 67.72
555 regnety_160 513.33 498.688 256 288 26.37 38.07 83.59
556 jx_nest_base 510.61 501.344 256 224 17.96 53.39 67.72
557 tnt_s_patch16_224 491.14 521.218 256 224 5.24 24.37 23.76
558 seresnet152d 484.32 528.565 256 320 24.09 47.72 66.84
559 resnetrs152 479.63 533.736 256 320 24.34 48.14 86.62
560 convnext_large 469.79 544.914 256 224 34.4 43.13 197.77
561 convnext_large_in22ft1k 468.89 545.96 256 224 34.4 43.13 197.77
562 regnetx_320 458.91 557.836 256 224 31.81 36.3 107.81
563 halonet_h1 456.22 561.121 256 256 3.0 51.17 8.1
564 vit_large_patch32_384 455.24 562.325 256 384 45.31 43.86 306.63
565 efficientnetv2_m 448.05 571.353 256 416 18.6 67.5 54.14
566 regnetz_e8 441.39 579.972 256 320 15.46 63.94 57.7
567 mixer_l16_224 428.45 597.494 256 224 44.6 41.69 208.2
568 seresnet269d 419.5 610.23 256 256 26.59 53.6 113.67
569 nf_regnet_b5 418.97 611.004 256 456 11.7 61.95 49.74
570 xcit_tiny_24_p8_224 417.22 613.573 256 224 9.21 45.39 12.11
571 xcit_tiny_24_p8_224_dist 416.09 615.239 256 224 9.21 45.39 12.11
572 xcit_small_12_p8_224 415.7 615.812 256 224 18.69 47.21 26.21
573 xcit_small_12_p8_224_dist 414.47 617.648 256 224 18.69 47.21 26.21
574 efficientnetv2_rw_m 401.01 638.372 256 416 21.49 79.62 53.24
575 resnet200d 390.11 656.216 256 320 31.25 67.33 64.69
576 resnetv2_50x1_bitm 388.79 658.439 256 448 16.62 44.46 25.55
577 tnt_b_patch16_224 387.0 661.483 256 224 14.09 39.01 65.41
578 xcit_nano_12_p8_384_dist 385.68 663.749 256 384 6.34 46.08 3.05
579 swin_large_patch4_window7_224 383.12 668.188 256 224 34.53 54.94 196.53
580 nfnet_f1s 376.13 680.6 256 320 35.97 46.77 132.63
581 nfnet_f1 374.75 683.103 256 320 35.97 46.77 132.63
582 xcit_large_24_p16_224 373.32 685.729 256 224 35.86 47.27 189.1
583 xcit_large_24_p16_224_dist 372.9 686.493 256 224 35.86 47.27 189.1
584 ssl_resnext101_32x16d 364.85 701.653 256 224 36.27 51.18 194.03
585 ig_resnext101_32x16d 364.28 702.733 256 224 36.27 51.18 194.03
586 swsl_resnext101_32x16d 364.28 702.739 256 224 36.27 51.18 194.03
587 tresnet_l_448 351.48 728.329 256 448 43.5 47.56 55.99
588 resnetrs200 347.71 736.231 256 320 31.51 67.81 93.21
589 tf_efficientnetv2_m 339.44 754.17 256 480 24.76 89.84 54.14
590 tf_efficientnetv2_m_in21ft1k 338.69 755.846 256 480 24.76 89.84 54.14
591 regnetz_d8_evob 337.57 758.355 256 320 6.12 37.08 23.41
592 efficientnet_b5 330.43 774.739 256 456 10.46 98.86 30.39
593 vit_large_patch16_224 328.99 778.119 256 224 61.6 63.52 304.33
594 dm_nfnet_f1 328.44 779.437 256 320 35.97 46.77 132.63
595 convnext_xlarge_in22ft1k 319.16 802.079 256 224 60.97 57.5 350.2
596 tf_efficientnet_b5 318.33 804.192 256 456 10.46 98.86 30.39
597 tf_efficientnet_b5_ap 317.92 805.231 256 456 10.46 98.86 30.39
598 tf_efficientnet_b5_ns 317.77 805.609 256 456 10.46 98.86 30.39
599 crossvit_15_dagger_408 307.3 833.049 256 408 21.45 95.05 28.5
600 beit_large_patch16_224 302.42 846.497 256 224 61.6 63.52 304.43
601 xcit_small_24_p16_384_dist 290.22 882.071 256 384 26.72 68.58 47.67
602 convmixer_768_32 283.01 904.552 256 224 19.55 25.95 21.11
603 eca_nfnet_l2 276.43 926.087 256 384 30.05 68.28 56.72
604 regnetz_d8_evos 272.53 939.333 256 320 7.03 38.92 23.46
605 resnetv2_152x2_bit_teacher 270.79 945.382 256 224 46.95 45.11 236.34
606 xcit_tiny_12_p8_384_dist 269.38 950.319 256 384 14.13 69.14 6.71
607 convnext_base_384_in22ft1k 268.35 953.955 256 384 45.2 84.49 88.59
608 tresnet_xl_448 264.06 969.471 256 448 60.65 61.31 78.44
609 deit_base_patch16_384 258.82 989.071 256 384 55.54 101.56 86.86
610 vit_base_patch16_384 258.72 989.47 256 384 55.54 101.56 86.86
611 resnest200e 257.74 993.222 256 320 35.69 82.78 70.2
612 deit_base_distilled_patch16_384 255.57 1001.681 256 384 55.65 101.82 87.63
613 resnetv2_101x1_bitm 242.52 1055.587 256 448 31.65 64.93 44.54
614 cait_xxs24_384 236.82 1080.978 256 384 9.63 122.66 12.03
615 crossvit_18_dagger_408 228.68 559.729 128 408 32.47 124.87 44.61
616 ecaresnet269d 228.16 1122.012 256 352 50.25 101.25 102.09
617 vit_large_r50_s32_384 228.16 1121.984 256 384 57.43 76.52 329.09
618 nasnetalarge 224.77 1138.915 256 331 23.89 90.56 88.75
619 pnasnet5large 224.27 1141.456 256 331 25.04 92.89 86.06
620 beit_base_patch16_384 222.85 1148.718 256 384 55.54 101.56 86.74
621 resnetrs270 221.37 1156.398 256 352 51.13 105.48 129.86
622 xcit_small_24_p8_224 217.0 1179.688 256 224 35.81 90.78 47.63
623 xcit_small_24_p8_224_dist 216.72 1181.234 256 224 35.81 90.78 47.63
624 nfnet_f2s 212.63 1203.934 256 352 63.22 79.06 193.78
625 nfnet_f2 211.38 1211.098 256 352 63.22 79.06 193.78
626 xcit_medium_24_p16_384_dist 210.86 1214.056 256 384 47.39 91.64 84.4
627 resmlp_big_24_224 198.74 1288.096 256 224 100.23 87.31 129.14
628 resmlp_big_24_224_in22ft1k 197.93 1293.386 256 224 100.23 87.31 129.14
629 resmlp_big_24_distilled_224 197.54 1295.901 256 224 100.23 87.31 129.14
630 efficientnetv2_l 196.77 1301.027 256 480 56.4 157.99 118.52
631 tf_efficientnetv2_l 194.85 1313.812 256 480 56.4 157.99 118.52
632 tf_efficientnetv2_l_in21ft1k 194.51 1316.142 256 480 56.4 157.99 118.52
633 efficientnet_b6 189.94 673.894 128 528 19.4 167.39 43.04
634 dm_nfnet_f2 186.3 1374.105 256 352 63.22 79.06 193.78
635 tf_efficientnet_b6_ns 183.84 696.254 128 528 19.4 167.39 43.04
636 tf_efficientnet_b6_ap 183.67 696.899 128 528 19.4 167.39 43.04
637 tf_efficientnet_b6 183.44 697.757 128 528 19.4 167.39 43.04
638 swin_base_patch4_window12_384 171.35 746.981 128 384 47.19 134.78 87.9
639 vit_base_patch8_224 170.3 1503.253 256 224 78.22 161.69 86.58
640 cait_xs24_384 168.32 1520.881 256 384 19.28 183.98 26.67
641 vit_base_r50_s16_384 163.47 1566.011 256 384 67.43 135.03 98.95
642 vit_base_resnet50_384 163.26 1568.003 256 384 67.43 135.03 98.95
643 convmixer_1536_20 162.04 1579.822 256 224 48.68 33.03 51.63
644 convnext_large_384_in22ft1k 160.02 1599.775 256 384 101.09 126.74 197.77
645 cait_xxs36_384 158.26 1617.553 256 384 14.35 183.7 17.37
646 xcit_medium_24_p8_224 157.17 1628.769 256 224 63.53 121.23 84.32
647 xcit_medium_24_p8_224_dist 157.13 1629.205 256 224 63.53 121.23 84.32
648 eca_nfnet_l3 156.17 1639.21 256 448 52.55 118.4 72.04
649 resnetrs350 147.0 1741.501 256 384 77.59 154.74 163.96
650 ig_resnext101_32x32d 144.49 1771.686 256 224 87.29 91.12 468.53
651 xcit_tiny_24_p8_384_dist 141.04 1815.079 256 384 27.05 132.95 12.11
652 xcit_small_12_p8_384_dist 137.26 1865.112 256 384 54.92 138.29 26.21
653 vit_huge_patch14_224 131.97 1939.888 256 224 167.4 139.41 632.05
654 cait_s24_384 129.91 1970.595 256 384 32.17 245.31 47.06
655 xcit_large_24_p16_384_dist 127.3 2011.004 256 384 105.35 137.17 189.1
656 efficientnetv2_xl 125.14 2045.696 256 512 93.85 247.32 208.12
657 tf_efficientnetv2_xl_in21ft1k 124.18 2061.565 256 512 93.85 247.32 208.12
658 resnest269e 119.71 2138.483 256 416 77.69 171.98 110.93
659 nfnet_f3s 116.49 2197.633 256 416 115.58 141.78 254.92
660 nfnet_f3 115.68 2213.012 256 416 115.58 141.78 254.92
661 convnext_xlarge_384_in22ft1k 109.41 1169.883 128 384 179.18 168.99 350.2
662 efficientnet_b7 108.34 590.697 64 600 38.33 289.94 66.35
663 swin_large_patch4_window12_384 107.91 1186.161 128 384 104.08 202.16 196.74
664 resnetrs420 106.92 2394.315 256 416 108.45 213.79 191.89
665 tf_efficientnet_b7_ap 105.47 606.819 64 600 38.33 289.94 66.35
666 tf_efficientnet_b7_ns 105.47 606.797 64 600 38.33 289.94 66.35
667 tf_efficientnet_b7 105.43 607.002 64 600 38.33 289.94 66.35
668 dm_nfnet_f3 102.2 2504.865 256 416 115.58 141.78 254.92
669 xcit_large_24_p8_224_dist 94.99 2695.138 256 224 141.23 181.56 188.93
670 xcit_large_24_p8_224 94.97 2695.444 256 224 141.23 181.56 188.93
671 resnetv2_152x2_bit_teacher_384 94.51 2708.696 256 384 136.16 132.56 236.34
672 resnetv2_50x3_bitm 93.18 1373.716 128 448 145.7 133.37 217.32
673 vit_large_patch16_384 88.86 2880.826 256 384 191.21 270.24 304.72
674 vit_giant_patch14_224 87.24 2934.557 256 224 267.18 192.64 1012.61
675 cait_s36_384 87.06 2940.456 256 384 47.99 367.4 68.37
676 ig_resnext101_32x48d 85.45 1497.919 128 224 153.57 131.06 828.41
677 beit_large_patch16_384 77.25 3314.013 256 384 191.21 270.24 305.0
678 xcit_small_24_p8_384_dist 72.02 3554.648 256 384 105.24 265.91 47.63
679 resnetv2_152x2_bitm 69.3 1847.055 128 448 184.99 180.43 236.34
680 efficientnet_b8 67.89 942.729 64 672 63.48 442.89 87.41
681 tf_efficientnet_b8 66.48 962.617 64 672 63.48 442.89 87.41
682 tf_efficientnet_b8_ap 66.47 962.833 64 672 63.48 442.89 87.41
683 nfnet_f4s 65.05 3935.454 256 512 216.26 262.26 316.07
684 nfnet_f4 64.4 3975.384 256 512 216.26 262.26 316.07
685 resnetv2_101x3_bitm 57.01 2245.355 128 448 280.33 194.78 387.93
686 dm_nfnet_f4 56.77 4509.206 256 512 216.26 262.26 316.07
687 xcit_medium_24_p8_384_dist 53.32 4800.797 256 384 186.67 354.73 84.32
688 vit_gigantic_patch14_224 53.03 4827.242 256 224 483.95 275.37 1844.44
689 nfnet_f5s 47.06 5439.402 256 544 290.97 349.71 377.21
690 nfnet_f5 46.62 5491.767 256 544 290.97 349.71 377.21
691 dm_nfnet_f5 41.31 6196.83 256 544 290.97 349.71 377.21
692 tf_efficientnet_l2_ns_475 38.18 1676.113 64 475 172.11 609.89 480.31
693 nfnet_f6s 33.85 7563.356 256 576 378.69 452.2 438.36
694 nfnet_f6 33.53 7634.937 256 576 378.69 452.2 438.36
695 xcit_large_24_p8_384_dist 32.23 7943.828 256 384 415.0 531.82 188.93
696 beit_large_patch16_512 31.3 2044.665 64 512 362.24 656.39 305.67
697 cait_m36_384 30.09 8507.481 256 384 173.11 734.81 271.22
698 dm_nfnet_f6 30.02 8526.734 256 576 378.69 452.2 438.36
699 nfnet_f7s 26.4 9698.058 256 608 480.39 570.85 499.5
700 nfnet_f7 26.15 9791.252 256 608 480.39 570.85 499.5
701 resnetv2_152x4_bitm 18.07 3541.734 64 480 844.84 414.26 936.53
702 efficientnet_l2 13.68 2266.184 31 800 479.12 1707.39 480.31
703 tf_efficientnet_l2_ns 13.51 2295.302 31 800 479.12 1707.39 480.31
704 cait_m48_448 13.05 9810.688 128 448 329.41 1708.23 356.46

@ -0,0 +1,696 @@
model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,52443.33,4.87,256,106,0.03,0.69,2.04
lcnet_035,41836.65,6.108,256,224,0.03,1.04,1.64
mobilenetv3_small_050,40675.32,6.283,256,224,0.03,0.92,1.59
lcnet_050,37031.22,6.902,256,224,0.05,1.26,1.88
mobilenetv3_small_075,33134.36,7.715,256,224,0.05,1.3,2.04
mobilenetv3_small_100,30332.75,8.428,256,224,0.06,1.42,2.54
tinynet_d,29729.64,8.599,256,152,0.05,1.42,2.34
tf_mobilenetv3_small_minimal_100,28698.97,8.909,256,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,27515.61,9.292,256,224,0.05,1.3,2.04
tf_mobilenetv3_small_100,25529.73,10.016,256,224,0.06,1.42,2.54
lcnet_075,25351.21,10.087,256,224,0.1,1.99,2.36
lcnet_100,21251.26,12.035,256,224,0.16,2.52,2.95
mnasnet_small,20610.12,12.41,256,224,0.07,2.16,2.03
levit_128s,18679.12,13.694,256,224,0.31,1.88,7.78
mobilenetv2_035,18422.82,13.885,256,224,0.07,2.86,1.68
ghostnet_050,17065.19,14.99,256,224,0.05,1.77,2.59
regnetx_002,17008.51,15.04,256,224,0.2,2.16,2.68
mnasnet_050,16943.72,15.098,256,224,0.11,3.07,2.22
tinynet_c,16089.67,15.9,256,184,0.11,2.87,2.46
mobilenetv2_050,15226.35,16.802,256,224,0.1,3.64,1.97
regnety_002,15182.41,16.851,256,224,0.2,2.17,3.16
semnasnet_050,14956.97,17.104,256,224,0.11,3.44,2.08
lcnet_150,14301.96,17.889,256,224,0.34,3.79,4.5
regnetx_004,13656.57,18.735,256,224,0.4,3.14,5.16
gernet_s,13307.64,19.226,256,224,0.75,2.65,8.17
mobilenetv3_large_075,12875.59,19.872,256,224,0.16,4.0,3.99
levit_128,12869.05,19.881,256,224,0.41,2.71,9.21
mnasnet_075,11851.07,21.591,256,224,0.23,4.77,3.17
mobilenetv3_rw,11749.18,21.778,256,224,0.23,4.41,5.48
hardcorenas_a,11724.1,21.825,256,224,0.23,4.38,5.26
mobilenetv3_large_100,11546.8,22.16,256,224,0.23,4.41,5.48
mobilenetv3_large_100_miil,11527.85,22.196,256,224,0.23,4.41,5.48
levit_192,11521.32,22.208,256,224,0.66,3.2,10.95
tf_mobilenetv3_large_075,11097.44,23.056,256,224,0.16,4.0,3.99
ese_vovnet19b_slim_dw,11011.34,23.237,256,224,0.4,5.28,1.9
tf_mobilenetv3_large_minimal_100,10807.48,23.676,256,224,0.22,4.4,3.92
tinynet_b,10794.3,23.705,256,188,0.21,4.44,3.73
hardcorenas_b,10649.59,24.027,256,224,0.26,5.09,5.18
hardcorenas_c,10616.11,24.103,256,224,0.28,5.01,5.52
mnasnet_100,10429.97,24.534,256,224,0.33,5.46,4.38
mnasnet_b1,10405.42,24.591,256,224,0.33,5.46,4.38
mixer_s32_224,10314.02,24.809,256,224,1.0,2.28,19.1
ssl_resnet18,10228.31,25.016,256,224,1.82,2.48,11.69
gluon_resnet18_v1b,10218.86,25.04,256,224,1.82,2.48,11.69
swsl_resnet18,10183.71,25.126,256,224,1.82,2.48,11.69
resnet18,10180.53,25.135,256,224,1.82,2.48,11.69
tf_mobilenetv3_large_100,10049.81,25.462,256,224,0.23,4.41,5.48
ghostnet_100,9984.48,25.628,256,224,0.15,3.55,5.18
mobilenetv2_075,9980.04,25.64,256,224,0.22,5.86,2.64
semnasnet_075,9964.54,25.68,256,224,0.23,5.54,2.91
hardcorenas_d,9736.47,26.282,256,224,0.3,4.93,7.5
seresnet18,9448.37,27.084,256,224,1.82,2.49,11.78
spnasnet_100,9378.19,27.285,256,224,0.35,6.03,4.42
vit_small_patch32_224,9330.47,27.426,256,224,1.15,2.5,22.88
regnety_006,9290.2,27.545,256,224,0.61,4.33,6.06
legacy_seresnet18,9195.77,27.828,256,224,1.82,2.49,11.78
mobilenetv2_100,8931.8,28.65,256,224,0.31,6.68,3.5
regnety_004,8913.44,28.71,256,224,0.41,3.89,4.34
semnasnet_100,8878.3,28.823,256,224,0.32,6.23,3.89
mnasnet_a1,8872.5,28.841,256,224,0.32,6.23,3.89
hardcorenas_f,8746.45,29.258,256,224,0.35,5.57,8.2
tinynet_a,8705.83,29.394,256,192,0.35,5.41,6.19
efficientnet_lite0,8678.08,29.489,256,224,0.4,6.74,4.65
levit_256,8605.17,29.738,256,224,1.13,4.23,18.89
fbnetc_100,8589.17,29.794,256,224,0.4,6.51,5.57
hardcorenas_e,8587.1,29.801,256,224,0.35,5.65,8.07
resnet18d,8454.18,30.27,256,224,2.06,3.29,11.71
tf_efficientnetv2_b0,8451.14,30.28,256,224,0.73,4.77,7.14
ese_vovnet19b_slim,8409.91,30.429,256,224,1.69,3.52,3.17
regnetx_008,8190.92,31.242,256,224,0.81,5.15,7.26
vit_tiny_r_s16_p8_224,8188.54,31.252,256,224,0.44,2.06,6.34
regnetx_006,7654.81,33.432,256,224,0.61,3.98,6.2
ghostnet_130,7516.11,34.049,256,224,0.24,4.6,7.36
tf_efficientnet_lite0,7513.34,34.061,256,224,0.4,6.74,4.65
regnety_008,7334.93,34.89,256,224,0.81,5.25,6.26
mnasnet_140,7212.43,35.483,256,224,0.6,7.71,7.12
efficientnet_b0,7189.76,35.595,256,224,0.4,6.75,5.29
rexnetr_100,6816.78,37.543,256,224,0.43,7.72,4.88
mobilenetv2_110d,6785.21,37.718,256,224,0.45,8.71,4.52
tf_efficientnet_b0_ns,6397.82,40.002,256,224,0.4,6.75,5.29
tf_efficientnet_b0_ap,6366.01,40.201,256,224,0.4,6.75,5.29
tf_efficientnet_b0,6345.74,40.33,256,224,0.4,6.75,5.29
hrnet_w18_small,6240.1,41.014,256,224,1.61,5.72,13.19
semnasnet_140,6194.36,41.317,256,224,0.6,8.87,6.11
ese_vovnet19b_dw,6016.74,42.537,256,224,1.34,8.25,6.54
mobilenetv2_140,6010.52,42.581,256,224,0.6,9.57,6.11
resnet34,5949.09,43.021,256,224,3.67,3.74,21.8
gluon_resnet34_v1b,5944.0,43.057,256,224,3.67,3.74,21.8
tv_resnet34,5920.52,43.227,256,224,3.67,3.74,21.8
gernet_m,5909.18,43.311,256,224,3.02,5.24,21.14
efficientnet_lite1,5815.72,44.007,256,240,0.62,10.14,5.42
selecsls42,5752.79,44.489,256,224,2.94,4.62,30.35
selecsls42b,5752.33,44.492,256,224,2.98,4.62,32.46
fbnetv3_b,5567.33,45.971,256,256,0.55,9.1,8.6
efficientnet_b1_pruned,5558.39,46.045,256,240,0.4,6.21,6.33
resnet26,5532.69,46.26,256,224,2.36,7.35,16.0
skresnet18,5529.57,46.284,256,224,1.82,3.24,11.96
efficientnet_es_pruned,5519.91,46.366,256,224,1.81,8.73,5.44
efficientnet_es,5518.49,46.378,256,224,1.81,8.73,5.44
tf_efficientnetv2_b1,5515.51,46.402,256,240,1.21,7.34,8.14
rexnet_100,5500.83,46.527,256,224,0.41,7.44,4.8
dla46_c,5464.03,46.84,256,224,0.58,4.5,1.3
seresnet34,5439.78,47.05,256,224,3.67,3.74,21.96
resnet34d,5325.4,48.06,256,224,3.91,4.54,21.82
resnetblur18,5301.3,48.279,256,224,2.34,3.39,11.69
legacy_seresnet34,5280.08,48.473,256,224,3.67,3.74,21.96
rexnetr_130,5239.92,48.845,256,224,0.68,9.81,7.61
tf_efficientnet_es,5226.26,48.971,256,224,1.81,8.73,5.44
tf_efficientnet_lite1,5192.67,49.288,256,240,0.62,10.14,5.42
levit_384,5136.88,49.824,256,224,2.36,6.26,39.13
nf_regnet_b0,5081.73,50.365,256,256,0.64,5.58,8.76
selecsls60,5015.37,51.032,256,224,3.59,5.52,30.67
selecsls60b,5008.51,51.102,256,224,3.63,5.52,32.77
mobilenetv2_120d,5006.42,51.123,256,224,0.69,11.97,5.83
repvgg_b0,4831.81,52.969,256,224,3.41,6.15,15.82
resnet26d,4774.01,53.612,256,224,2.6,8.15,16.01
rexnetr_150,4621.39,55.383,256,224,0.89,11.13,9.78
fbnetv3_d,4543.95,56.327,256,256,0.68,11.1,10.31
nf_resnet26,4522.01,56.601,256,224,2.41,7.35,16.0
deit_tiny_patch16_224,4516.94,56.664,256,224,1.26,5.97,5.72
visformer_tiny,4514.36,56.695,256,224,1.27,5.72,10.32
vit_tiny_patch16_224,4509.39,56.76,256,224,1.26,5.97,5.72
efficientnet_lite2,4476.87,57.171,256,260,0.89,12.9,6.09
xcit_nano_12_p16_224_dist,4461.13,57.373,256,224,0.56,4.17,3.05
deit_tiny_distilled_patch16_224,4450.37,57.513,256,224,1.27,6.01,5.91
xcit_nano_12_p16_224,4443.48,57.6,256,224,0.56,4.17,3.05
pit_ti_distilled_224,4267.82,59.972,256,224,0.71,6.23,5.1
pit_ti_224,4256.93,60.125,256,224,0.7,6.19,4.85
resmlp_12_distilled_224,4163.6,61.475,256,224,3.01,5.5,15.35
resmlp_12_224,4160.41,61.521,256,224,3.01,5.5,15.35
gernet_l,4096.96,62.474,256,256,4.57,8.0,31.08
tf_efficientnetv2_b2,4081.75,62.706,256,260,1.72,9.84,10.1
legacy_seresnext26_32x4d,4060.33,63.037,256,224,2.49,9.39,16.79
tf_efficientnet_b1,4046.06,63.259,256,240,0.71,10.88,7.79
tf_efficientnet_b1_ap,4044.6,63.282,256,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,4042.64,63.313,256,240,0.71,10.88,7.79
tf_efficientnet_lite2,4027.76,63.547,256,260,0.89,12.9,6.09
resnext26ts,3956.89,64.685,256,256,2.43,10.52,10.3
dla46x_c,3913.74,65.398,256,224,0.54,5.66,1.07
efficientnet_b1,3903.13,65.577,256,256,0.77,12.22,7.79
dla34,3901.31,65.607,256,224,3.07,5.02,15.74
mixer_b32_224,3872.36,66.099,256,224,3.24,6.29,60.29
rexnet_130,3847.96,66.517,256,224,0.68,9.71,7.56
vit_base_patch32_224_sam,3843.13,66.599,256,224,4.41,5.01,88.22
nf_seresnet26,3833.86,66.763,256,224,2.41,7.36,17.4
eca_resnext26ts,3823.14,66.95,256,256,2.43,10.52,10.3
vit_base_patch32_224,3821.26,66.98,256,224,4.41,5.01,88.22
nf_ecaresnet26,3821.0,66.987,256,224,2.41,7.36,16.0
seresnext26ts,3814.04,67.108,256,256,2.43,10.52,10.39
dla60x_c,3762.66,68.025,256,224,0.59,6.01,1.32
efficientnet_b2_pruned,3734.36,68.542,256,260,0.73,9.13,8.31
regnetx_016,3682.98,69.498,256,224,1.62,7.93,9.19
resnet26t,3677.69,69.597,256,256,3.35,10.52,16.01
gcresnext26ts,3645.26,70.216,256,256,2.43,10.53,10.48
rexnet_150,3639.9,70.32,256,224,0.9,11.21,9.73
seresnext26tn_32x4d,3635.04,70.413,256,224,2.7,10.09,16.81
seresnext26t_32x4d,3629.32,70.524,256,224,2.7,10.09,16.81
ecaresnext50t_32x4d,3625.17,70.606,256,224,2.7,10.09,15.41
seresnext26d_32x4d,3620.48,70.696,256,224,2.73,10.19,16.81
ecaresnet50d_pruned,3618.55,70.735,256,224,2.53,6.43,19.94
ecaresnext26t_32x4d,3617.96,70.747,256,224,2.7,10.09,15.41
resnetv2_50,3573.69,71.622,256,224,4.11,11.11,25.55
eca_botnext26ts_256,3538.36,72.339,256,256,2.46,11.6,10.59
repvgg_a2,3525.89,72.594,256,224,5.7,6.26,28.21
pit_xs_224,3490.24,73.336,256,224,1.4,7.71,10.62
mixer_s16_224,3485.97,73.426,256,224,3.79,5.97,18.53
pit_xs_distilled_224,3470.1,73.761,256,224,1.41,7.76,11.0
eca_halonext26ts,3433.36,74.552,256,256,2.44,11.46,10.76
gluon_resnet50_v1b,3424.93,74.734,256,224,4.11,11.11,25.56
rexnetr_200,3422.25,74.791,256,224,1.59,15.11,16.52
ssl_resnet50,3418.93,74.864,256,224,4.11,11.11,25.56
resnet50,3416.87,74.911,256,224,4.11,11.11,25.56
tv_resnet50,3416.58,74.915,256,224,4.11,11.11,25.56
swsl_resnet50,3415.95,74.931,256,224,4.11,11.11,25.56
ecaresnetlight,3410.59,75.049,256,224,4.11,8.42,30.16
efficientnet_em,3345.86,76.5,256,240,3.04,14.34,6.9
dpn68b,3321.6,77.058,256,224,2.35,10.47,12.61
dpn68,3321.37,77.064,256,224,2.35,10.47,12.61
resnet32ts,3318.56,77.131,256,256,4.63,11.58,17.96
botnet26t_256,3309.73,77.335,256,256,3.32,11.98,12.49
resnet33ts,3269.93,78.278,256,256,4.76,11.66,19.68
resnetv2_50t,3252.33,78.701,256,224,4.32,11.82,25.57
halonet26t,3247.75,78.812,256,256,3.19,11.69,12.48
resnetv2_50d,3238.36,79.041,256,224,4.35,11.92,25.57
gluon_resnet50_v1c,3234.34,79.139,256,224,4.35,11.92,25.58
nf_regnet_b1,3220.71,79.473,256,288,1.02,9.2,10.22
tf_efficientnet_em,3211.19,79.71,256,240,3.04,14.34,6.9
nf_regnet_b2,3198.33,80.03,256,272,1.22,9.27,14.31
xcit_tiny_12_p16_224_dist,3183.42,80.405,256,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,3177.02,80.567,256,224,1.24,6.29,6.72
regnety_016,3168.83,80.775,256,224,1.63,8.04,11.2
gmixer_12_224,3164.43,80.888,256,224,2.67,7.26,12.7
tf_efficientnet_b2_ap,3144.11,81.41,256,260,1.02,13.83,9.11
tf_efficientnet_b2,3133.02,81.698,256,260,1.02,13.83,9.11
eca_resnet33ts,3132.75,81.706,256,256,4.76,11.66,19.68
seresnet33ts,3132.5,81.713,256,256,4.76,11.66,19.78
skresnet34,3122.38,81.975,256,224,3.67,5.13,22.28
resnet50t,3114.92,82.173,256,224,4.32,11.82,25.57
tf_efficientnet_b2_ns,3112.24,82.244,256,260,1.02,13.83,9.11
gluon_resnet50_v1d,3111.66,82.259,256,224,4.35,11.92,25.58
resnet50d,3107.36,82.374,256,224,4.35,11.92,25.58
vovnet39a,3097.06,82.648,256,224,7.09,6.73,22.6
bat_resnext26ts,3052.18,83.861,256,256,2.53,12.51,10.73
legacy_seresnet50,3043.79,84.094,256,224,3.88,10.6,28.09
cspresnext50,3003.53,85.222,256,224,3.1,12.14,20.57
gcresnet33ts,2986.89,85.696,256,256,4.76,11.68,19.88
selecsls84,2981.3,85.857,256,224,5.9,7.57,50.95
vit_small_patch32_384,2981.2,85.86,256,384,3.45,8.25,22.92
ese_vovnet39b,2975.03,86.038,256,224,7.09,6.74,24.57
efficientnet_b2a,2964.53,86.343,256,288,1.12,16.2,9.11
efficientnet_b2,2962.37,86.406,256,288,1.12,16.2,9.11
eca_vovnet39b,2962.13,86.413,256,224,7.09,6.74,22.6
res2net50_48w_2s,2946.94,86.858,256,224,4.18,11.72,25.29
seresnet50,2942.22,86.998,256,224,4.11,11.13,28.09
efficientnet_b3_pruned,2917.98,87.72,256,300,1.04,11.86,9.86
haloregnetz_b,2882.69,88.795,256,224,1.97,11.94,11.68
vit_small_resnet26d_224,2880.85,88.851,256,224,5.07,11.12,63.61
vgg11,2809.5,91.106,256,224,7.61,7.44,132.86
gluon_resnext50_32x4d,2797.04,91.514,256,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2795.88,91.552,256,224,4.26,14.4,25.03
tv_resnext50_32x4d,2795.77,91.554,256,224,4.26,14.4,25.03
mixnet_s,2795.45,91.566,256,224,0.25,6.25,4.13
ssl_resnext50_32x4d,2795.35,91.567,256,224,4.26,14.4,25.03
resnext50_32x4d,2795.04,91.578,256,224,4.26,14.4,25.03
resnetaa50d,2786.19,91.87,256,224,5.39,12.44,25.58
ecaresnet101d_pruned,2770.2,92.4,256,224,3.48,7.69,24.88
densenet121,2765.59,92.554,256,224,2.87,6.9,7.98
tv_densenet121,2754.97,92.91,256,224,2.87,6.9,7.98
gluon_resnet50_v1s,2746.47,93.198,256,224,5.47,13.52,25.68
rexnet_200,2741.96,93.353,256,224,1.56,14.91,16.37
cspresnet50,2739.95,93.42,256,256,4.54,11.5,21.62
seresnet50t,2715.96,94.246,256,224,4.32,11.83,28.1
resnetrs50,2709.47,94.471,256,224,4.48,12.14,35.69
crossvit_tiny_240,2698.8,94.845,256,240,1.57,9.08,7.01
ecaresnet50d,2695.97,94.946,256,224,4.35,11.93,25.58
tf_mixnet_s,2668.56,95.92,256,224,0.25,6.25,4.13
dla60,2658.15,96.295,256,224,4.26,10.16,22.04
crossvit_9_240,2646.19,96.732,256,240,1.85,9.52,8.55
densenet121d,2643.5,96.83,256,224,3.11,7.7,8.0
efficientnet_lite3,2630.67,97.302,256,300,1.65,21.85,8.2
convit_tiny,2612.77,97.969,256,224,1.26,7.94,5.71
hrnet_w18_small_v2,2612.23,97.989,256,224,2.62,9.65,15.6
crossvit_9_dagger_240,2607.67,98.16,256,240,1.99,9.97,8.78
regnetz_b16,2592.86,98.722,256,288,2.39,16.43,9.72
resnext50d_32x4d,2582.77,99.107,256,224,4.5,15.2,25.05
cspresnet50d,2577.78,99.299,256,256,4.86,12.55,21.64
cspresnet50w,2560.3,99.977,256,256,5.04,12.19,28.12
vgg11_bn,2537.64,100.869,256,224,7.62,7.44,132.87
vovnet57a,2533.31,101.041,256,224,8.95,7.52,36.64
resnetblur50,2528.53,101.233,256,224,5.16,12.02,25.56
gmlp_ti16_224,2479.86,103.22,256,224,1.34,7.55,5.87
seresnext50_32x4d,2470.29,103.618,256,224,4.26,14.42,27.56
legacy_seresnext50_32x4d,2466.47,103.78,256,224,4.26,14.42,27.56
seresnetaa50d,2460.49,104.031,256,224,5.4,12.46,28.11
gluon_seresnext50_32x4d,2457.31,104.168,256,224,4.26,14.42,27.56
fbnetv3_g,2441.71,104.833,256,288,1.77,21.09,16.62
res2net50_26w_4s,2439.77,104.916,256,224,4.28,12.61,25.7
ese_vovnet57b,2429.1,105.377,256,224,8.95,7.52,38.61
vit_tiny_r_s16_p8_384,2420.04,105.772,256,384,1.34,6.49,6.36
gcresnet50t,2406.95,106.347,256,256,5.42,14.67,25.9
adv_inception_v3,2387.53,107.209,256,299,5.73,8.97,23.83
inception_v3,2378.4,107.624,256,299,5.73,8.97,23.83
gluon_inception_v3,2378.14,107.635,256,299,5.73,8.97,23.83
dla60x,2376.48,107.71,256,224,3.54,13.8,17.35
efficientnetv2_rw_t,2371.59,107.933,256,288,3.19,16.42,13.65
tf_inception_v3,2369.67,108.018,256,299,5.73,8.97,23.83
densenetblur121d,2366.44,108.168,256,224,3.11,7.9,8.0
tf_efficientnet_lite3,2359.22,108.498,256,300,1.65,21.85,8.2
resnetblur50d,2356.51,108.624,256,224,5.4,12.82,25.58
nf_seresnet50,2321.05,110.283,256,224,4.21,11.13,28.09
lambda_resnet26rpt_256,2319.42,110.361,256,256,3.16,11.87,10.99
tf_efficientnetv2_b3,2313.61,110.636,256,300,3.04,15.74,14.36
nf_ecaresnet50,2312.19,110.706,256,224,4.21,11.13,25.56
resnest14d,2311.92,110.718,256,224,2.76,7.33,10.61
deit_small_patch16_224,2305.42,111.031,256,224,4.61,11.95,22.05
pit_s_224,2300.71,111.258,256,224,2.88,11.56,23.46
vit_small_patch16_224,2295.85,111.494,256,224,4.61,11.95,22.05
pit_s_distilled_224,2281.8,112.18,256,224,2.9,11.64,24.04
densenet169,2278.65,112.335,256,224,3.4,7.3,14.15
vit_base_resnet26d_224,2268.07,112.857,256,224,6.97,13.16,101.4
deit_small_distilled_patch16_224,2267.44,112.892,256,224,4.63,12.02,22.44
skresnet50,2244.08,114.064,256,224,4.11,12.5,25.8
darknet53,2213.23,115.656,256,256,9.31,12.39,41.61
sehalonet33ts,2208.53,115.902,256,256,3.55,14.7,13.69
res2net50_14w_8s,2201.46,116.274,256,224,4.21,13.28,25.06
gc_efficientnetv2_rw_t,2198.9,116.41,256,288,3.2,16.45,13.68
gcresnext50ts,2187.09,117.039,256,256,3.75,15.46,15.67
resnetv2_101,2146.17,119.27,256,224,7.83,16.23,44.54
resmlp_24_224,2127.24,120.334,256,224,5.96,10.91,30.02
resmlp_24_distilled_224,2125.15,120.451,256,224,5.96,10.91,30.02
skresnet50d,2105.03,121.599,256,224,4.36,13.31,25.82
gluon_resnet101_v1b,2098.13,122.001,256,224,7.83,16.23,44.55
tv_resnet101,2095.51,122.152,256,224,7.83,16.23,44.55
resnet101,2094.89,122.19,256,224,7.83,16.23,44.55
res2next50,2089.9,122.482,256,224,4.2,13.71,24.67
ecaresnet26t,2087.45,122.626,256,320,5.24,16.44,16.01
dla60_res2net,2078.29,123.165,256,224,4.15,12.34,20.85
nf_regnet_b3,2039.85,125.487,256,320,2.05,14.61,18.59
gluon_resnet101_v1c,2026.41,126.32,256,224,8.08,17.04,44.57
resnetv2_101d,2021.62,126.619,256,224,8.07,17.04,44.56
gluon_resnet101_v1d,1976.83,129.488,256,224,8.08,17.04,44.57
vgg13,1972.22,129.79,256,224,11.31,12.25,133.05
wide_resnet50_2,1932.56,132.455,256,224,11.43,14.4,68.88
sebotnet33ts_256,1892.63,135.25,256,256,3.89,17.46,13.7
repvgg_b1,1864.64,137.281,256,224,13.16,10.64,57.42
resnetaa101d,1840.38,139.09,256,224,9.12,17.56,44.57
legacy_seresnet101,1838.54,139.229,256,224,7.61,15.74,49.33
resnet51q,1828.83,139.968,256,288,8.07,20.94,35.7
gluon_resnet101_v1s,1822.02,140.491,256,224,9.19,18.64,44.67
dla102,1820.91,140.577,256,224,7.19,14.18,33.27
dla60_res2next,1817.78,140.818,256,224,3.49,13.17,17.03
coat_lite_tiny,1811.49,141.309,256,224,1.6,11.65,5.72
vit_base_resnet50d_224,1807.55,141.614,256,224,8.73,16.92,110.97
tf_efficientnet_b3,1803.3,141.948,256,300,1.87,23.83,12.23
tf_efficientnet_b3_ap,1802.94,141.978,256,300,1.87,23.83,12.23
tf_efficientnet_b3_ns,1802.1,142.043,256,300,1.87,23.83,12.23
cspdarknet53,1801.31,142.107,256,256,6.57,16.81,27.64
seresnet101,1796.92,142.453,256,224,7.84,16.27,49.33
efficientnet_b3,1780.99,143.727,256,320,2.01,26.52,12.23
efficientnet_b3a,1780.57,143.761,256,320,2.01,26.52,12.23
densenet201,1754.6,145.89,256,224,4.34,7.85,20.01
vgg13_bn,1750.09,146.264,256,224,11.33,12.25,133.05
ssl_resnext101_32x4d,1737.01,147.366,256,224,8.01,21.23,44.18
resnext101_32x4d,1736.55,147.407,256,224,8.01,21.23,44.18
gluon_resnext101_32x4d,1736.42,147.419,256,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1735.28,147.514,256,224,8.01,21.23,44.18
halonet50ts,1731.56,147.831,256,256,5.3,19.2,22.73
res2net50_26w_6s,1726.13,148.297,256,224,6.33,15.28,37.05
nf_resnet101,1725.55,148.347,256,224,8.01,16.23,44.55
coat_lite_mini,1724.07,148.474,256,224,2.0,12.25,11.01
regnetz_c16,1702.47,150.358,256,320,3.92,25.88,13.46
nf_resnet50,1701.7,150.426,256,288,6.88,18.37,25.56
ecaresnet101d,1699.81,150.594,256,224,8.08,17.07,44.57
xcit_tiny_24_p16_224_dist,1697.25,150.82,256,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,1694.56,151.06,256,224,2.34,11.82,12.12
xcit_small_12_p16_224_dist,1690.19,151.45,256,224,4.82,12.58,26.25
xcit_small_12_p16_224,1687.09,151.729,256,224,4.82,12.58,26.25
resnest26d,1686.65,151.769,256,224,3.64,9.97,17.07
convnext_tiny,1676.02,152.729,256,224,4.47,13.44,28.59
convnext_tiny_hnf,1674.55,152.865,256,224,4.47,13.44,28.59
vit_small_r26_s32_224,1656.11,154.567,256,224,3.56,9.85,36.43
resnetblur101d,1639.84,156.101,256,224,9.12,17.94,44.57
dla102x,1638.98,156.183,256,224,5.89,19.42,26.31
resnet61q,1638.11,156.265,256,288,9.87,21.52,36.85
gmixer_24_224,1615.83,158.42,256,224,5.28,14.45,24.72
vgg16,1592.01,160.79,256,224,15.47,13.56,138.36
xcit_nano_12_p16_384_dist,1580.63,161.949,256,384,1.64,12.15,3.05
regnetx_032,1571.21,162.919,256,224,3.2,11.37,15.3
repvgg_b1g4,1566.48,163.412,256,224,8.15,10.64,39.97
regnetx_040,1550.05,165.143,256,224,3.99,12.2,22.12
twins_svt_small,1531.98,167.089,256,224,2.94,13.75,24.06
seresnext101_32x4d,1528.6,167.459,256,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1525.5,167.801,256,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1523.33,168.041,256,224,8.02,21.26,48.96
res2net101_26w_4s,1522.04,168.183,256,224,8.1,18.45,45.21
xception,1515.79,168.877,256,299,8.4,35.83,22.86
visformer_small,1505.04,170.081,256,224,4.88,11.43,40.22
botnet50ts_256,1504.07,170.19,256,256,5.54,22.23,22.74
resnest50d_1s4x24d,1495.52,171.166,256,224,4.43,13.57,25.68
resnetv2_50x1_bit_distilled,1491.91,171.579,256,224,4.23,11.11,25.55
resnetv2_152,1488.31,171.994,256,224,11.55,22.56,60.19
efficientnet_el,1485.73,172.293,256,300,8.0,30.7,10.59
efficientnet_el_pruned,1484.88,172.392,256,300,8.0,30.7,10.59
gluon_resnet152_v1b,1466.32,174.574,256,224,11.56,22.56,60.19
crossvit_small_240,1465.43,174.68,256,240,5.63,18.17,26.86
tv_resnet152,1464.76,174.758,256,224,11.56,22.56,60.19
resnet152,1463.7,174.886,256,224,11.56,22.56,60.19
res2net50_26w_8s,1440.77,177.671,256,224,8.37,17.95,48.4
tf_efficientnet_el,1439.79,177.79,256,300,8.0,30.7,10.59
halo2botnet50ts_256,1435.45,178.33,256,256,5.02,21.78,22.64
hrnet_w32,1431.59,178.81,256,224,8.97,22.02,41.23
gluon_resnet152_v1c,1431.14,178.867,256,224,11.8,23.36,60.21
nf_seresnet101,1430.41,178.958,256,224,8.02,16.27,49.33
vgg16_bn,1430.18,178.985,256,224,15.5,13.56,138.37
resnetv2_152d,1427.39,179.335,256,224,11.8,23.36,60.2
resmlp_36_224,1426.81,179.41,256,224,8.91,16.33,44.69
resmlp_36_distilled_224,1426.22,179.484,256,224,8.91,16.33,44.69
nf_ecaresnet101,1423.4,179.839,256,224,8.01,16.27,44.55
mixnet_m,1415.37,180.859,256,224,0.36,8.19,5.01
gluon_resnet152_v1d,1405.52,182.126,256,224,11.8,23.36,60.21
ese_vovnet99b,1395.24,183.468,256,224,16.51,11.27,63.2
gmlp_s16_224,1389.58,184.216,256,224,4.42,15.1,19.42
tf_mixnet_m,1379.18,185.603,256,224,0.36,8.19,5.01
regnety_040,1367.09,187.246,256,224,4.0,12.29,20.65
vit_large_patch32_224,1365.88,187.414,256,224,15.39,13.3,306.54
hrnet_w18,1363.25,187.774,256,224,4.32,16.31,21.3
vit_base_r26_s32_224,1359.83,188.244,256,224,6.81,12.36,101.38
crossvit_15_240,1344.51,190.391,256,240,5.81,19.77,27.53
resnetv2_50d_evob,1343.28,190.567,256,224,4.33,11.92,25.59
ecaresnet50t,1341.42,190.83,256,320,8.82,24.13,25.57
mixer_b16_224,1341.1,190.877,256,224,12.62,14.53,59.88
mixer_b16_224_miil,1340.95,190.898,256,224,12.62,14.53,59.88
vgg19,1334.38,191.835,256,224,19.63,14.86,143.67
regnety_032,1331.17,192.299,256,288,5.29,18.61,19.44
cait_xxs24_224,1325.98,193.051,256,224,2.53,20.29,11.96
gluon_resnet152_v1s,1323.82,193.367,256,224,12.92,24.96,60.32
mixer_l32_224,1320.53,193.849,256,224,11.27,19.86,206.94
skresnext50_32x4d,1317.3,194.322,256,224,4.5,17.18,27.48
xception41,1314.21,194.782,256,299,9.28,39.86,26.97
crossvit_15_dagger_240,1313.12,194.944,256,240,6.13,20.43,28.21
densenet161,1297.86,197.236,256,224,7.79,11.06,28.68
dpn92,1283.16,199.496,256,224,6.54,18.21,37.67
efficientnet_lite4,1279.6,200.05,256,380,4.04,45.66,13.01
tresnet_m,1272.03,201.24,256,224,5.74,7.31,31.39
legacy_seresnet152,1268.99,201.723,256,224,11.33,22.08,66.82
dla169,1257.87,203.505,256,224,11.6,20.2,53.39
seresnet152,1252.8,204.325,256,224,11.57,22.61,66.82
swin_tiny_patch4_window7_224,1250.47,204.71,256,224,4.51,17.06,28.29
vit_base_patch32_384,1245.01,205.608,256,384,13.06,16.5,88.3
repvgg_b2,1240.95,206.281,256,224,20.45,12.9,89.02
twins_pcpvt_small,1237.75,206.812,256,224,3.83,18.08,24.11
inception_v4,1232.0,207.78,256,299,12.28,15.09,42.68
regnetx_080,1230.26,208.073,256,224,8.02,14.06,39.57
convit_small,1215.14,210.662,256,224,5.76,17.87,27.78
vgg19_bn,1208.18,211.876,256,224,19.66,14.86,143.68
xcit_nano_12_p8_224_dist,1199.12,213.478,256,224,2.16,15.71,3.05
xcit_nano_12_p8_224,1193.48,214.486,256,224,2.16,15.71,3.05
hrnet_w30,1182.47,216.483,256,224,8.15,21.21,37.71
tf_efficientnet_lite4,1173.72,218.096,256,380,4.04,45.66,13.01
resnetv2_50d_gn,1171.09,218.587,256,224,4.38,11.92,25.57
vit_tiny_patch16_384,1165.29,219.677,256,384,4.7,25.39,5.79
dla102x2,1162.53,220.196,256,224,9.34,29.91,41.28
regnetx_064,1154.46,221.737,256,224,6.49,16.37,26.21
vit_small_resnet50d_s16_224,1148.12,222.96,256,224,13.48,24.82,57.53
resnest50d,1142.3,224.096,256,224,5.4,14.36,27.48
xcit_tiny_12_p16_384_dist,1120.72,228.412,256,384,3.64,18.26,6.72
nf_regnet_b4,1118.05,228.958,256,384,4.7,28.61,30.21
mixnet_l,1111.69,230.267,256,224,0.58,10.84,7.33
efficientnetv2_s,1109.52,230.718,256,384,8.44,35.77,21.46
resnet50_gn,1084.83,235.97,256,224,4.14,11.11,25.56
tf_efficientnetv2_s,1082.56,236.462,256,384,8.44,35.77,21.46
tf_efficientnetv2_s_in21ft1k,1080.83,236.841,256,384,8.44,35.77,21.46
tf_mixnet_l,1080.28,236.961,256,224,0.58,10.84,7.33
wide_resnet101_2,1080.17,236.987,256,224,22.8,21.23,126.89
gluon_resnext101_64x4d,1075.35,238.048,256,224,15.52,31.21,83.46
dpn98,1075.31,238.059,256,224,11.73,25.2,61.57
eca_nfnet_l0,1073.57,238.443,256,288,7.12,17.29,24.14
resnext101_64x4d,1073.4,238.483,256,224,15.52,31.21,83.46
regnetz_d8,1072.66,238.647,256,320,6.19,37.08,23.37
convnext_small,1068.35,239.61,256,224,8.7,21.56,50.22
nfnet_l0,1067.0,239.911,256,288,7.13,17.29,35.07
resnet200,1062.15,241.009,256,224,15.07,32.19,64.67
resnetrs101,1051.01,243.563,256,288,13.56,28.53,63.62
inception_resnet_v2,1033.59,247.668,256,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,1033.26,247.747,256,299,13.18,25.06,55.84
efficientnetv2_rw_s,1032.33,247.972,256,384,8.72,38.03,23.94
nest_tiny,1020.47,250.854,256,224,5.83,25.48,17.06
coat_lite_small,1020.33,250.888,256,224,3.96,22.09,19.84
jx_nest_tiny,1008.4,253.854,256,224,5.83,25.48,17.06
vit_base_patch16_224_miil,996.21,256.96,256,224,17.58,23.9,86.54
crossvit_18_240,991.56,258.167,256,240,9.05,26.26,43.27
gluon_seresnext101_64x4d,990.21,258.518,256,224,15.53,31.25,88.23
swsl_resnext101_32x8d,977.82,261.793,256,224,16.48,31.21,88.79
ssl_resnext101_32x8d,977.78,261.803,256,224,16.48,31.21,88.79
resnext101_32x8d,976.71,262.092,256,224,16.48,31.21,88.79
resnet101d,976.38,262.181,256,320,16.48,34.77,44.57
ig_resnext101_32x8d,976.0,262.282,256,224,16.48,31.21,88.79
crossvit_18_dagger_240,971.37,263.533,256,240,9.5,27.03,44.27
regnetz_d32,970.52,263.764,256,320,9.33,37.08,27.58
vit_base_patch16_224_sam,969.72,263.98,256,224,17.58,23.9,86.57
deit_base_patch16_224,967.9,264.478,256,224,17.58,23.9,86.57
vit_base_patch16_224,966.99,264.723,256,224,17.58,23.9,86.57
deit_base_distilled_patch16_224,962.72,265.901,256,224,17.68,24.05,87.34
repvgg_b3,962.17,266.052,256,224,29.16,15.1,123.09
resnest50d_4s2x40d,957.24,267.424,256,224,4.4,17.94,30.42
gluon_xception65,952.59,268.729,256,299,13.96,52.48,39.92
efficientnet_b4,950.53,269.31,256,384,4.51,50.04,19.34
xception65,944.4,271.059,256,299,13.96,52.48,39.92
ese_vovnet39b_evos,937.23,273.135,256,224,7.07,6.74,24.58
hrnet_w40,919.71,278.336,256,224,12.75,25.29,57.56
seresnext101_32x8d,905.52,282.696,256,224,16.48,31.25,93.57
twins_pcpvt_base,904.7,282.951,256,224,6.68,25.25,43.83
tf_efficientnet_b4,899.69,284.527,256,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,899.17,284.692,256,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,898.81,284.808,256,380,4.49,49.49,19.34
xcit_small_24_p16_224_dist,897.88,285.104,256,224,9.1,23.64,47.67
xcit_small_24_p16_224,897.08,285.357,256,224,9.1,23.64,47.67
beit_base_patch16_224,894.85,286.068,256,224,17.58,23.9,86.53
cait_xxs36_224,891.78,287.055,256,224,3.77,30.34,17.3
twins_svt_base,885.5,289.087,256,224,8.59,26.33,56.07
regnety_080,865.03,295.931,256,224,8.0,17.97,39.18
hrnet_w48,862.23,296.892,256,224,17.34,28.56,77.47
xcit_tiny_12_p8_224_dist,851.38,300.676,256,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,850.38,301.029,256,224,4.81,23.6,6.71
pit_b_224,831.73,307.778,256,224,12.42,32.94,73.76
pit_b_distilled_224,829.76,308.512,256,224,12.5,33.07,74.79
nfnet_f0s,822.42,311.265,256,256,12.62,18.05,71.49
nfnet_f0,820.01,312.18,256,256,12.62,18.05,71.49
swin_small_patch4_window7_224,808.39,316.666,256,224,8.77,27.47,49.61
repvgg_b2g4,805.1,317.961,256,224,12.63,12.9,61.76
resnetv2_50d_evos,799.75,320.087,256,224,4.33,11.92,25.59
convnext_base,795.35,321.858,256,224,15.38,28.75,88.59
convnext_base_in22ft1k,795.35,321.856,256,224,15.38,28.75,88.59
dpn131,790.54,323.815,256,224,16.09,32.97,79.25
regnetx_120,762.35,335.792,256,224,12.13,21.37,46.11
regnety_064,760.83,336.463,256,224,6.39,16.41,30.58
hrnet_w44,758.65,337.428,256,224,14.94,26.92,67.06
densenet264,753.54,339.715,256,224,12.95,12.8,72.69
mixnet_xl,746.77,342.795,256,224,0.93,14.57,11.9
cait_s24_224,746.74,342.81,256,224,9.35,40.58,46.92
regnety_120,732.77,349.348,256,224,12.14,21.38,51.82
dm_nfnet_f0,732.71,349.374,256,256,12.62,18.05,71.49
xception71,724.87,353.154,256,299,18.09,69.92,42.34
coat_tiny,707.44,361.857,256,224,4.35,27.2,5.5
resnet152d,694.55,368.574,256,320,24.08,47.67,60.21
dpn107,691.54,370.175,256,224,18.38,33.46,86.92
hrnet_w64,684.53,373.966,256,224,28.97,35.09,128.06
nest_small,681.18,375.805,256,224,10.35,40.04,38.35
vit_large_r50_s32_224,676.89,378.187,256,224,19.58,24.41,328.99
jx_nest_small,675.63,378.895,256,224,10.35,40.04,38.35
twins_svt_large,674.75,379.385,256,224,15.15,35.1,99.27
seresnet200d,673.13,380.295,256,256,20.01,43.15,71.86
ecaresnet200d,672.08,380.893,256,256,20.0,43.15,64.69
repvgg_b3g4,671.77,381.071,256,224,17.89,15.1,83.83
cspresnext50_iabn,667.96,383.242,256,256,4.02,15.86,20.57
gmlp_b16_224,667.23,383.66,256,224,15.78,30.21,73.08
crossvit_base_240,662.98,386.123,256,240,21.22,36.33,105.03
xcit_medium_24_p16_224_dist,661.18,387.174,256,224,16.13,31.71,84.4
xcit_medium_24_p16_224,660.34,387.669,256,224,16.13,31.71,84.4
tresnet_l,657.41,389.391,256,224,10.88,11.9,55.99
twins_pcpvt_large,657.27,389.474,256,224,9.84,35.82,60.99
gluon_senet154,656.15,390.143,256,224,20.77,38.69,115.09
senet154,655.06,390.791,256,224,20.77,38.69,115.09
legacy_senet154,654.75,390.977,256,224,20.77,38.69,115.09
convit_base,638.4,400.989,256,224,17.52,31.77,86.54
swin_base_patch4_window7_224,617.48,414.571,256,224,15.47,36.63,87.77
ese_vovnet99b_iabn,611.75,418.46,256,224,16.49,11.27,63.2
regnetx_160,608.26,420.859,256,224,15.99,25.52,54.28
cspdarknet53_iabn,603.85,423.936,256,256,6.53,16.81,27.64
resnetrs152,602.36,424.984,256,320,24.34,48.14,86.62
seresnet152d,600.43,426.343,256,320,24.09,47.72,66.84
xcit_tiny_24_p16_384_dist,596.99,428.807,256,384,6.87,34.29,12.12
xcit_small_12_p16_384_dist,594.31,430.737,256,384,14.14,36.51,26.25
coat_mini,592.69,431.92,256,224,6.82,33.68,10.34
vit_small_patch16_384,592.64,431.956,256,384,15.52,50.78,22.2
regnetz_e8,575.64,444.705,256,320,15.46,63.94,57.7
convmixer_768_32,563.29,454.463,256,224,19.55,25.95,21.11
resnest101e,556.07,460.362,256,256,13.38,28.66,48.28
efficientnetv2_m,555.39,460.921,256,416,18.6,67.5,54.14
vit_base_r50_s16_224,554.34,461.796,256,224,21.66,35.29,98.66
vit_small_r26_s32_384,549.37,465.972,256,384,10.43,29.85,36.47
nf_regnet_b5,533.77,479.592,256,456,11.7,61.95,49.74
seresnet269d,524.13,488.41,256,256,26.59,53.6,113.67
nest_base,514.16,497.89,256,224,17.96,53.39,67.72
jx_nest_base,510.9,501.061,256,224,17.96,53.39,67.72
resnet200d,509.84,502.105,256,320,31.25,67.33,64.69
tnt_s_patch16_224,490.75,521.641,256,224,5.24,24.37,23.76
mixnet_xxl,486.35,526.355,256,224,2.04,23.43,23.96
tresnet_xl,476.44,537.307,256,224,15.17,15.34,78.44
convnext_large,476.21,537.564,256,224,34.4,43.13,197.77
eca_nfnet_l1,475.22,538.689,256,320,14.92,34.42,41.41
convnext_large_in22ft1k,475.1,538.817,256,224,34.4,43.13,197.77
efficientnetv2_rw_m,464.7,550.875,256,416,21.49,79.62,53.24
efficientnet_b5,456.59,560.666,256,456,10.46,98.86,30.39
xcit_small_12_p8_224,453.25,564.8,256,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,452.33,565.946,256,224,18.69,47.21,26.21
xcit_tiny_24_p8_224,450.37,568.411,256,224,9.21,45.39,12.11
mixer_l16_224,449.08,570.035,256,224,44.6,41.69,208.2
xcit_tiny_24_p8_224_dist,448.81,570.379,256,224,9.21,45.39,12.11
halonet_h1,448.78,570.426,256,256,3.0,51.17,8.1
vit_large_patch32_384,441.35,580.019,256,384,45.31,43.86,306.63
resnetrs200,436.51,586.457,256,320,31.51,67.81,93.21
tf_efficientnet_b5,434.35,589.365,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,433.75,590.18,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ns,433.48,590.557,256,456,10.46,98.86,30.39
tf_efficientnetv2_m_in21ft1k,410.56,623.518,256,480,24.76,89.84,54.14
tf_efficientnetv2_m,410.47,623.661,256,480,24.76,89.84,54.14
xcit_nano_12_p8_384_dist,410.15,624.154,256,384,6.34,46.08,3.05
regnety_320,406.99,628.99,256,224,32.34,30.26,145.05
regnety_160,405.46,631.367,256,288,26.37,38.07,83.59
xcit_large_24_p16_224_dist,400.4,639.34,256,224,35.86,47.27,189.1
xcit_large_24_p16_224,400.19,639.684,256,224,35.86,47.27,189.1
regnetz_d8_evob,388.06,659.67,256,320,6.12,37.08,23.41
tnt_b_patch16_224,385.58,663.922,256,224,14.09,39.01,65.41
swin_large_patch4_window7_224,383.73,667.122,256,224,34.53,54.94,196.53
resnetv2_50x1_bitm,374.41,683.729,256,448,16.62,44.46,25.55
ssl_resnext101_32x16d,338.57,756.102,256,224,36.27,51.18,194.03
swsl_resnext101_32x16d,338.32,756.674,256,224,36.27,51.18,194.03
ig_resnext101_32x16d,338.18,756.969,256,224,36.27,51.18,194.03
vit_large_patch16_224,326.85,783.223,256,224,61.6,63.52,304.33
convnext_xlarge_in22ft1k,321.97,795.088,256,224,60.97,57.5,350.2
tresnet_m_448,321.59,796.023,256,448,22.94,29.21,31.39
xcit_small_24_p16_384_dist,315.9,810.38,256,384,26.72,68.58,47.67
crossvit_15_dagger_408,307.27,833.13,256,408,21.45,95.05,28.5
nasnetalarge,305.09,839.069,256,331,23.89,90.56,88.75
beit_large_patch16_224,301.03,850.396,256,224,61.6,63.52,304.43
pnasnet5large,294.97,867.879,256,331,25.04,92.89,86.06
xcit_tiny_12_p8_384_dist,290.12,882.382,256,384,14.13,69.14,6.71
ecaresnet269d,282.41,906.484,256,352,50.25,101.25,102.09
nfnet_f1s,278.33,919.751,256,320,35.97,46.77,132.63
nfnet_f1,277.93,921.079,256,320,35.97,46.77,132.63
resnetrs270,274.74,931.762,256,352,51.13,105.48,129.86
convnext_base_384_in22ft1k,274.29,933.296,256,384,45.2,84.49,88.59
resnetv2_152x2_bit_teacher,268.11,954.835,256,224,46.95,45.11,236.34
efficientnet_b6,261.2,490.039,128,528,19.4,167.39,43.04
regnetx_320,257.17,995.447,256,224,31.81,36.3,107.81
vit_base_patch16_384,256.27,998.923,256,384,55.54,101.56,86.86
deit_base_patch16_384,256.23,999.078,256,384,55.54,101.56,86.86
deit_base_distilled_patch16_384,253.03,1011.71,256,384,55.65,101.82,87.63
dm_nfnet_f1,251.34,1018.532,256,320,35.97,46.77,132.63
tf_efficientnet_b6_ns,249.3,513.425,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ap,249.28,513.468,128,528,19.4,167.39,43.04
tf_efficientnet_b6,249.27,513.475,128,528,19.4,167.39,43.04
cait_xxs24_384,240.96,1062.389,256,384,9.63,122.66,12.03
resnetv2_101x1_bitm,237.63,1077.288,256,448,31.65,64.93,44.54
xcit_small_24_p8_224,236.59,1082.038,256,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,236.51,1082.395,256,224,35.81,90.78,47.63
efficientnetv2_l,230.67,1109.79,256,480,56.4,157.99,118.52
xcit_medium_24_p16_384_dist,229.71,1114.433,256,384,47.39,91.64,84.4
crossvit_18_dagger_408,228.55,560.041,128,408,32.47,124.87,44.61
tf_efficientnetv2_l,228.05,1122.527,256,480,56.4,157.99,118.52
eca_nfnet_l2,227.63,1124.623,256,384,30.05,68.28,56.72
tf_efficientnetv2_l_in21ft1k,227.22,1126.624,256,480,56.4,157.99,118.52
vit_large_r50_s32_384,223.83,1143.726,256,384,57.43,76.52,329.09
beit_base_patch16_384,221.26,1157.006,256,384,55.54,101.56,86.74
regnetz_d8_evos,211.13,1212.504,256,320,7.03,38.92,23.46
resmlp_big_24_224,198.32,1290.833,256,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,197.59,1295.599,256,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,197.33,1297.278,256,224,100.23,87.31,129.14
resnest200e,191.82,1334.548,256,320,35.69,82.78,70.2
resnetrs350,182.63,1401.718,256,384,77.59,154.74,163.96
swin_base_patch4_window12_384,171.61,745.879,128,384,47.19,134.78,87.9
cait_xs24_384,171.6,1491.866,256,384,19.28,183.98,26.67
xcit_medium_24_p8_224,171.09,1496.317,256,224,63.53,121.23,84.32
xcit_medium_24_p8_224_dist,171.08,1496.368,256,224,63.53,121.23,84.32
vit_base_patch8_224,170.14,1504.628,256,224,78.22,161.69,86.58
tresnet_l_448,163.98,1561.153,256,448,43.5,47.56,55.99
convnext_large_384_in22ft1k,162.15,1578.727,256,384,101.09,126.74,197.77
cait_xxs36_384,161.38,1586.306,256,384,14.35,183.7,17.37
vit_base_r50_s16_384,161.14,1588.636,256,384,67.43,135.03,98.95
vit_base_resnet50_384,161.06,1589.462,256,384,67.43,135.03,98.95
nfnet_f2s,156.52,1635.579,256,352,63.22,79.06,193.78
efficientnet_b7,156.11,409.945,64,600,38.33,289.94,66.35
nfnet_f2,155.79,1643.217,256,352,63.22,79.06,193.78
xcit_tiny_24_p8_384_dist,152.9,1674.228,256,384,27.05,132.95,12.11
densenet264d_iabn,151.23,1692.723,256,224,13.47,14.0,72.74
xcit_small_12_p8_384_dist,150.53,1700.627,256,384,54.92,138.29,26.21
tf_efficientnet_b7_ns,150.44,425.41,64,600,38.33,289.94,66.35
tf_efficientnet_b7_ap,150.35,425.668,64,600,38.33,289.94,66.35
tf_efficientnet_b7,150.33,425.703,64,600,38.33,289.94,66.35
efficientnetv2_xl,146.15,1751.589,256,512,93.85,247.32,208.12
tf_efficientnetv2_xl_in21ft1k,144.77,1768.278,256,512,93.85,247.32,208.12
dm_nfnet_f2,141.71,1806.539,256,352,63.22,79.06,193.78
xcit_large_24_p16_384_dist,138.32,1850.744,256,384,105.35,137.17,189.1
cait_s24_384,132.94,1925.665,256,384,32.17,245.31,47.06
resnetrs420,132.69,1929.303,256,416,108.45,213.79,191.89
vit_huge_patch14_224,131.53,1946.246,256,224,167.4,139.41,632.05
eca_nfnet_l3,127.14,2013.536,256,448,52.55,118.4,72.04
tresnet_xl_448,120.79,2119.391,256,448,60.65,61.31,78.44
efficientnet_cc_b0_8e,117.25,8.519,1,224,0.42,9.42,24.01
efficientnet_cc_b0_4e,114.89,8.693,1,224,0.41,9.42,13.31
convnext_xlarge_384_in22ft1k,110.42,1159.152,128,384,179.18,168.99,350.2
tf_efficientnet_cc_b0_8e,109.13,9.153,1,224,0.42,9.42,24.01
swin_large_patch4_window12_384,108.07,1184.415,128,384,104.08,202.16,196.74
tf_efficientnet_cc_b0_4e,106.38,9.39,1,224,0.41,9.42,13.31
xcit_large_24_p8_224,103.21,2480.29,256,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,103.09,2483.315,256,224,141.23,181.56,188.93
efficientnet_b8,95.86,667.618,64,672,63.48,442.89,87.41
resnetv2_152x2_bit_teacher_384,93.91,2726.129,256,384,136.16,132.56,236.34
tf_efficientnet_b8,93.03,687.97,64,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,92.99,688.234,64,672,63.48,442.89,87.41
resnetv2_50x3_bitm,90.18,1419.305,128,448,145.7,133.37,217.32
resnest269e,90.1,2841.126,256,416,77.69,171.98,110.93
cait_s36_384,88.82,2882.347,256,384,47.99,367.4,68.37
vit_large_patch16_384,88.44,2894.698,256,384,191.21,270.24,304.72
vit_giant_patch14_224,87.02,2941.98,256,224,267.18,192.64,1012.61
nfnet_f3s,85.07,3009.339,256,416,115.58,141.78,254.92
nfnet_f3,84.65,3024.233,256,416,115.58,141.78,254.92
efficientnet_cc_b1_8e,83.46,11.971,1,240,0.75,15.44,39.72
convmixer_1024_20_ks9_p14,81.44,3143.542,256,224,5.55,5.51,24.38
xcit_small_24_p8_384_dist,78.98,3241.259,256,384,105.24,265.91,47.63
beit_large_patch16_384,77.03,3323.174,256,384,191.21,270.24,305.0
dm_nfnet_f3,76.93,3327.777,256,416,115.58,141.78,254.92
tf_efficientnet_cc_b1_8e,76.09,13.131,1,240,0.75,15.44,39.72
resnetv2_152x2_bitm,68.83,1859.681,128,448,184.99,180.43,236.34
xcit_medium_24_p8_384_dist,58.64,4365.679,256,384,186.67,354.73,84.32
resnetv2_101x3_bitm,55.09,2323.244,128,448,280.33,194.78,387.93
vit_gigantic_patch14_224,52.92,4837.231,256,224,483.95,275.37,1844.44
nfnet_f4s,45.21,5662.39,256,512,216.26,262.26,316.07
nfnet_f4,45.13,5672.08,256,512,216.26,262.26,316.07
dm_nfnet_f4,41.12,6225.397,256,512,216.26,262.26,316.07
xcit_large_24_p8_384_dist,35.18,3638.243,128,384,415.0,531.82,188.93
nfnet_f5s,33.4,7663.628,256,544,290.97,349.71,377.21
nfnet_f5,33.22,7705.647,256,544,290.97,349.71,377.21
beit_large_patch16_512,31.23,2049.459,64,512,362.24,656.39,305.67
cait_m36_384,30.6,8365.416,256,384,173.11,734.81,271.22
dm_nfnet_f5,30.51,8392.012,256,544,290.97,349.71,377.21
nfnet_f6s,24.87,10292.837,256,576,378.69,452.2,438.36
nfnet_f6,24.74,10346.138,256,576,378.69,452.2,438.36
dm_nfnet_f6,23.05,11108.286,256,576,378.69,452.2,438.36
nfnet_f7s,19.54,13099.713,256,608,480.39,570.85,499.5
nfnet_f7,19.47,13146.917,256,608,480.39,570.85,499.5
resnetv2_152x4_bitm,17.96,3562.952,64,480,844.84,414.26,936.53
cait_m48_448,13.23,9672.182,128,448,329.41,1708.23,356.46
convmixer_1536_20,13.15,19467.539,256,224,48.68,33.03,51.63
1 model infer_samples_per_sec infer_step_time infer_batch_size infer_img_size infer_gmacs infer_macts param_count
2 tinynet_e 52443.33 4.87 256 106 0.03 0.69 2.04
3 lcnet_035 41836.65 6.108 256 224 0.03 1.04 1.64
4 mobilenetv3_small_050 40675.32 6.283 256 224 0.03 0.92 1.59
5 lcnet_050 37031.22 6.902 256 224 0.05 1.26 1.88
6 mobilenetv3_small_075 33134.36 7.715 256 224 0.05 1.3 2.04
7 mobilenetv3_small_100 30332.75 8.428 256 224 0.06 1.42 2.54
8 tinynet_d 29729.64 8.599 256 152 0.05 1.42 2.34
9 tf_mobilenetv3_small_minimal_100 28698.97 8.909 256 224 0.06 1.41 2.04
10 tf_mobilenetv3_small_075 27515.61 9.292 256 224 0.05 1.3 2.04
11 tf_mobilenetv3_small_100 25529.73 10.016 256 224 0.06 1.42 2.54
12 lcnet_075 25351.21 10.087 256 224 0.1 1.99 2.36
13 lcnet_100 21251.26 12.035 256 224 0.16 2.52 2.95
14 mnasnet_small 20610.12 12.41 256 224 0.07 2.16 2.03
15 levit_128s 18679.12 13.694 256 224 0.31 1.88 7.78
16 mobilenetv2_035 18422.82 13.885 256 224 0.07 2.86 1.68
17 ghostnet_050 17065.19 14.99 256 224 0.05 1.77 2.59
18 regnetx_002 17008.51 15.04 256 224 0.2 2.16 2.68
19 mnasnet_050 16943.72 15.098 256 224 0.11 3.07 2.22
20 tinynet_c 16089.67 15.9 256 184 0.11 2.87 2.46
21 mobilenetv2_050 15226.35 16.802 256 224 0.1 3.64 1.97
22 regnety_002 15182.41 16.851 256 224 0.2 2.17 3.16
23 semnasnet_050 14956.97 17.104 256 224 0.11 3.44 2.08
24 lcnet_150 14301.96 17.889 256 224 0.34 3.79 4.5
25 regnetx_004 13656.57 18.735 256 224 0.4 3.14 5.16
26 gernet_s 13307.64 19.226 256 224 0.75 2.65 8.17
27 mobilenetv3_large_075 12875.59 19.872 256 224 0.16 4.0 3.99
28 levit_128 12869.05 19.881 256 224 0.41 2.71 9.21
29 mnasnet_075 11851.07 21.591 256 224 0.23 4.77 3.17
30 mobilenetv3_rw 11749.18 21.778 256 224 0.23 4.41 5.48
31 hardcorenas_a 11724.1 21.825 256 224 0.23 4.38 5.26
32 mobilenetv3_large_100 11546.8 22.16 256 224 0.23 4.41 5.48
33 mobilenetv3_large_100_miil 11527.85 22.196 256 224 0.23 4.41 5.48
34 levit_192 11521.32 22.208 256 224 0.66 3.2 10.95
35 tf_mobilenetv3_large_075 11097.44 23.056 256 224 0.16 4.0 3.99
36 ese_vovnet19b_slim_dw 11011.34 23.237 256 224 0.4 5.28 1.9
37 tf_mobilenetv3_large_minimal_100 10807.48 23.676 256 224 0.22 4.4 3.92
38 tinynet_b 10794.3 23.705 256 188 0.21 4.44 3.73
39 hardcorenas_b 10649.59 24.027 256 224 0.26 5.09 5.18
40 hardcorenas_c 10616.11 24.103 256 224 0.28 5.01 5.52
41 mnasnet_100 10429.97 24.534 256 224 0.33 5.46 4.38
42 mnasnet_b1 10405.42 24.591 256 224 0.33 5.46 4.38
43 mixer_s32_224 10314.02 24.809 256 224 1.0 2.28 19.1
44 ssl_resnet18 10228.31 25.016 256 224 1.82 2.48 11.69
45 gluon_resnet18_v1b 10218.86 25.04 256 224 1.82 2.48 11.69
46 swsl_resnet18 10183.71 25.126 256 224 1.82 2.48 11.69
47 resnet18 10180.53 25.135 256 224 1.82 2.48 11.69
48 tf_mobilenetv3_large_100 10049.81 25.462 256 224 0.23 4.41 5.48
49 ghostnet_100 9984.48 25.628 256 224 0.15 3.55 5.18
50 mobilenetv2_075 9980.04 25.64 256 224 0.22 5.86 2.64
51 semnasnet_075 9964.54 25.68 256 224 0.23 5.54 2.91
52 hardcorenas_d 9736.47 26.282 256 224 0.3 4.93 7.5
53 seresnet18 9448.37 27.084 256 224 1.82 2.49 11.78
54 spnasnet_100 9378.19 27.285 256 224 0.35 6.03 4.42
55 vit_small_patch32_224 9330.47 27.426 256 224 1.15 2.5 22.88
56 regnety_006 9290.2 27.545 256 224 0.61 4.33 6.06
57 legacy_seresnet18 9195.77 27.828 256 224 1.82 2.49 11.78
58 mobilenetv2_100 8931.8 28.65 256 224 0.31 6.68 3.5
59 regnety_004 8913.44 28.71 256 224 0.41 3.89 4.34
60 semnasnet_100 8878.3 28.823 256 224 0.32 6.23 3.89
61 mnasnet_a1 8872.5 28.841 256 224 0.32 6.23 3.89
62 hardcorenas_f 8746.45 29.258 256 224 0.35 5.57 8.2
63 tinynet_a 8705.83 29.394 256 192 0.35 5.41 6.19
64 efficientnet_lite0 8678.08 29.489 256 224 0.4 6.74 4.65
65 levit_256 8605.17 29.738 256 224 1.13 4.23 18.89
66 fbnetc_100 8589.17 29.794 256 224 0.4 6.51 5.57
67 hardcorenas_e 8587.1 29.801 256 224 0.35 5.65 8.07
68 resnet18d 8454.18 30.27 256 224 2.06 3.29 11.71
69 tf_efficientnetv2_b0 8451.14 30.28 256 224 0.73 4.77 7.14
70 ese_vovnet19b_slim 8409.91 30.429 256 224 1.69 3.52 3.17
71 regnetx_008 8190.92 31.242 256 224 0.81 5.15 7.26
72 vit_tiny_r_s16_p8_224 8188.54 31.252 256 224 0.44 2.06 6.34
73 regnetx_006 7654.81 33.432 256 224 0.61 3.98 6.2
74 ghostnet_130 7516.11 34.049 256 224 0.24 4.6 7.36
75 tf_efficientnet_lite0 7513.34 34.061 256 224 0.4 6.74 4.65
76 regnety_008 7334.93 34.89 256 224 0.81 5.25 6.26
77 mnasnet_140 7212.43 35.483 256 224 0.6 7.71 7.12
78 efficientnet_b0 7189.76 35.595 256 224 0.4 6.75 5.29
79 rexnetr_100 6816.78 37.543 256 224 0.43 7.72 4.88
80 mobilenetv2_110d 6785.21 37.718 256 224 0.45 8.71 4.52
81 tf_efficientnet_b0_ns 6397.82 40.002 256 224 0.4 6.75 5.29
82 tf_efficientnet_b0_ap 6366.01 40.201 256 224 0.4 6.75 5.29
83 tf_efficientnet_b0 6345.74 40.33 256 224 0.4 6.75 5.29
84 hrnet_w18_small 6240.1 41.014 256 224 1.61 5.72 13.19
85 semnasnet_140 6194.36 41.317 256 224 0.6 8.87 6.11
86 ese_vovnet19b_dw 6016.74 42.537 256 224 1.34 8.25 6.54
87 mobilenetv2_140 6010.52 42.581 256 224 0.6 9.57 6.11
88 resnet34 5949.09 43.021 256 224 3.67 3.74 21.8
89 gluon_resnet34_v1b 5944.0 43.057 256 224 3.67 3.74 21.8
90 tv_resnet34 5920.52 43.227 256 224 3.67 3.74 21.8
91 gernet_m 5909.18 43.311 256 224 3.02 5.24 21.14
92 efficientnet_lite1 5815.72 44.007 256 240 0.62 10.14 5.42
93 selecsls42 5752.79 44.489 256 224 2.94 4.62 30.35
94 selecsls42b 5752.33 44.492 256 224 2.98 4.62 32.46
95 fbnetv3_b 5567.33 45.971 256 256 0.55 9.1 8.6
96 efficientnet_b1_pruned 5558.39 46.045 256 240 0.4 6.21 6.33
97 resnet26 5532.69 46.26 256 224 2.36 7.35 16.0
98 skresnet18 5529.57 46.284 256 224 1.82 3.24 11.96
99 efficientnet_es_pruned 5519.91 46.366 256 224 1.81 8.73 5.44
100 efficientnet_es 5518.49 46.378 256 224 1.81 8.73 5.44
101 tf_efficientnetv2_b1 5515.51 46.402 256 240 1.21 7.34 8.14
102 rexnet_100 5500.83 46.527 256 224 0.41 7.44 4.8
103 dla46_c 5464.03 46.84 256 224 0.58 4.5 1.3
104 seresnet34 5439.78 47.05 256 224 3.67 3.74 21.96
105 resnet34d 5325.4 48.06 256 224 3.91 4.54 21.82
106 resnetblur18 5301.3 48.279 256 224 2.34 3.39 11.69
107 legacy_seresnet34 5280.08 48.473 256 224 3.67 3.74 21.96
108 rexnetr_130 5239.92 48.845 256 224 0.68 9.81 7.61
109 tf_efficientnet_es 5226.26 48.971 256 224 1.81 8.73 5.44
110 tf_efficientnet_lite1 5192.67 49.288 256 240 0.62 10.14 5.42
111 levit_384 5136.88 49.824 256 224 2.36 6.26 39.13
112 nf_regnet_b0 5081.73 50.365 256 256 0.64 5.58 8.76
113 selecsls60 5015.37 51.032 256 224 3.59 5.52 30.67
114 selecsls60b 5008.51 51.102 256 224 3.63 5.52 32.77
115 mobilenetv2_120d 5006.42 51.123 256 224 0.69 11.97 5.83
116 repvgg_b0 4831.81 52.969 256 224 3.41 6.15 15.82
117 resnet26d 4774.01 53.612 256 224 2.6 8.15 16.01
118 rexnetr_150 4621.39 55.383 256 224 0.89 11.13 9.78
119 fbnetv3_d 4543.95 56.327 256 256 0.68 11.1 10.31
120 nf_resnet26 4522.01 56.601 256 224 2.41 7.35 16.0
121 deit_tiny_patch16_224 4516.94 56.664 256 224 1.26 5.97 5.72
122 visformer_tiny 4514.36 56.695 256 224 1.27 5.72 10.32
123 vit_tiny_patch16_224 4509.39 56.76 256 224 1.26 5.97 5.72
124 efficientnet_lite2 4476.87 57.171 256 260 0.89 12.9 6.09
125 xcit_nano_12_p16_224_dist 4461.13 57.373 256 224 0.56 4.17 3.05
126 deit_tiny_distilled_patch16_224 4450.37 57.513 256 224 1.27 6.01 5.91
127 xcit_nano_12_p16_224 4443.48 57.6 256 224 0.56 4.17 3.05
128 pit_ti_distilled_224 4267.82 59.972 256 224 0.71 6.23 5.1
129 pit_ti_224 4256.93 60.125 256 224 0.7 6.19 4.85
130 resmlp_12_distilled_224 4163.6 61.475 256 224 3.01 5.5 15.35
131 resmlp_12_224 4160.41 61.521 256 224 3.01 5.5 15.35
132 gernet_l 4096.96 62.474 256 256 4.57 8.0 31.08
133 tf_efficientnetv2_b2 4081.75 62.706 256 260 1.72 9.84 10.1
134 legacy_seresnext26_32x4d 4060.33 63.037 256 224 2.49 9.39 16.79
135 tf_efficientnet_b1 4046.06 63.259 256 240 0.71 10.88 7.79
136 tf_efficientnet_b1_ap 4044.6 63.282 256 240 0.71 10.88 7.79
137 tf_efficientnet_b1_ns 4042.64 63.313 256 240 0.71 10.88 7.79
138 tf_efficientnet_lite2 4027.76 63.547 256 260 0.89 12.9 6.09
139 resnext26ts 3956.89 64.685 256 256 2.43 10.52 10.3
140 dla46x_c 3913.74 65.398 256 224 0.54 5.66 1.07
141 efficientnet_b1 3903.13 65.577 256 256 0.77 12.22 7.79
142 dla34 3901.31 65.607 256 224 3.07 5.02 15.74
143 mixer_b32_224 3872.36 66.099 256 224 3.24 6.29 60.29
144 rexnet_130 3847.96 66.517 256 224 0.68 9.71 7.56
145 vit_base_patch32_224_sam 3843.13 66.599 256 224 4.41 5.01 88.22
146 nf_seresnet26 3833.86 66.763 256 224 2.41 7.36 17.4
147 eca_resnext26ts 3823.14 66.95 256 256 2.43 10.52 10.3
148 vit_base_patch32_224 3821.26 66.98 256 224 4.41 5.01 88.22
149 nf_ecaresnet26 3821.0 66.987 256 224 2.41 7.36 16.0
150 seresnext26ts 3814.04 67.108 256 256 2.43 10.52 10.39
151 dla60x_c 3762.66 68.025 256 224 0.59 6.01 1.32
152 efficientnet_b2_pruned 3734.36 68.542 256 260 0.73 9.13 8.31
153 regnetx_016 3682.98 69.498 256 224 1.62 7.93 9.19
154 resnet26t 3677.69 69.597 256 256 3.35 10.52 16.01
155 gcresnext26ts 3645.26 70.216 256 256 2.43 10.53 10.48
156 rexnet_150 3639.9 70.32 256 224 0.9 11.21 9.73
157 seresnext26tn_32x4d 3635.04 70.413 256 224 2.7 10.09 16.81
158 seresnext26t_32x4d 3629.32 70.524 256 224 2.7 10.09 16.81
159 ecaresnext50t_32x4d 3625.17 70.606 256 224 2.7 10.09 15.41
160 seresnext26d_32x4d 3620.48 70.696 256 224 2.73 10.19 16.81
161 ecaresnet50d_pruned 3618.55 70.735 256 224 2.53 6.43 19.94
162 ecaresnext26t_32x4d 3617.96 70.747 256 224 2.7 10.09 15.41
163 resnetv2_50 3573.69 71.622 256 224 4.11 11.11 25.55
164 eca_botnext26ts_256 3538.36 72.339 256 256 2.46 11.6 10.59
165 repvgg_a2 3525.89 72.594 256 224 5.7 6.26 28.21
166 pit_xs_224 3490.24 73.336 256 224 1.4 7.71 10.62
167 mixer_s16_224 3485.97 73.426 256 224 3.79 5.97 18.53
168 pit_xs_distilled_224 3470.1 73.761 256 224 1.41 7.76 11.0
169 eca_halonext26ts 3433.36 74.552 256 256 2.44 11.46 10.76
170 gluon_resnet50_v1b 3424.93 74.734 256 224 4.11 11.11 25.56
171 rexnetr_200 3422.25 74.791 256 224 1.59 15.11 16.52
172 ssl_resnet50 3418.93 74.864 256 224 4.11 11.11 25.56
173 resnet50 3416.87 74.911 256 224 4.11 11.11 25.56
174 tv_resnet50 3416.58 74.915 256 224 4.11 11.11 25.56
175 swsl_resnet50 3415.95 74.931 256 224 4.11 11.11 25.56
176 ecaresnetlight 3410.59 75.049 256 224 4.11 8.42 30.16
177 efficientnet_em 3345.86 76.5 256 240 3.04 14.34 6.9
178 dpn68b 3321.6 77.058 256 224 2.35 10.47 12.61
179 dpn68 3321.37 77.064 256 224 2.35 10.47 12.61
180 resnet32ts 3318.56 77.131 256 256 4.63 11.58 17.96
181 botnet26t_256 3309.73 77.335 256 256 3.32 11.98 12.49
182 resnet33ts 3269.93 78.278 256 256 4.76 11.66 19.68
183 resnetv2_50t 3252.33 78.701 256 224 4.32 11.82 25.57
184 halonet26t 3247.75 78.812 256 256 3.19 11.69 12.48
185 resnetv2_50d 3238.36 79.041 256 224 4.35 11.92 25.57
186 gluon_resnet50_v1c 3234.34 79.139 256 224 4.35 11.92 25.58
187 nf_regnet_b1 3220.71 79.473 256 288 1.02 9.2 10.22
188 tf_efficientnet_em 3211.19 79.71 256 240 3.04 14.34 6.9
189 nf_regnet_b2 3198.33 80.03 256 272 1.22 9.27 14.31
190 xcit_tiny_12_p16_224_dist 3183.42 80.405 256 224 1.24 6.29 6.72
191 xcit_tiny_12_p16_224 3177.02 80.567 256 224 1.24 6.29 6.72
192 regnety_016 3168.83 80.775 256 224 1.63 8.04 11.2
193 gmixer_12_224 3164.43 80.888 256 224 2.67 7.26 12.7
194 tf_efficientnet_b2_ap 3144.11 81.41 256 260 1.02 13.83 9.11
195 tf_efficientnet_b2 3133.02 81.698 256 260 1.02 13.83 9.11
196 eca_resnet33ts 3132.75 81.706 256 256 4.76 11.66 19.68
197 seresnet33ts 3132.5 81.713 256 256 4.76 11.66 19.78
198 skresnet34 3122.38 81.975 256 224 3.67 5.13 22.28
199 resnet50t 3114.92 82.173 256 224 4.32 11.82 25.57
200 tf_efficientnet_b2_ns 3112.24 82.244 256 260 1.02 13.83 9.11
201 gluon_resnet50_v1d 3111.66 82.259 256 224 4.35 11.92 25.58
202 resnet50d 3107.36 82.374 256 224 4.35 11.92 25.58
203 vovnet39a 3097.06 82.648 256 224 7.09 6.73 22.6
204 bat_resnext26ts 3052.18 83.861 256 256 2.53 12.51 10.73
205 legacy_seresnet50 3043.79 84.094 256 224 3.88 10.6 28.09
206 cspresnext50 3003.53 85.222 256 224 3.1 12.14 20.57
207 gcresnet33ts 2986.89 85.696 256 256 4.76 11.68 19.88
208 selecsls84 2981.3 85.857 256 224 5.9 7.57 50.95
209 vit_small_patch32_384 2981.2 85.86 256 384 3.45 8.25 22.92
210 ese_vovnet39b 2975.03 86.038 256 224 7.09 6.74 24.57
211 efficientnet_b2a 2964.53 86.343 256 288 1.12 16.2 9.11
212 efficientnet_b2 2962.37 86.406 256 288 1.12 16.2 9.11
213 eca_vovnet39b 2962.13 86.413 256 224 7.09 6.74 22.6
214 res2net50_48w_2s 2946.94 86.858 256 224 4.18 11.72 25.29
215 seresnet50 2942.22 86.998 256 224 4.11 11.13 28.09
216 efficientnet_b3_pruned 2917.98 87.72 256 300 1.04 11.86 9.86
217 haloregnetz_b 2882.69 88.795 256 224 1.97 11.94 11.68
218 vit_small_resnet26d_224 2880.85 88.851 256 224 5.07 11.12 63.61
219 vgg11 2809.5 91.106 256 224 7.61 7.44 132.86
220 gluon_resnext50_32x4d 2797.04 91.514 256 224 4.26 14.4 25.03
221 swsl_resnext50_32x4d 2795.88 91.552 256 224 4.26 14.4 25.03
222 tv_resnext50_32x4d 2795.77 91.554 256 224 4.26 14.4 25.03
223 mixnet_s 2795.45 91.566 256 224 0.25 6.25 4.13
224 ssl_resnext50_32x4d 2795.35 91.567 256 224 4.26 14.4 25.03
225 resnext50_32x4d 2795.04 91.578 256 224 4.26 14.4 25.03
226 resnetaa50d 2786.19 91.87 256 224 5.39 12.44 25.58
227 ecaresnet101d_pruned 2770.2 92.4 256 224 3.48 7.69 24.88
228 densenet121 2765.59 92.554 256 224 2.87 6.9 7.98
229 tv_densenet121 2754.97 92.91 256 224 2.87 6.9 7.98
230 gluon_resnet50_v1s 2746.47 93.198 256 224 5.47 13.52 25.68
231 rexnet_200 2741.96 93.353 256 224 1.56 14.91 16.37
232 cspresnet50 2739.95 93.42 256 256 4.54 11.5 21.62
233 seresnet50t 2715.96 94.246 256 224 4.32 11.83 28.1
234 resnetrs50 2709.47 94.471 256 224 4.48 12.14 35.69
235 crossvit_tiny_240 2698.8 94.845 256 240 1.57 9.08 7.01
236 ecaresnet50d 2695.97 94.946 256 224 4.35 11.93 25.58
237 tf_mixnet_s 2668.56 95.92 256 224 0.25 6.25 4.13
238 dla60 2658.15 96.295 256 224 4.26 10.16 22.04
239 crossvit_9_240 2646.19 96.732 256 240 1.85 9.52 8.55
240 densenet121d 2643.5 96.83 256 224 3.11 7.7 8.0
241 efficientnet_lite3 2630.67 97.302 256 300 1.65 21.85 8.2
242 convit_tiny 2612.77 97.969 256 224 1.26 7.94 5.71
243 hrnet_w18_small_v2 2612.23 97.989 256 224 2.62 9.65 15.6
244 crossvit_9_dagger_240 2607.67 98.16 256 240 1.99 9.97 8.78
245 regnetz_b16 2592.86 98.722 256 288 2.39 16.43 9.72
246 resnext50d_32x4d 2582.77 99.107 256 224 4.5 15.2 25.05
247 cspresnet50d 2577.78 99.299 256 256 4.86 12.55 21.64
248 cspresnet50w 2560.3 99.977 256 256 5.04 12.19 28.12
249 vgg11_bn 2537.64 100.869 256 224 7.62 7.44 132.87
250 vovnet57a 2533.31 101.041 256 224 8.95 7.52 36.64
251 resnetblur50 2528.53 101.233 256 224 5.16 12.02 25.56
252 gmlp_ti16_224 2479.86 103.22 256 224 1.34 7.55 5.87
253 seresnext50_32x4d 2470.29 103.618 256 224 4.26 14.42 27.56
254 legacy_seresnext50_32x4d 2466.47 103.78 256 224 4.26 14.42 27.56
255 seresnetaa50d 2460.49 104.031 256 224 5.4 12.46 28.11
256 gluon_seresnext50_32x4d 2457.31 104.168 256 224 4.26 14.42 27.56
257 fbnetv3_g 2441.71 104.833 256 288 1.77 21.09 16.62
258 res2net50_26w_4s 2439.77 104.916 256 224 4.28 12.61 25.7
259 ese_vovnet57b 2429.1 105.377 256 224 8.95 7.52 38.61
260 vit_tiny_r_s16_p8_384 2420.04 105.772 256 384 1.34 6.49 6.36
261 gcresnet50t 2406.95 106.347 256 256 5.42 14.67 25.9
262 adv_inception_v3 2387.53 107.209 256 299 5.73 8.97 23.83
263 inception_v3 2378.4 107.624 256 299 5.73 8.97 23.83
264 gluon_inception_v3 2378.14 107.635 256 299 5.73 8.97 23.83
265 dla60x 2376.48 107.71 256 224 3.54 13.8 17.35
266 efficientnetv2_rw_t 2371.59 107.933 256 288 3.19 16.42 13.65
267 tf_inception_v3 2369.67 108.018 256 299 5.73 8.97 23.83
268 densenetblur121d 2366.44 108.168 256 224 3.11 7.9 8.0
269 tf_efficientnet_lite3 2359.22 108.498 256 300 1.65 21.85 8.2
270 resnetblur50d 2356.51 108.624 256 224 5.4 12.82 25.58
271 nf_seresnet50 2321.05 110.283 256 224 4.21 11.13 28.09
272 lambda_resnet26rpt_256 2319.42 110.361 256 256 3.16 11.87 10.99
273 tf_efficientnetv2_b3 2313.61 110.636 256 300 3.04 15.74 14.36
274 nf_ecaresnet50 2312.19 110.706 256 224 4.21 11.13 25.56
275 resnest14d 2311.92 110.718 256 224 2.76 7.33 10.61
276 deit_small_patch16_224 2305.42 111.031 256 224 4.61 11.95 22.05
277 pit_s_224 2300.71 111.258 256 224 2.88 11.56 23.46
278 vit_small_patch16_224 2295.85 111.494 256 224 4.61 11.95 22.05
279 pit_s_distilled_224 2281.8 112.18 256 224 2.9 11.64 24.04
280 densenet169 2278.65 112.335 256 224 3.4 7.3 14.15
281 vit_base_resnet26d_224 2268.07 112.857 256 224 6.97 13.16 101.4
282 deit_small_distilled_patch16_224 2267.44 112.892 256 224 4.63 12.02 22.44
283 skresnet50 2244.08 114.064 256 224 4.11 12.5 25.8
284 darknet53 2213.23 115.656 256 256 9.31 12.39 41.61
285 sehalonet33ts 2208.53 115.902 256 256 3.55 14.7 13.69
286 res2net50_14w_8s 2201.46 116.274 256 224 4.21 13.28 25.06
287 gc_efficientnetv2_rw_t 2198.9 116.41 256 288 3.2 16.45 13.68
288 gcresnext50ts 2187.09 117.039 256 256 3.75 15.46 15.67
289 resnetv2_101 2146.17 119.27 256 224 7.83 16.23 44.54
290 resmlp_24_224 2127.24 120.334 256 224 5.96 10.91 30.02
291 resmlp_24_distilled_224 2125.15 120.451 256 224 5.96 10.91 30.02
292 skresnet50d 2105.03 121.599 256 224 4.36 13.31 25.82
293 gluon_resnet101_v1b 2098.13 122.001 256 224 7.83 16.23 44.55
294 tv_resnet101 2095.51 122.152 256 224 7.83 16.23 44.55
295 resnet101 2094.89 122.19 256 224 7.83 16.23 44.55
296 res2next50 2089.9 122.482 256 224 4.2 13.71 24.67
297 ecaresnet26t 2087.45 122.626 256 320 5.24 16.44 16.01
298 dla60_res2net 2078.29 123.165 256 224 4.15 12.34 20.85
299 nf_regnet_b3 2039.85 125.487 256 320 2.05 14.61 18.59
300 gluon_resnet101_v1c 2026.41 126.32 256 224 8.08 17.04 44.57
301 resnetv2_101d 2021.62 126.619 256 224 8.07 17.04 44.56
302 gluon_resnet101_v1d 1976.83 129.488 256 224 8.08 17.04 44.57
303 vgg13 1972.22 129.79 256 224 11.31 12.25 133.05
304 wide_resnet50_2 1932.56 132.455 256 224 11.43 14.4 68.88
305 sebotnet33ts_256 1892.63 135.25 256 256 3.89 17.46 13.7
306 repvgg_b1 1864.64 137.281 256 224 13.16 10.64 57.42
307 resnetaa101d 1840.38 139.09 256 224 9.12 17.56 44.57
308 legacy_seresnet101 1838.54 139.229 256 224 7.61 15.74 49.33
309 resnet51q 1828.83 139.968 256 288 8.07 20.94 35.7
310 gluon_resnet101_v1s 1822.02 140.491 256 224 9.19 18.64 44.67
311 dla102 1820.91 140.577 256 224 7.19 14.18 33.27
312 dla60_res2next 1817.78 140.818 256 224 3.49 13.17 17.03
313 coat_lite_tiny 1811.49 141.309 256 224 1.6 11.65 5.72
314 vit_base_resnet50d_224 1807.55 141.614 256 224 8.73 16.92 110.97
315 tf_efficientnet_b3 1803.3 141.948 256 300 1.87 23.83 12.23
316 tf_efficientnet_b3_ap 1802.94 141.978 256 300 1.87 23.83 12.23
317 tf_efficientnet_b3_ns 1802.1 142.043 256 300 1.87 23.83 12.23
318 cspdarknet53 1801.31 142.107 256 256 6.57 16.81 27.64
319 seresnet101 1796.92 142.453 256 224 7.84 16.27 49.33
320 efficientnet_b3 1780.99 143.727 256 320 2.01 26.52 12.23
321 efficientnet_b3a 1780.57 143.761 256 320 2.01 26.52 12.23
322 densenet201 1754.6 145.89 256 224 4.34 7.85 20.01
323 vgg13_bn 1750.09 146.264 256 224 11.33 12.25 133.05
324 ssl_resnext101_32x4d 1737.01 147.366 256 224 8.01 21.23 44.18
325 resnext101_32x4d 1736.55 147.407 256 224 8.01 21.23 44.18
326 gluon_resnext101_32x4d 1736.42 147.419 256 224 8.01 21.23 44.18
327 swsl_resnext101_32x4d 1735.28 147.514 256 224 8.01 21.23 44.18
328 halonet50ts 1731.56 147.831 256 256 5.3 19.2 22.73
329 res2net50_26w_6s 1726.13 148.297 256 224 6.33 15.28 37.05
330 nf_resnet101 1725.55 148.347 256 224 8.01 16.23 44.55
331 coat_lite_mini 1724.07 148.474 256 224 2.0 12.25 11.01
332 regnetz_c16 1702.47 150.358 256 320 3.92 25.88 13.46
333 nf_resnet50 1701.7 150.426 256 288 6.88 18.37 25.56
334 ecaresnet101d 1699.81 150.594 256 224 8.08 17.07 44.57
335 xcit_tiny_24_p16_224_dist 1697.25 150.82 256 224 2.34 11.82 12.12
336 xcit_tiny_24_p16_224 1694.56 151.06 256 224 2.34 11.82 12.12
337 xcit_small_12_p16_224_dist 1690.19 151.45 256 224 4.82 12.58 26.25
338 xcit_small_12_p16_224 1687.09 151.729 256 224 4.82 12.58 26.25
339 resnest26d 1686.65 151.769 256 224 3.64 9.97 17.07
340 convnext_tiny 1676.02 152.729 256 224 4.47 13.44 28.59
341 convnext_tiny_hnf 1674.55 152.865 256 224 4.47 13.44 28.59
342 vit_small_r26_s32_224 1656.11 154.567 256 224 3.56 9.85 36.43
343 resnetblur101d 1639.84 156.101 256 224 9.12 17.94 44.57
344 dla102x 1638.98 156.183 256 224 5.89 19.42 26.31
345 resnet61q 1638.11 156.265 256 288 9.87 21.52 36.85
346 gmixer_24_224 1615.83 158.42 256 224 5.28 14.45 24.72
347 vgg16 1592.01 160.79 256 224 15.47 13.56 138.36
348 xcit_nano_12_p16_384_dist 1580.63 161.949 256 384 1.64 12.15 3.05
349 regnetx_032 1571.21 162.919 256 224 3.2 11.37 15.3
350 repvgg_b1g4 1566.48 163.412 256 224 8.15 10.64 39.97
351 regnetx_040 1550.05 165.143 256 224 3.99 12.2 22.12
352 twins_svt_small 1531.98 167.089 256 224 2.94 13.75 24.06
353 seresnext101_32x4d 1528.6 167.459 256 224 8.02 21.26 48.96
354 gluon_seresnext101_32x4d 1525.5 167.801 256 224 8.02 21.26 48.96
355 legacy_seresnext101_32x4d 1523.33 168.041 256 224 8.02 21.26 48.96
356 res2net101_26w_4s 1522.04 168.183 256 224 8.1 18.45 45.21
357 xception 1515.79 168.877 256 299 8.4 35.83 22.86
358 visformer_small 1505.04 170.081 256 224 4.88 11.43 40.22
359 botnet50ts_256 1504.07 170.19 256 256 5.54 22.23 22.74
360 resnest50d_1s4x24d 1495.52 171.166 256 224 4.43 13.57 25.68
361 resnetv2_50x1_bit_distilled 1491.91 171.579 256 224 4.23 11.11 25.55
362 resnetv2_152 1488.31 171.994 256 224 11.55 22.56 60.19
363 efficientnet_el 1485.73 172.293 256 300 8.0 30.7 10.59
364 efficientnet_el_pruned 1484.88 172.392 256 300 8.0 30.7 10.59
365 gluon_resnet152_v1b 1466.32 174.574 256 224 11.56 22.56 60.19
366 crossvit_small_240 1465.43 174.68 256 240 5.63 18.17 26.86
367 tv_resnet152 1464.76 174.758 256 224 11.56 22.56 60.19
368 resnet152 1463.7 174.886 256 224 11.56 22.56 60.19
369 res2net50_26w_8s 1440.77 177.671 256 224 8.37 17.95 48.4
370 tf_efficientnet_el 1439.79 177.79 256 300 8.0 30.7 10.59
371 halo2botnet50ts_256 1435.45 178.33 256 256 5.02 21.78 22.64
372 hrnet_w32 1431.59 178.81 256 224 8.97 22.02 41.23
373 gluon_resnet152_v1c 1431.14 178.867 256 224 11.8 23.36 60.21
374 nf_seresnet101 1430.41 178.958 256 224 8.02 16.27 49.33
375 vgg16_bn 1430.18 178.985 256 224 15.5 13.56 138.37
376 resnetv2_152d 1427.39 179.335 256 224 11.8 23.36 60.2
377 resmlp_36_224 1426.81 179.41 256 224 8.91 16.33 44.69
378 resmlp_36_distilled_224 1426.22 179.484 256 224 8.91 16.33 44.69
379 nf_ecaresnet101 1423.4 179.839 256 224 8.01 16.27 44.55
380 mixnet_m 1415.37 180.859 256 224 0.36 8.19 5.01
381 gluon_resnet152_v1d 1405.52 182.126 256 224 11.8 23.36 60.21
382 ese_vovnet99b 1395.24 183.468 256 224 16.51 11.27 63.2
383 gmlp_s16_224 1389.58 184.216 256 224 4.42 15.1 19.42
384 tf_mixnet_m 1379.18 185.603 256 224 0.36 8.19 5.01
385 regnety_040 1367.09 187.246 256 224 4.0 12.29 20.65
386 vit_large_patch32_224 1365.88 187.414 256 224 15.39 13.3 306.54
387 hrnet_w18 1363.25 187.774 256 224 4.32 16.31 21.3
388 vit_base_r26_s32_224 1359.83 188.244 256 224 6.81 12.36 101.38
389 crossvit_15_240 1344.51 190.391 256 240 5.81 19.77 27.53
390 resnetv2_50d_evob 1343.28 190.567 256 224 4.33 11.92 25.59
391 ecaresnet50t 1341.42 190.83 256 320 8.82 24.13 25.57
392 mixer_b16_224 1341.1 190.877 256 224 12.62 14.53 59.88
393 mixer_b16_224_miil 1340.95 190.898 256 224 12.62 14.53 59.88
394 vgg19 1334.38 191.835 256 224 19.63 14.86 143.67
395 regnety_032 1331.17 192.299 256 288 5.29 18.61 19.44
396 cait_xxs24_224 1325.98 193.051 256 224 2.53 20.29 11.96
397 gluon_resnet152_v1s 1323.82 193.367 256 224 12.92 24.96 60.32
398 mixer_l32_224 1320.53 193.849 256 224 11.27 19.86 206.94
399 skresnext50_32x4d 1317.3 194.322 256 224 4.5 17.18 27.48
400 xception41 1314.21 194.782 256 299 9.28 39.86 26.97
401 crossvit_15_dagger_240 1313.12 194.944 256 240 6.13 20.43 28.21
402 densenet161 1297.86 197.236 256 224 7.79 11.06 28.68
403 dpn92 1283.16 199.496 256 224 6.54 18.21 37.67
404 efficientnet_lite4 1279.6 200.05 256 380 4.04 45.66 13.01
405 tresnet_m 1272.03 201.24 256 224 5.74 7.31 31.39
406 legacy_seresnet152 1268.99 201.723 256 224 11.33 22.08 66.82
407 dla169 1257.87 203.505 256 224 11.6 20.2 53.39
408 seresnet152 1252.8 204.325 256 224 11.57 22.61 66.82
409 swin_tiny_patch4_window7_224 1250.47 204.71 256 224 4.51 17.06 28.29
410 vit_base_patch32_384 1245.01 205.608 256 384 13.06 16.5 88.3
411 repvgg_b2 1240.95 206.281 256 224 20.45 12.9 89.02
412 twins_pcpvt_small 1237.75 206.812 256 224 3.83 18.08 24.11
413 inception_v4 1232.0 207.78 256 299 12.28 15.09 42.68
414 regnetx_080 1230.26 208.073 256 224 8.02 14.06 39.57
415 convit_small 1215.14 210.662 256 224 5.76 17.87 27.78
416 vgg19_bn 1208.18 211.876 256 224 19.66 14.86 143.68
417 xcit_nano_12_p8_224_dist 1199.12 213.478 256 224 2.16 15.71 3.05
418 xcit_nano_12_p8_224 1193.48 214.486 256 224 2.16 15.71 3.05
419 hrnet_w30 1182.47 216.483 256 224 8.15 21.21 37.71
420 tf_efficientnet_lite4 1173.72 218.096 256 380 4.04 45.66 13.01
421 resnetv2_50d_gn 1171.09 218.587 256 224 4.38 11.92 25.57
422 vit_tiny_patch16_384 1165.29 219.677 256 384 4.7 25.39 5.79
423 dla102x2 1162.53 220.196 256 224 9.34 29.91 41.28
424 regnetx_064 1154.46 221.737 256 224 6.49 16.37 26.21
425 vit_small_resnet50d_s16_224 1148.12 222.96 256 224 13.48 24.82 57.53
426 resnest50d 1142.3 224.096 256 224 5.4 14.36 27.48
427 xcit_tiny_12_p16_384_dist 1120.72 228.412 256 384 3.64 18.26 6.72
428 nf_regnet_b4 1118.05 228.958 256 384 4.7 28.61 30.21
429 mixnet_l 1111.69 230.267 256 224 0.58 10.84 7.33
430 efficientnetv2_s 1109.52 230.718 256 384 8.44 35.77 21.46
431 resnet50_gn 1084.83 235.97 256 224 4.14 11.11 25.56
432 tf_efficientnetv2_s 1082.56 236.462 256 384 8.44 35.77 21.46
433 tf_efficientnetv2_s_in21ft1k 1080.83 236.841 256 384 8.44 35.77 21.46
434 tf_mixnet_l 1080.28 236.961 256 224 0.58 10.84 7.33
435 wide_resnet101_2 1080.17 236.987 256 224 22.8 21.23 126.89
436 gluon_resnext101_64x4d 1075.35 238.048 256 224 15.52 31.21 83.46
437 dpn98 1075.31 238.059 256 224 11.73 25.2 61.57
438 eca_nfnet_l0 1073.57 238.443 256 288 7.12 17.29 24.14
439 resnext101_64x4d 1073.4 238.483 256 224 15.52 31.21 83.46
440 regnetz_d8 1072.66 238.647 256 320 6.19 37.08 23.37
441 convnext_small 1068.35 239.61 256 224 8.7 21.56 50.22
442 nfnet_l0 1067.0 239.911 256 288 7.13 17.29 35.07
443 resnet200 1062.15 241.009 256 224 15.07 32.19 64.67
444 resnetrs101 1051.01 243.563 256 288 13.56 28.53 63.62
445 inception_resnet_v2 1033.59 247.668 256 299 13.18 25.06 55.84
446 ens_adv_inception_resnet_v2 1033.26 247.747 256 299 13.18 25.06 55.84
447 efficientnetv2_rw_s 1032.33 247.972 256 384 8.72 38.03 23.94
448 nest_tiny 1020.47 250.854 256 224 5.83 25.48 17.06
449 coat_lite_small 1020.33 250.888 256 224 3.96 22.09 19.84
450 jx_nest_tiny 1008.4 253.854 256 224 5.83 25.48 17.06
451 vit_base_patch16_224_miil 996.21 256.96 256 224 17.58 23.9 86.54
452 crossvit_18_240 991.56 258.167 256 240 9.05 26.26 43.27
453 gluon_seresnext101_64x4d 990.21 258.518 256 224 15.53 31.25 88.23
454 swsl_resnext101_32x8d 977.82 261.793 256 224 16.48 31.21 88.79
455 ssl_resnext101_32x8d 977.78 261.803 256 224 16.48 31.21 88.79
456 resnext101_32x8d 976.71 262.092 256 224 16.48 31.21 88.79
457 resnet101d 976.38 262.181 256 320 16.48 34.77 44.57
458 ig_resnext101_32x8d 976.0 262.282 256 224 16.48 31.21 88.79
459 crossvit_18_dagger_240 971.37 263.533 256 240 9.5 27.03 44.27
460 regnetz_d32 970.52 263.764 256 320 9.33 37.08 27.58
461 vit_base_patch16_224_sam 969.72 263.98 256 224 17.58 23.9 86.57
462 deit_base_patch16_224 967.9 264.478 256 224 17.58 23.9 86.57
463 vit_base_patch16_224 966.99 264.723 256 224 17.58 23.9 86.57
464 deit_base_distilled_patch16_224 962.72 265.901 256 224 17.68 24.05 87.34
465 repvgg_b3 962.17 266.052 256 224 29.16 15.1 123.09
466 resnest50d_4s2x40d 957.24 267.424 256 224 4.4 17.94 30.42
467 gluon_xception65 952.59 268.729 256 299 13.96 52.48 39.92
468 efficientnet_b4 950.53 269.31 256 384 4.51 50.04 19.34
469 xception65 944.4 271.059 256 299 13.96 52.48 39.92
470 ese_vovnet39b_evos 937.23 273.135 256 224 7.07 6.74 24.58
471 hrnet_w40 919.71 278.336 256 224 12.75 25.29 57.56
472 seresnext101_32x8d 905.52 282.696 256 224 16.48 31.25 93.57
473 twins_pcpvt_base 904.7 282.951 256 224 6.68 25.25 43.83
474 tf_efficientnet_b4 899.69 284.527 256 380 4.49 49.49 19.34
475 tf_efficientnet_b4_ap 899.17 284.692 256 380 4.49 49.49 19.34
476 tf_efficientnet_b4_ns 898.81 284.808 256 380 4.49 49.49 19.34
477 xcit_small_24_p16_224_dist 897.88 285.104 256 224 9.1 23.64 47.67
478 xcit_small_24_p16_224 897.08 285.357 256 224 9.1 23.64 47.67
479 beit_base_patch16_224 894.85 286.068 256 224 17.58 23.9 86.53
480 cait_xxs36_224 891.78 287.055 256 224 3.77 30.34 17.3
481 twins_svt_base 885.5 289.087 256 224 8.59 26.33 56.07
482 regnety_080 865.03 295.931 256 224 8.0 17.97 39.18
483 hrnet_w48 862.23 296.892 256 224 17.34 28.56 77.47
484 xcit_tiny_12_p8_224_dist 851.38 300.676 256 224 4.81 23.6 6.71
485 xcit_tiny_12_p8_224 850.38 301.029 256 224 4.81 23.6 6.71
486 pit_b_224 831.73 307.778 256 224 12.42 32.94 73.76
487 pit_b_distilled_224 829.76 308.512 256 224 12.5 33.07 74.79
488 nfnet_f0s 822.42 311.265 256 256 12.62 18.05 71.49
489 nfnet_f0 820.01 312.18 256 256 12.62 18.05 71.49
490 swin_small_patch4_window7_224 808.39 316.666 256 224 8.77 27.47 49.61
491 repvgg_b2g4 805.1 317.961 256 224 12.63 12.9 61.76
492 resnetv2_50d_evos 799.75 320.087 256 224 4.33 11.92 25.59
493 convnext_base 795.35 321.858 256 224 15.38 28.75 88.59
494 convnext_base_in22ft1k 795.35 321.856 256 224 15.38 28.75 88.59
495 dpn131 790.54 323.815 256 224 16.09 32.97 79.25
496 regnetx_120 762.35 335.792 256 224 12.13 21.37 46.11
497 regnety_064 760.83 336.463 256 224 6.39 16.41 30.58
498 hrnet_w44 758.65 337.428 256 224 14.94 26.92 67.06
499 densenet264 753.54 339.715 256 224 12.95 12.8 72.69
500 mixnet_xl 746.77 342.795 256 224 0.93 14.57 11.9
501 cait_s24_224 746.74 342.81 256 224 9.35 40.58 46.92
502 regnety_120 732.77 349.348 256 224 12.14 21.38 51.82
503 dm_nfnet_f0 732.71 349.374 256 256 12.62 18.05 71.49
504 xception71 724.87 353.154 256 299 18.09 69.92 42.34
505 coat_tiny 707.44 361.857 256 224 4.35 27.2 5.5
506 resnet152d 694.55 368.574 256 320 24.08 47.67 60.21
507 dpn107 691.54 370.175 256 224 18.38 33.46 86.92
508 hrnet_w64 684.53 373.966 256 224 28.97 35.09 128.06
509 nest_small 681.18 375.805 256 224 10.35 40.04 38.35
510 vit_large_r50_s32_224 676.89 378.187 256 224 19.58 24.41 328.99
511 jx_nest_small 675.63 378.895 256 224 10.35 40.04 38.35
512 twins_svt_large 674.75 379.385 256 224 15.15 35.1 99.27
513 seresnet200d 673.13 380.295 256 256 20.01 43.15 71.86
514 ecaresnet200d 672.08 380.893 256 256 20.0 43.15 64.69
515 repvgg_b3g4 671.77 381.071 256 224 17.89 15.1 83.83
516 cspresnext50_iabn 667.96 383.242 256 256 4.02 15.86 20.57
517 gmlp_b16_224 667.23 383.66 256 224 15.78 30.21 73.08
518 crossvit_base_240 662.98 386.123 256 240 21.22 36.33 105.03
519 xcit_medium_24_p16_224_dist 661.18 387.174 256 224 16.13 31.71 84.4
520 xcit_medium_24_p16_224 660.34 387.669 256 224 16.13 31.71 84.4
521 tresnet_l 657.41 389.391 256 224 10.88 11.9 55.99
522 twins_pcpvt_large 657.27 389.474 256 224 9.84 35.82 60.99
523 gluon_senet154 656.15 390.143 256 224 20.77 38.69 115.09
524 senet154 655.06 390.791 256 224 20.77 38.69 115.09
525 legacy_senet154 654.75 390.977 256 224 20.77 38.69 115.09
526 convit_base 638.4 400.989 256 224 17.52 31.77 86.54
527 swin_base_patch4_window7_224 617.48 414.571 256 224 15.47 36.63 87.77
528 ese_vovnet99b_iabn 611.75 418.46 256 224 16.49 11.27 63.2
529 regnetx_160 608.26 420.859 256 224 15.99 25.52 54.28
530 cspdarknet53_iabn 603.85 423.936 256 256 6.53 16.81 27.64
531 resnetrs152 602.36 424.984 256 320 24.34 48.14 86.62
532 seresnet152d 600.43 426.343 256 320 24.09 47.72 66.84
533 xcit_tiny_24_p16_384_dist 596.99 428.807 256 384 6.87 34.29 12.12
534 xcit_small_12_p16_384_dist 594.31 430.737 256 384 14.14 36.51 26.25
535 coat_mini 592.69 431.92 256 224 6.82 33.68 10.34
536 vit_small_patch16_384 592.64 431.956 256 384 15.52 50.78 22.2
537 regnetz_e8 575.64 444.705 256 320 15.46 63.94 57.7
538 convmixer_768_32 563.29 454.463 256 224 19.55 25.95 21.11
539 resnest101e 556.07 460.362 256 256 13.38 28.66 48.28
540 efficientnetv2_m 555.39 460.921 256 416 18.6 67.5 54.14
541 vit_base_r50_s16_224 554.34 461.796 256 224 21.66 35.29 98.66
542 vit_small_r26_s32_384 549.37 465.972 256 384 10.43 29.85 36.47
543 nf_regnet_b5 533.77 479.592 256 456 11.7 61.95 49.74
544 seresnet269d 524.13 488.41 256 256 26.59 53.6 113.67
545 nest_base 514.16 497.89 256 224 17.96 53.39 67.72
546 jx_nest_base 510.9 501.061 256 224 17.96 53.39 67.72
547 resnet200d 509.84 502.105 256 320 31.25 67.33 64.69
548 tnt_s_patch16_224 490.75 521.641 256 224 5.24 24.37 23.76
549 mixnet_xxl 486.35 526.355 256 224 2.04 23.43 23.96
550 tresnet_xl 476.44 537.307 256 224 15.17 15.34 78.44
551 convnext_large 476.21 537.564 256 224 34.4 43.13 197.77
552 eca_nfnet_l1 475.22 538.689 256 320 14.92 34.42 41.41
553 convnext_large_in22ft1k 475.1 538.817 256 224 34.4 43.13 197.77
554 efficientnetv2_rw_m 464.7 550.875 256 416 21.49 79.62 53.24
555 efficientnet_b5 456.59 560.666 256 456 10.46 98.86 30.39
556 xcit_small_12_p8_224 453.25 564.8 256 224 18.69 47.21 26.21
557 xcit_small_12_p8_224_dist 452.33 565.946 256 224 18.69 47.21 26.21
558 xcit_tiny_24_p8_224 450.37 568.411 256 224 9.21 45.39 12.11
559 mixer_l16_224 449.08 570.035 256 224 44.6 41.69 208.2
560 xcit_tiny_24_p8_224_dist 448.81 570.379 256 224 9.21 45.39 12.11
561 halonet_h1 448.78 570.426 256 256 3.0 51.17 8.1
562 vit_large_patch32_384 441.35 580.019 256 384 45.31 43.86 306.63
563 resnetrs200 436.51 586.457 256 320 31.51 67.81 93.21
564 tf_efficientnet_b5 434.35 589.365 256 456 10.46 98.86 30.39
565 tf_efficientnet_b5_ap 433.75 590.18 256 456 10.46 98.86 30.39
566 tf_efficientnet_b5_ns 433.48 590.557 256 456 10.46 98.86 30.39
567 tf_efficientnetv2_m_in21ft1k 410.56 623.518 256 480 24.76 89.84 54.14
568 tf_efficientnetv2_m 410.47 623.661 256 480 24.76 89.84 54.14
569 xcit_nano_12_p8_384_dist 410.15 624.154 256 384 6.34 46.08 3.05
570 regnety_320 406.99 628.99 256 224 32.34 30.26 145.05
571 regnety_160 405.46 631.367 256 288 26.37 38.07 83.59
572 xcit_large_24_p16_224_dist 400.4 639.34 256 224 35.86 47.27 189.1
573 xcit_large_24_p16_224 400.19 639.684 256 224 35.86 47.27 189.1
574 regnetz_d8_evob 388.06 659.67 256 320 6.12 37.08 23.41
575 tnt_b_patch16_224 385.58 663.922 256 224 14.09 39.01 65.41
576 swin_large_patch4_window7_224 383.73 667.122 256 224 34.53 54.94 196.53
577 resnetv2_50x1_bitm 374.41 683.729 256 448 16.62 44.46 25.55
578 ssl_resnext101_32x16d 338.57 756.102 256 224 36.27 51.18 194.03
579 swsl_resnext101_32x16d 338.32 756.674 256 224 36.27 51.18 194.03
580 ig_resnext101_32x16d 338.18 756.969 256 224 36.27 51.18 194.03
581 vit_large_patch16_224 326.85 783.223 256 224 61.6 63.52 304.33
582 convnext_xlarge_in22ft1k 321.97 795.088 256 224 60.97 57.5 350.2
583 tresnet_m_448 321.59 796.023 256 448 22.94 29.21 31.39
584 xcit_small_24_p16_384_dist 315.9 810.38 256 384 26.72 68.58 47.67
585 crossvit_15_dagger_408 307.27 833.13 256 408 21.45 95.05 28.5
586 nasnetalarge 305.09 839.069 256 331 23.89 90.56 88.75
587 beit_large_patch16_224 301.03 850.396 256 224 61.6 63.52 304.43
588 pnasnet5large 294.97 867.879 256 331 25.04 92.89 86.06
589 xcit_tiny_12_p8_384_dist 290.12 882.382 256 384 14.13 69.14 6.71
590 ecaresnet269d 282.41 906.484 256 352 50.25 101.25 102.09
591 nfnet_f1s 278.33 919.751 256 320 35.97 46.77 132.63
592 nfnet_f1 277.93 921.079 256 320 35.97 46.77 132.63
593 resnetrs270 274.74 931.762 256 352 51.13 105.48 129.86
594 convnext_base_384_in22ft1k 274.29 933.296 256 384 45.2 84.49 88.59
595 resnetv2_152x2_bit_teacher 268.11 954.835 256 224 46.95 45.11 236.34
596 efficientnet_b6 261.2 490.039 128 528 19.4 167.39 43.04
597 regnetx_320 257.17 995.447 256 224 31.81 36.3 107.81
598 vit_base_patch16_384 256.27 998.923 256 384 55.54 101.56 86.86
599 deit_base_patch16_384 256.23 999.078 256 384 55.54 101.56 86.86
600 deit_base_distilled_patch16_384 253.03 1011.71 256 384 55.65 101.82 87.63
601 dm_nfnet_f1 251.34 1018.532 256 320 35.97 46.77 132.63
602 tf_efficientnet_b6_ns 249.3 513.425 128 528 19.4 167.39 43.04
603 tf_efficientnet_b6_ap 249.28 513.468 128 528 19.4 167.39 43.04
604 tf_efficientnet_b6 249.27 513.475 128 528 19.4 167.39 43.04
605 cait_xxs24_384 240.96 1062.389 256 384 9.63 122.66 12.03
606 resnetv2_101x1_bitm 237.63 1077.288 256 448 31.65 64.93 44.54
607 xcit_small_24_p8_224 236.59 1082.038 256 224 35.81 90.78 47.63
608 xcit_small_24_p8_224_dist 236.51 1082.395 256 224 35.81 90.78 47.63
609 efficientnetv2_l 230.67 1109.79 256 480 56.4 157.99 118.52
610 xcit_medium_24_p16_384_dist 229.71 1114.433 256 384 47.39 91.64 84.4
611 crossvit_18_dagger_408 228.55 560.041 128 408 32.47 124.87 44.61
612 tf_efficientnetv2_l 228.05 1122.527 256 480 56.4 157.99 118.52
613 eca_nfnet_l2 227.63 1124.623 256 384 30.05 68.28 56.72
614 tf_efficientnetv2_l_in21ft1k 227.22 1126.624 256 480 56.4 157.99 118.52
615 vit_large_r50_s32_384 223.83 1143.726 256 384 57.43 76.52 329.09
616 beit_base_patch16_384 221.26 1157.006 256 384 55.54 101.56 86.74
617 regnetz_d8_evos 211.13 1212.504 256 320 7.03 38.92 23.46
618 resmlp_big_24_224 198.32 1290.833 256 224 100.23 87.31 129.14
619 resmlp_big_24_224_in22ft1k 197.59 1295.599 256 224 100.23 87.31 129.14
620 resmlp_big_24_distilled_224 197.33 1297.278 256 224 100.23 87.31 129.14
621 resnest200e 191.82 1334.548 256 320 35.69 82.78 70.2
622 resnetrs350 182.63 1401.718 256 384 77.59 154.74 163.96
623 swin_base_patch4_window12_384 171.61 745.879 128 384 47.19 134.78 87.9
624 cait_xs24_384 171.6 1491.866 256 384 19.28 183.98 26.67
625 xcit_medium_24_p8_224 171.09 1496.317 256 224 63.53 121.23 84.32
626 xcit_medium_24_p8_224_dist 171.08 1496.368 256 224 63.53 121.23 84.32
627 vit_base_patch8_224 170.14 1504.628 256 224 78.22 161.69 86.58
628 tresnet_l_448 163.98 1561.153 256 448 43.5 47.56 55.99
629 convnext_large_384_in22ft1k 162.15 1578.727 256 384 101.09 126.74 197.77
630 cait_xxs36_384 161.38 1586.306 256 384 14.35 183.7 17.37
631 vit_base_r50_s16_384 161.14 1588.636 256 384 67.43 135.03 98.95
632 vit_base_resnet50_384 161.06 1589.462 256 384 67.43 135.03 98.95
633 nfnet_f2s 156.52 1635.579 256 352 63.22 79.06 193.78
634 efficientnet_b7 156.11 409.945 64 600 38.33 289.94 66.35
635 nfnet_f2 155.79 1643.217 256 352 63.22 79.06 193.78
636 xcit_tiny_24_p8_384_dist 152.9 1674.228 256 384 27.05 132.95 12.11
637 densenet264d_iabn 151.23 1692.723 256 224 13.47 14.0 72.74
638 xcit_small_12_p8_384_dist 150.53 1700.627 256 384 54.92 138.29 26.21
639 tf_efficientnet_b7_ns 150.44 425.41 64 600 38.33 289.94 66.35
640 tf_efficientnet_b7_ap 150.35 425.668 64 600 38.33 289.94 66.35
641 tf_efficientnet_b7 150.33 425.703 64 600 38.33 289.94 66.35
642 efficientnetv2_xl 146.15 1751.589 256 512 93.85 247.32 208.12
643 tf_efficientnetv2_xl_in21ft1k 144.77 1768.278 256 512 93.85 247.32 208.12
644 dm_nfnet_f2 141.71 1806.539 256 352 63.22 79.06 193.78
645 xcit_large_24_p16_384_dist 138.32 1850.744 256 384 105.35 137.17 189.1
646 cait_s24_384 132.94 1925.665 256 384 32.17 245.31 47.06
647 resnetrs420 132.69 1929.303 256 416 108.45 213.79 191.89
648 vit_huge_patch14_224 131.53 1946.246 256 224 167.4 139.41 632.05
649 eca_nfnet_l3 127.14 2013.536 256 448 52.55 118.4 72.04
650 tresnet_xl_448 120.79 2119.391 256 448 60.65 61.31 78.44
651 efficientnet_cc_b0_8e 117.25 8.519 1 224 0.42 9.42 24.01
652 efficientnet_cc_b0_4e 114.89 8.693 1 224 0.41 9.42 13.31
653 convnext_xlarge_384_in22ft1k 110.42 1159.152 128 384 179.18 168.99 350.2
654 tf_efficientnet_cc_b0_8e 109.13 9.153 1 224 0.42 9.42 24.01
655 swin_large_patch4_window12_384 108.07 1184.415 128 384 104.08 202.16 196.74
656 tf_efficientnet_cc_b0_4e 106.38 9.39 1 224 0.41 9.42 13.31
657 xcit_large_24_p8_224 103.21 2480.29 256 224 141.23 181.56 188.93
658 xcit_large_24_p8_224_dist 103.09 2483.315 256 224 141.23 181.56 188.93
659 efficientnet_b8 95.86 667.618 64 672 63.48 442.89 87.41
660 resnetv2_152x2_bit_teacher_384 93.91 2726.129 256 384 136.16 132.56 236.34
661 tf_efficientnet_b8 93.03 687.97 64 672 63.48 442.89 87.41
662 tf_efficientnet_b8_ap 92.99 688.234 64 672 63.48 442.89 87.41
663 resnetv2_50x3_bitm 90.18 1419.305 128 448 145.7 133.37 217.32
664 resnest269e 90.1 2841.126 256 416 77.69 171.98 110.93
665 cait_s36_384 88.82 2882.347 256 384 47.99 367.4 68.37
666 vit_large_patch16_384 88.44 2894.698 256 384 191.21 270.24 304.72
667 vit_giant_patch14_224 87.02 2941.98 256 224 267.18 192.64 1012.61
668 nfnet_f3s 85.07 3009.339 256 416 115.58 141.78 254.92
669 nfnet_f3 84.65 3024.233 256 416 115.58 141.78 254.92
670 efficientnet_cc_b1_8e 83.46 11.971 1 240 0.75 15.44 39.72
671 convmixer_1024_20_ks9_p14 81.44 3143.542 256 224 5.55 5.51 24.38
672 xcit_small_24_p8_384_dist 78.98 3241.259 256 384 105.24 265.91 47.63
673 beit_large_patch16_384 77.03 3323.174 256 384 191.21 270.24 305.0
674 dm_nfnet_f3 76.93 3327.777 256 416 115.58 141.78 254.92
675 tf_efficientnet_cc_b1_8e 76.09 13.131 1 240 0.75 15.44 39.72
676 resnetv2_152x2_bitm 68.83 1859.681 128 448 184.99 180.43 236.34
677 xcit_medium_24_p8_384_dist 58.64 4365.679 256 384 186.67 354.73 84.32
678 resnetv2_101x3_bitm 55.09 2323.244 128 448 280.33 194.78 387.93
679 vit_gigantic_patch14_224 52.92 4837.231 256 224 483.95 275.37 1844.44
680 nfnet_f4s 45.21 5662.39 256 512 216.26 262.26 316.07
681 nfnet_f4 45.13 5672.08 256 512 216.26 262.26 316.07
682 dm_nfnet_f4 41.12 6225.397 256 512 216.26 262.26 316.07
683 xcit_large_24_p8_384_dist 35.18 3638.243 128 384 415.0 531.82 188.93
684 nfnet_f5s 33.4 7663.628 256 544 290.97 349.71 377.21
685 nfnet_f5 33.22 7705.647 256 544 290.97 349.71 377.21
686 beit_large_patch16_512 31.23 2049.459 64 512 362.24 656.39 305.67
687 cait_m36_384 30.6 8365.416 256 384 173.11 734.81 271.22
688 dm_nfnet_f5 30.51 8392.012 256 544 290.97 349.71 377.21
689 nfnet_f6s 24.87 10292.837 256 576 378.69 452.2 438.36
690 nfnet_f6 24.74 10346.138 256 576 378.69 452.2 438.36
691 dm_nfnet_f6 23.05 11108.286 256 576 378.69 452.2 438.36
692 nfnet_f7s 19.54 13099.713 256 608 480.39 570.85 499.5
693 nfnet_f7 19.47 13146.917 256 608 480.39 570.85 499.5
694 resnetv2_152x4_bitm 17.96 3562.952 64 480 844.84 414.26 936.53
695 cait_m48_448 13.23 9672.182 128 448 329.41 1708.23 356.46
696 convmixer_1536_20 13.15 19467.539 256 224 48.68 33.03 51.63

@ -0,0 +1,703 @@
model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,8788.08,57.613,512,106,2.04
mobilenetv3_small_050,6451.43,78.69,512,224,1.59
tf_mobilenetv3_small_minimal_100,6231.46,81.639,512,224,2.04
levit_128s,5345.65,94.962,512,224,7.78
lcnet_035,5326.51,95.668,512,224,1.64
mobilenetv3_small_075,5149.15,98.754,512,224,2.04
lcnet_050,4879.43,104.466,512,224,1.88
tf_mobilenetv3_small_075,4839.2,105.1,512,224,2.04
mobilenetv3_small_100,4741.69,107.315,512,224,2.54
tf_mobilenetv3_small_100,4474.54,113.738,512,224,2.54
tinynet_d,4465.05,113.942,512,152,2.34
mixer_s32_224,3957.52,128.859,512,224,19.1
levit_128,3868.72,131.359,512,224,9.21
lcnet_075,3674.09,138.887,512,224,2.36
vit_small_patch32_224,3657.34,139.252,512,224,22.88
vit_tiny_r_s16_p8_224,3407.13,149.506,512,224,6.34
regnetx_002,3372.01,151.204,512,224,2.68
levit_192,3357.19,151.505,512,224,10.95
mnasnet_small,3158.42,161.274,512,224,2.03
regnety_002,3119.2,163.263,512,224,3.16
lcnet_100,3075.72,165.994,512,224,2.95
mobilenetv2_035,3007.95,169.496,512,224,1.68
gernet_s,2901.28,175.82,512,224,8.17
gluon_resnet18_v1b,2671.91,191.302,512,224,11.69
swsl_resnet18,2669.15,191.488,512,224,11.69
ssl_resnet18,2668.71,191.54,512,224,11.69
resnet18,2662.83,191.947,512,224,11.69
mobilenetv2_050,2596.25,196.482,512,224,1.97
levit_256,2559.67,199.033,512,224,18.89
regnetx_004,2499.65,203.84,512,224,5.16
mnasnet_050,2494.39,204.524,512,224,2.22
semnasnet_050,2494.01,204.433,512,224,2.08
seresnet18,2463.7,207.356,512,224,11.78
ese_vovnet19b_slim_dw,2437.98,209.545,512,224,1.9
legacy_seresnet18,2413.99,211.632,512,224,11.78
tinynet_c,2389.53,213.326,512,184,2.46
tf_mobilenetv3_large_minimal_100,2278.25,224.06,512,224,3.92
lcnet_150,2248.73,227.214,512,224,4.5
mobilenetv3_large_075,2139.42,238.494,512,224,3.99
ghostnet_050,2137.33,238.296,512,224,2.59
resnet18d,2046.03,249.892,512,224,11.71
tf_mobilenetv3_large_075,2027.78,251.65,512,224,3.99
regnetx_006,2023.49,252.29,512,224,6.2
ese_vovnet19b_slim,1970.7,259.464,512,224,3.17
mobilenetv3_rw,1940.58,263.024,512,224,5.48
mobilenetv3_large_100,1936.66,263.562,512,224,5.48
mobilenetv3_large_100_miil,1936.5,263.563,512,224,5.48
mnasnet_075,1879.23,271.718,512,224,3.17
mobilenetv2_075,1868.15,273.318,512,224,2.64
tf_efficientnetv2_b0,1853.32,275.137,512,224,7.14
tf_mobilenetv3_large_100,1838.66,277.647,512,224,5.48
regnety_004,1819.61,280.334,512,224,4.34
resnetblur18,1808.21,282.828,512,224,11.69
semnasnet_075,1791.94,284.855,512,224,2.91
regnety_006,1780.58,286.558,512,224,6.06
mobilenetv2_100,1744.9,292.687,512,224,3.5
skresnet18,1742.33,293.29,512,224,11.96
mnasnet_b1,1686.64,302.825,512,224,4.38
mnasnet_100,1686.2,302.919,512,224,4.38
deit_tiny_patch16_224,1678.5,304.281,512,224,5.72
vit_tiny_patch16_224,1673.2,305.238,512,224,5.72
tinynet_b,1669.81,305.621,512,188,3.73
hardcorenas_a,1656.9,308.354,512,224,5.26
deit_tiny_distilled_patch16_224,1654.85,308.608,512,224,5.91
hardcorenas_b,1648.45,309.73,512,224,5.18
regnetx_008,1647.09,310.092,512,224,7.26
mnasnet_a1,1641.47,311.08,512,224,3.89
semnasnet_100,1640.14,311.319,512,224,3.89
levit_384,1616.42,315.722,512,224,39.13
gluon_resnet34_v1b,1598.5,319.781,512,224,21.8
resnet34,1593.87,320.693,512,224,21.8
tv_resnet34,1592.81,320.898,512,224,21.8
mixer_b32_224,1579.25,323.449,512,224,60.29
hardcorenas_c,1569.06,325.387,512,224,5.52
visformer_tiny,1563.94,326.764,512,224,10.32
spnasnet_100,1551.19,329.16,512,224,4.42
pit_ti_distilled_224,1547.24,330.092,512,224,5.1
pit_ti_224,1546.92,330.186,512,224,4.85
hardcorenas_d,1543.77,330.549,512,224,7.5
ghostnet_100,1541.6,330.868,512,224,5.18
vit_base_patch32_224,1529.64,333.95,512,224,88.22
vit_base_patch32_224_sam,1527.96,334.33,512,224,88.22
regnety_008,1525.57,334.693,512,224,6.26
resmlp_12_224,1513.1,337.634,512,224,15.35
resmlp_12_distilled_224,1513.05,337.643,512,224,15.35
seresnet34,1461.69,349.453,512,224,21.96
nf_regnet_b0,1450.04,351.967,512,256,8.76
legacy_seresnet34,1428.59,357.543,512,224,21.96
gernet_m,1427.9,357.926,512,224,21.14
ese_vovnet19b_dw,1411.33,362.322,512,224,6.54
nf_resnet26,1408.11,363.145,512,224,16.0
dla46_c,1390.48,367.526,512,224,1.3
tinynet_a,1383.86,368.826,512,192,6.19
efficientnet_lite0,1368.92,373.303,512,224,4.65
mobilenetv2_110d,1368.66,279.598,384,224,4.52
rexnetr_100,1367.14,279.805,384,224,4.88
rexnet_100,1361.11,281.051,384,224,4.8
resnet34d,1349.98,378.71,512,224,21.82
xcit_nano_12_p16_224_dist,1340.16,380.375,512,224,3.05
xcit_nano_12_p16_224,1338.66,380.8,512,224,3.05
hardcorenas_f,1338.06,381.608,512,224,8.2
selecsls42,1328.24,384.874,512,224,30.35
ghostnet_130,1326.71,384.661,512,224,7.36
selecsls42b,1324.18,386.065,512,224,32.46
mixer_s16_224,1308.0,390.939,512,224,18.53
resnet26,1305.0,391.896,512,224,16.0
tf_efficientnet_lite0,1300.06,393.134,512,224,4.65
hardcorenas_e,1287.59,396.566,512,224,8.07
hrnet_w18_small,1275.09,400.255,512,224,13.19
pit_xs_distilled_224,1245.81,410.186,512,224,11.0
pit_xs_224,1244.79,410.521,512,224,10.62
mnasnet_140,1243.07,411.14,512,224,7.12
fbnetc_100,1241.42,411.527,512,224,5.57
mobilenetv2_140,1236.2,309.893,384,224,6.11
vit_small_patch32_384,1210.76,422.138,512,384,22.92
efficientnet_b0,1207.97,316.872,384,224,5.29
semnasnet_140,1199.21,426.076,512,224,6.11
nf_seresnet26,1183.4,432.047,512,224,17.4
tf_efficientnetv2_b1,1182.26,323.455,384,240,8.14
vit_tiny_r_s16_p8_384,1176.2,325.707,384,384,6.36
repvgg_b0,1166.7,437.858,512,224,15.82
gmixer_12_224,1164.26,439.045,512,224,12.7
tf_efficientnet_b0_ns,1155.19,331.423,384,224,5.29
tf_efficientnet_b0,1155.11,331.42,384,224,5.29
tf_efficientnet_b0_ap,1154.21,331.702,384,224,5.29
efficientnet_b1_pruned,1141.45,447.176,512,240,6.33
selecsls60,1132.77,451.165,512,224,30.67
selecsls60b,1130.1,452.225,512,224,32.77
resnet26d,1110.35,460.655,512,224,16.01
nf_ecaresnet26,1108.5,461.384,512,224,16.0
rexnetr_130,1084.92,234.909,256,224,7.61
mixnet_s,1079.62,473.081,512,224,4.13
dla34,1055.21,484.649,512,224,15.74
dla46x_c,1044.64,489.413,512,224,1.07
rexnet_130,1038.12,245.54,256,224,7.56
regnetx_016,1027.51,497.437,512,224,9.19
mobilenetv2_120d,1026.61,248.294,256,224,5.83
ecaresnet50d_pruned,1019.51,501.3,512,224,19.94
tf_mixnet_s,1018.74,501.408,512,224,4.13
skresnet34,1008.77,506.519,512,224,22.28
dla60x_c,999.33,511.452,512,224,1.32
gernet_l,994.19,514.18,512,256,31.08
xcit_tiny_12_p16_224,974.89,523.578,512,224,6.72
xcit_tiny_12_p16_224_dist,974.56,523.762,512,224,6.72
rexnetr_150,962.03,265.059,256,224,9.78
crossvit_tiny_240,946.09,404.397,384,240,7.01
crossvit_9_240,942.0,406.34,384,240,8.55
rexnet_150,920.27,277.114,256,224,9.73
repvgg_a2,916.8,557.644,512,224,28.21
legacy_seresnext26_32x4d,911.29,561.256,512,224,16.79
efficientnet_lite1,907.6,281.167,256,240,5.42
crossvit_9_dagger_240,907.56,421.794,384,240,8.78
regnety_016,905.38,563.868,512,224,11.2
convit_tiny,905.04,423.366,384,224,5.71
gmlp_ti16_224,895.37,427.435,384,224,5.87
efficientnet_es_pruned,894.51,571.683,512,224,5.44
efficientnet_es,894.06,571.94,512,224,5.44
resnest14d,890.17,574.739,512,224,10.61
deit_small_patch16_224,879.08,436.051,384,224,22.05
vit_small_patch16_224,877.74,436.745,384,224,22.05
tf_efficientnet_es,877.36,582.875,512,224,5.44
resnetv2_50,874.29,584.904,512,224,25.55
resnext26ts,872.05,439.874,384,256,10.3
tf_efficientnet_lite1,870.75,293.088,256,240,5.42
resnet26t,867.6,589.639,512,256,16.01
tf_efficientnetv2_b2,866.58,294.02,256,260,10.1
deit_small_distilled_patch16_224,865.21,443.046,384,224,22.44
nf_regnet_b1,863.6,591.527,512,288,10.22
nf_regnet_b2,857.0,595.977,512,272,14.31
eca_resnext26ts,843.14,303.098,256,256,10.3
seresnext26ts,838.62,304.653,256,256,10.39
resnet50,826.48,463.878,384,224,25.56
ssl_resnet50,826.14,464.04,384,224,25.56
botnet26t_256,825.99,464.363,384,256,12.49
tv_resnet50,825.99,464.131,384,224,25.56
swsl_resnet50,825.61,464.367,384,224,25.56
gluon_resnet50_v1b,825.26,464.548,384,224,25.56
gcresnext26ts,820.94,311.077,256,256,10.48
seresnext26t_32x4d,819.08,468.216,384,224,16.81
seresnext26tn_32x4d,818.65,468.46,384,224,16.81
seresnext26d_32x4d,816.35,469.756,384,224,16.81
pit_s_224,814.17,313.637,256,224,23.46
pit_s_distilled_224,813.41,313.92,256,224,24.04
vgg11,798.47,641.065,512,224,132.86
halonet26t,796.75,481.425,384,256,12.48
eca_botnext26ts_256,794.14,321.819,256,256,10.59
resnetv2_50t,783.96,652.33,512,224,25.57
vit_small_resnet26d_224,783.75,488.981,384,224,63.61
ecaresnext26t_32x4d,782.53,490.179,384,224,15.41
ecaresnext50t_32x4d,782.45,490.233,384,224,15.41
resnetv2_50d,781.21,654.649,512,224,25.57
tresnet_m,773.27,659.381,512,224,31.39
fbnetv3_d,771.82,329.985,256,256,10.31
eca_halonext26ts,770.01,331.906,256,256,10.76
cspresnet50,766.64,500.094,384,256,21.62
fbnetv3_b,765.95,499.824,384,256,8.6
ecaresnet101d_pruned,765.08,667.536,512,224,24.88
vovnet39a,764.37,669.255,512,224,22.6
gluon_resnet50_v1c,761.03,503.811,384,224,25.58
cspresnext50,757.53,506.115,384,224,20.57
efficientnet_cc_b0_4e,755.85,506.911,384,224,13.31
ecaresnetlight,754.13,678.075,512,224,30.16
efficientnet_cc_b0_8e,750.91,510.273,384,224,24.01
mixnet_m,749.54,510.912,384,224,5.01
efficientnet_b2_pruned,748.23,340.751,256,260,8.31
resnet50t,745.06,514.605,384,224,25.57
resmlp_24_distilled_224,744.01,342.726,256,224,30.02
gluon_resnet50_v1d,743.98,515.37,384,224,25.58
resnet50d,743.76,515.521,384,224,25.58
resmlp_24_224,743.43,342.965,256,224,30.02
legacy_seresnet50,739.53,518.192,384,224,28.09
resnet32ts,736.71,346.942,256,256,17.96
ese_vovnet39b,733.2,523.109,384,224,24.57
tf_efficientnet_cc_b0_4e,732.92,522.856,384,224,13.31
tf_efficientnet_cc_b0_8e,731.1,524.143,384,224,24.01
dpn68b,728.18,526.241,384,224,12.61
resnet33ts,727.42,351.376,256,256,19.68
selecsls84,719.68,710.264,512,224,50.95
nf_seresnet50,718.4,533.452,384,224,28.09
rexnetr_200,715.44,267.313,192,224,16.52
visformer_small,713.57,537.5,384,224,40.22
res2net50_48w_2s,712.35,538.286,384,224,25.29
tf_mixnet_m,711.23,538.464,384,224,5.01
dpn68,711.07,539.016,384,224,12.61
seresnet50,710.55,539.38,384,224,28.09
lambda_resnet26t,708.6,541.35,384,256,10.96
bat_resnext26ts,706.1,361.453,256,256,10.73
tf_efficientnet_b1,705.85,361.254,256,240,7.79
tf_efficientnet_b1_ns,705.53,361.468,256,240,7.79
tf_efficientnet_b1_ap,705.49,361.504,256,240,7.79
seresnet33ts,700.84,364.51,256,256,19.78
eca_resnet33ts,700.22,364.953,256,256,19.68
resnetblur50,694.99,551.777,384,224,25.56
cspresnet50d,694.84,551.823,384,256,21.64
rexnet_200,693.57,275.752,192,224,16.37
cspresnet50w,686.88,558.19,384,256,28.12
eca_vovnet39b,683.56,748.398,512,224,22.6
densenet121,682.9,373.209,256,224,7.98
dla60,682.79,561.488,384,224,22.04
efficientnet_b1,682.7,373.555,256,256,7.79
tv_densenet121,681.14,374.178,256,224,7.98
gcresnet33ts,680.99,374.966,256,256,19.88
efficientnet_lite2,680.43,375.329,256,260,6.09
resnest26d,672.85,570.012,384,224,17.07
resnetaa50d,669.52,572.758,384,224,25.58
vgg11_bn,669.19,382.326,256,224,132.87
gluon_resnet50_v1s,664.71,576.903,384,224,25.68
nf_ecaresnet50,663.54,577.843,384,224,25.56
hrnet_w18_small_v2,663.05,769.959,512,224,15.6
tf_efficientnet_lite2,656.12,389.28,256,260,6.09
vit_small_r26_s32_224,654.12,390.216,256,224,36.43
lambda_resnet26rpt_256,650.73,196.144,128,256,10.99
vit_base_resnet26d_224,650.56,589.099,384,224,101.4
seresnet50t,645.62,395.439,256,224,28.1
gluon_resnext50_32x4d,643.1,596.344,384,224,25.03
ssl_resnext50_32x4d,642.69,596.753,384,224,25.03
resnext50_32x4d,642.56,596.871,384,224,25.03
tv_resnext50_32x4d,642.26,597.116,384,224,25.03
swsl_resnext50_32x4d,642.22,597.166,384,224,25.03
haloregnetz_b,639.45,398.971,256,224,11.68
resnetrs50,639.32,599.548,384,224,35.69
densenet121d,637.87,399.654,256,224,8.0
regnetx_032,635.48,603.11,384,224,15.3
resnetblur50d,634.6,402.62,256,224,25.58
res2net50_26w_4s,625.68,407.946,256,224,25.7
skresnet50,620.44,411.364,256,224,25.8
ese_vovnet57b,618.38,620.149,384,224,38.61
densenetblur121d,611.64,416.815,256,224,8.0
ecaresnet50d,601.62,424.614,256,224,25.58
adv_inception_v3,592.83,646.406,384,299,23.83
resnext50d_32x4d,590.58,432.684,256,224,25.05
gluon_inception_v3,589.45,650.157,384,299,23.83
inception_v3,588.55,651.172,384,299,23.83
tf_inception_v3,588.36,651.336,384,299,23.83
seresnetaa50d,587.38,434.758,256,224,28.11
sehalonet33ts,585.23,436.688,256,256,13.69
coat_lite_tiny,580.33,660.876,384,224,5.72
gmixer_24_224,577.9,441.611,256,224,24.72
gcresnet50t,577.25,442.093,256,256,25.9
efficientnet_b3_pruned,576.96,442.1,256,300,9.86
mixnet_l,576.91,442.34,256,224,7.33
resnetv2_50x1_bit_distilled,575.09,333.113,192,224,25.55
skresnet50d,572.85,445.576,256,224,25.82
vovnet57a,569.57,898.133,512,224,36.64
res2next50,568.73,448.924,256,224,24.67
seresnext50_32x4d,567.85,449.777,256,224,27.56
gluon_seresnext50_32x4d,567.27,450.224,256,224,27.56
cspresnext50_iabn,565.5,676.906,384,256,20.57
legacy_seresnext50_32x4d,565.44,451.632,256,224,27.56
resnest50d_1s4x24d,561.18,454.967,256,224,25.68
res2net50_14w_8s,560.86,454.355,256,224,25.06
repvgg_b1g4,560.47,912.475,512,224,39.97
densenet169,558.29,456.216,256,224,14.15
convnext_tiny,558.25,457.708,256,224,28.59
convnext_tiny_hnf,555.94,459.59,256,224,28.59
coat_lite_mini,554.95,691.16,384,224,11.01
regnety_040,554.71,690.835,384,224,20.65
dla60_res2net,550.65,463.555,256,224,20.85
darknet53,549.0,465.546,256,256,41.61
vgg13,548.68,699.681,384,224,133.05
tf_mixnet_l,547.03,466.516,256,224,7.33
dla60x,544.8,469.001,256,224,17.35
efficientnet_em,542.04,707.524,384,240,6.9
regnetx_040,541.76,707.76,384,224,22.12
nf_resnet101,541.63,707.493,384,224,44.55
tf_efficientnet_em,539.79,473.368,256,240,6.9
nf_regnet_b3,535.57,476.345,256,320,18.59
resnetv2_101,535.41,476.74,256,224,44.54
resnetv2_50d_gn,535.21,357.946,192,224,25.57
tf_efficientnet_b2_ns,534.14,358.079,192,260,9.11
tf_efficientnet_b2,533.77,358.295,192,260,9.11
tf_efficientnet_b2_ap,533.7,358.382,192,260,9.11
gcresnext50ts,533.26,358.674,192,256,15.67
tf_efficientnetv2_b3,532.84,358.667,192,300,14.36
nf_resnet50,527.83,726.718,384,288,25.56
sebotnet33ts_256,526.55,242.351,128,256,13.7
xcit_small_12_p16_224,524.79,486.162,256,224,26.25
xcit_small_12_p16_224_dist,524.65,486.278,256,224,26.25
resnet50_gn,521.4,367.482,192,224,25.56
efficientnetv2_rw_t,520.2,367.035,192,288,13.65
resnet101,518.79,491.983,256,224,44.55
tv_resnet101,518.57,492.187,256,224,44.55
gluon_resnet101_v1b,518.13,492.648,256,224,44.55
crossvit_small_240,516.01,370.633,192,240,26.86
twins_svt_small,514.62,496.02,256,224,24.06
vit_base_r26_s32_224,510.65,374.824,192,224,101.38
xcit_tiny_24_p16_224_dist,510.27,498.69,256,224,12.12
xcit_tiny_24_p16_224,510.13,498.896,256,224,12.12
gmlp_s16_224,504.22,379.373,192,224,19.42
resnetv2_101d,500.13,510.457,256,224,44.56
mixer_b16_224,499.95,511.32,256,224,59.88
mixer_b16_224_miil,499.11,512.204,256,224,59.88
vit_base_resnet50d_224,497.25,513.299,256,224,110.97
dla60_res2next,494.84,516.012,256,224,17.03
gluon_resnet101_v1c,492.43,518.398,256,224,44.57
vit_base_patch32_384,491.61,519.97,256,384,88.3
repvgg_b1,491.0,1041.743,512,224,57.42
resmlp_36_224,490.1,389.77,192,224,44.69
resmlp_36_distilled_224,490.1,389.707,192,224,44.69
efficientnet_b2,489.47,390.855,192,288,9.11
efficientnet_b2a,489.25,391.025,192,288,9.11
vit_large_patch32_224,487.93,523.265,256,224,306.54
gluon_resnet101_v1d,484.72,526.651,256,224,44.57
gc_efficientnetv2_rw_t,482.91,395.05,192,288,13.68
wide_resnet50_2,482.59,529.718,256,224,68.88
mixer_l32_224,479.14,399.369,192,224,206.94
cait_xxs24_224,478.85,532.347,256,224,11.96
cspdarknet53,478.49,534.051,256,256,27.64
efficientnet_cc_b1_8e,478.04,534.008,256,240,39.72
crossvit_15_240,474.46,403.054,192,240,27.53
resnest50d,473.17,539.784,256,224,27.48
res2net50_26w_6s,470.73,542.208,256,224,37.05
ecaresnet26t,470.49,543.574,256,320,16.01
dla102,468.81,544.602,256,224,33.27
regnetz_b16,467.13,409.622,192,288,9.72
xcit_nano_12_p16_384_dist,466.59,547.027,256,384,3.05
tf_efficientnet_cc_b1_8e,466.17,547.634,256,240,39.72
cspdarknet53_iabn,464.37,824.482,384,256,27.64
crossvit_15_dagger_240,458.06,417.527,192,240,28.21
regnetx_080,454.82,843.271,384,224,39.57
vgg13_bn,453.43,564.307,256,224,133.05
halonet50ts,453.0,422.945,192,256,22.73
lambda_resnet50ts,451.97,423.868,192,256,21.54
legacy_seresnet101,451.72,564.699,256,224,49.33
resnetaa101d,451.69,565.275,256,224,44.57
gluon_resnet101_v1s,450.48,566.841,256,224,44.67
vgg16,449.51,854.057,384,224,138.36
densenet201,445.52,428.155,192,224,20.01
swin_tiny_patch4_window7_224,443.41,576.481,256,224,28.29
seresnet101,441.23,578.213,256,224,49.33
nf_seresnet101,438.29,581.969,256,224,49.33
resnetblur101d,436.36,585.216,256,224,44.57
mixnet_xl,424.84,450.181,192,224,11.9
twins_pcpvt_small,421.68,605.595,256,224,24.11
nfnet_l0,417.1,612.673,256,288,35.07
botnet50ts_256,413.47,308.688,128,256,22.74
skresnext50_32x4d,412.36,619.54,256,224,27.48
gluon_resnext101_32x4d,412.2,619.638,256,224,44.18
swsl_resnext101_32x4d,412.03,619.892,256,224,44.18
resnext101_32x4d,411.85,620.12,256,224,44.18
ssl_resnext101_32x4d,411.66,620.437,256,224,44.18
vit_tiny_patch16_384,409.64,311.71,128,384,5.79
convit_small,402.9,475.62,192,224,27.78
halo2botnet50ts_256,398.01,320.711,128,256,22.64
res2net101_26w_4s,397.75,480.37,192,224,45.21
efficientnet_lite3,397.55,320.952,128,300,8.2
tresnet_l,396.67,1287.459,512,224,55.99
eca_nfnet_l0,396.55,644.544,256,288,24.14
resnet51q,393.31,487.375,192,288,35.7
nf_ecaresnet101,390.17,654.38,256,224,44.55
dla102x,386.87,494.82,192,224,26.31
res2net50_26w_8s,386.73,494.414,192,224,48.4
lamhalobotnet50ts_256,386.04,330.65,128,256,22.57
regnetx_064,385.03,664.106,256,224,26.21
regnety_032,384.2,665.001,256,288,19.44
tf_efficientnet_lite3,383.37,332.84,128,300,8.2
vgg19,381.34,1006.734,384,224,143.67
xception,378.7,337.254,128,299,22.86
vgg16_bn,376.28,680.016,256,224,138.37
ecaresnet101d,375.12,680.773,256,224,44.57
resnetv2_152,373.91,682.585,256,224,60.19
resnest50d_4s2x40d,372.15,686.672,256,224,30.42
regnety_064,370.51,689.324,256,224,30.58
repvgg_b2g4,370.32,1381.565,512,224,61.76
gluon_resnet152_v1b,366.03,522.463,192,224,60.19
tv_resnet152,365.47,523.229,192,224,60.19
resnet152,365.43,523.289,192,224,60.19
convnext_small,365.2,524.035,192,224,50.22
ese_vovnet99b,363.8,702.311,256,224,63.2
xcit_nano_12_p8_224,362.09,705.393,256,224,3.05
xcit_nano_12_p8_224_dist,361.89,705.792,256,224,3.05
gluon_seresnext101_32x4d,361.32,529.345,192,224,48.96
seresnext101_32x4d,361.3,529.348,192,224,48.96
legacy_seresnext101_32x4d,359.71,531.693,192,224,48.96
nfnet_f0,359.11,711.802,256,256,71.49
vit_base_patch16_224_miil,357.29,357.529,128,224,86.54
nest_tiny,356.86,357.893,128,224,17.06
nfnet_f0s,355.27,719.527,256,256,71.49
resnet61q,355.26,359.368,128,288,36.85
resnetv2_152d,355.12,538.597,192,224,60.2
jx_nest_tiny,353.84,360.954,128,224,17.06
vit_small_resnet50d_s16_224,353.26,542.366,192,224,57.53
hrnet_w18,352.9,721.041,256,224,21.3
deit_base_patch16_224,352.4,362.443,128,224,86.57
gluon_resnet152_v1c,352.05,543.263,192,224,60.21
vit_base_patch16_224_sam,351.83,363.047,128,224,86.57
vit_base_patch16_224,351.68,363.189,128,224,86.57
ese_vovnet99b_iabn,349.3,1096.435,384,224,63.2
gluon_resnet152_v1d,348.27,549.187,192,224,60.21
deit_base_distilled_patch16_224,347.05,368.041,128,224,87.34
crossvit_18_240,345.05,369.11,128,240,43.27
xcit_tiny_12_p16_384_dist,335.91,569.966,192,384,6.72
fbnetv3_g,335.24,379.926,128,288,16.62
regnety_080,335.22,762.533,256,224,39.18
crossvit_18_dagger_240,333.95,381.492,128,240,44.27
repvgg_b2,331.54,1543.333,512,224,89.02
gluon_resnet152_v1s,330.47,578.863,192,224,60.32
densenet161,327.93,388.08,128,224,28.68
dla169,324.94,588.567,192,224,53.39
beit_base_patch16_224,323.79,394.303,128,224,86.53
coat_lite_small,323.43,592.328,192,224,19.84
vgg19_bn,322.88,792.477,256,224,143.68
dm_nfnet_f0,322.48,792.711,256,256,71.49
repvgg_b3g4,313.61,1223.425,384,224,83.83
tf_efficientnet_b3,313.56,406.655,128,300,12.23
tf_efficientnet_b3_ns,313.33,406.959,128,300,12.23
tf_efficientnet_b3_ap,313.29,407.029,128,300,12.23
legacy_seresnet152,311.06,614.195,192,224,66.82
ese_vovnet39b_evos,310.12,411.897,128,224,24.58
dpn92,308.99,827.182,256,224,37.67
efficientnet_b3,306.79,415.635,128,320,12.23
efficientnet_b3a,306.46,416.067,128,320,12.23
cait_xxs36_224,306.42,414.392,128,224,17.3
inception_v4,305.84,625.702,192,299,42.68
twins_pcpvt_base,304.9,627.165,192,224,43.83
regnetx_120,304.64,839.453,256,224,46.11
ecaresnet50t,303.47,420.896,128,320,25.57
hrnet_w32,302.01,631.305,192,224,41.23
seresnet152,300.58,422.864,128,224,66.82
hrnet_w30,299.22,851.13,256,224,37.71
convmixer_1024_20_ks9_p14,299.07,855.183,256,224,24.38
regnetz_c16,296.99,429.626,128,320,13.46
twins_svt_base,296.14,430.333,128,224,56.07
tresnet_xl,289.45,1323.227,384,224,78.44
swin_small_patch4_window7_224,288.11,442.639,128,224,49.61
xception41,287.58,443.922,128,299,26.97
regnety_120,286.74,668.36,192,224,51.82
pit_b_224,286.53,334.205,96,224,73.76
pit_b_distilled_224,285.98,334.794,96,224,74.79
nf_regnet_b4,282.38,451.32,128,384,30.21
wide_resnet101_2,281.79,679.914,192,224,126.89
mixnet_xxl,275.31,463.133,128,224,23.96
resnetv2_50d_evob,273.52,700.95,192,224,25.59
xcit_small_24_p16_224_dist,271.4,468.703,128,224,47.67
xcit_small_24_p16_224,271.18,469.033,128,224,47.67
repvgg_b3,269.71,1422.719,384,224,123.09
convnext_base,268.96,474.28,128,224,88.59
convnext_base_in22ft1k,268.69,474.728,128,224,88.59
resnetv2_50d_evos,264.08,483.67,128,224,25.59
resnext101_64x4d,263.45,484.429,128,224,83.46
gluon_resnext101_64x4d,263.28,484.77,128,224,83.46
cait_s24_224,262.8,484.707,128,224,46.92
dla102x2,261.95,487.178,128,224,41.28
resnet200,261.21,487.362,128,224,64.67
xcit_tiny_12_p8_224_dist,256.95,496.557,128,224,6.71
xcit_tiny_12_p8_224,256.64,497.116,128,224,6.71
regnetx_160,256.42,747.752,192,224,54.28
inception_resnet_v2,251.21,761.158,192,299,55.84
ens_adv_inception_resnet_v2,251.14,761.388,192,299,55.84
resnetrs101,248.83,512.362,128,288,63.62
efficientnet_el,248.41,514.229,128,300,10.59
efficientnet_el_pruned,248.32,514.423,128,300,10.59
swsl_resnext101_32x8d,247.35,516.019,128,224,88.79
ssl_resnext101_32x8d,247.27,516.22,128,224,88.79
ig_resnext101_32x8d,247.19,516.409,128,224,88.79
resnext101_32x8d,247.05,516.7,128,224,88.79
dpn98,245.05,520.932,128,224,61.57
tf_efficientnet_el,244.21,523.119,128,300,10.59
efficientnetv2_s,241.82,394.917,96,384,21.46
tf_efficientnetv2_s_in21ft1k,240.69,396.775,96,384,21.46
tf_efficientnetv2_s,239.59,398.639,96,384,21.46
gluon_seresnext101_64x4d,239.24,533.021,128,224,88.23
gmlp_b16_224,238.08,401.808,96,224,73.08
resnet101d,236.98,538.626,128,320,44.57
nest_small,234.62,407.715,96,224,38.35
resnest101e,234.0,544.633,128,256,48.28
jx_nest_small,233.02,410.534,96,224,38.35
crossvit_base_240,230.08,415.793,96,240,105.03
efficientnetv2_rw_s,228.99,277.434,64,384,23.94
twins_svt_large,227.78,560.084,128,224,99.27
coat_tiny,226.98,562.116,128,224,5.5
seresnext101_32x8d,225.04,566.768,128,224,93.57
vit_large_r50_s32_224,220.85,432.537,96,224,328.99
swin_base_patch4_window7_224,220.19,579.694,128,224,87.77
gluon_xception65,220.18,434.23,96,299,39.92
xception65,219.22,436.124,96,299,39.92
hrnet_w40,218.29,875.173,192,224,57.56
twins_pcpvt_large,217.89,583.814,128,224,60.99
vit_small_r26_s32_384,215.21,296.22,64,384,36.47
convit_base,210.68,454.737,96,224,86.54
vit_small_patch16_384,209.29,305.058,64,384,22.2
hrnet_w44,203.55,938.867,192,224,67.06
hrnet_w48,203.33,625.151,128,224,77.47
tresnet_m_448,203.05,943.084,192,448,31.39
efficientnet_lite4,198.26,321.517,64,380,13.01
xcit_medium_24_p16_224,197.63,482.811,96,224,84.4
xcit_medium_24_p16_224_dist,197.58,482.902,96,224,84.4
vit_base_r50_s16_224,195.39,326.034,64,224,98.66
densenet264,194.98,488.653,96,224,72.69
regnetz_d8,194.31,327.837,64,320,23.37
tf_efficientnet_lite4,192.8,330.742,64,380,13.01
coat_mini,192.49,663.128,128,224,10.34
eca_nfnet_l1,186.17,685.824,128,320,41.41
regnetz_d32,182.61,348.904,64,320,27.58
dpn131,182.53,699.301,128,224,79.25
xcit_small_12_p16_384_dist,180.8,529.313,96,384,26.25
nest_base,176.01,362.129,64,224,67.72
xcit_tiny_24_p16_384_dist,175.08,545.374,96,384,12.12
jx_nest_base,174.7,364.806,64,224,67.72
dpn107,171.15,746.299,128,224,86.92
resnet152d,170.12,562.135,96,320,60.21
hrnet_w64,167.69,758.827,128,224,128.06
efficientnet_b4,167.63,379.88,64,384,19.34
xception71,165.9,383.797,64,299,42.34
densenet264d_iabn,163.71,1168.658,192,224,72.74
regnety_320,163.11,783.43,128,224,145.05
halonet_h1,161.83,394.259,64,256,8.1
convnext_large,159.74,599.371,96,224,197.77
convnext_large_in22ft1k,159.74,599.406,96,224,197.77
tnt_s_patch16_224,158.13,807.71,128,224,23.76
tf_efficientnet_b4,157.41,404.653,64,380,19.34
tf_efficientnet_b4_ap,157.35,404.822,64,380,19.34
tf_efficientnet_b4_ns,157.32,404.855,64,380,19.34
mixer_l16_224,155.84,614.71,96,224,208.2
seresnet200d,155.16,614.74,96,256,71.86
ecaresnet200d,149.12,640.536,96,256,64.69
vit_large_patch32_384,146.32,435.992,64,384,306.63
regnetx_320,143.56,890.603,128,224,107.81
seresnet152d,141.0,450.755,64,320,66.84
resnetrs152,139.45,455.893,64,320,86.62
resnetv2_50x1_bitm,138.96,344.659,48,448,25.55
gluon_senet154,138.12,691.987,96,224,115.09
senet154,138.03,692.329,96,224,115.09
xcit_small_12_p8_224_dist,137.35,464.322,64,224,26.21
xcit_small_12_p8_224,137.29,464.503,64,224,26.21
legacy_senet154,136.93,698.045,96,224,115.09
regnety_160,133.17,719.682,96,288,83.59
xcit_tiny_24_p8_224,132.53,721.469,96,224,12.11
xcit_tiny_24_p8_224_dist,132.41,722.018,96,224,12.11
swin_large_patch4_window7_224,132.32,482.098,64,224,196.53
tnt_b_patch16_224,126.43,757.574,96,224,65.41
resnet200d,123.09,517.162,64,320,64.69
nfnet_f1,122.2,783.645,96,320,132.63
xcit_nano_12_p8_384_dist,122.19,522.144,64,384,3.05
nfnet_f1s,121.0,791.465,96,320,132.63
xcit_large_24_p16_224_dist,119.24,533.65,64,224,189.1
xcit_large_24_p16_224,119.19,534.033,64,224,189.1
efficientnetv2_m,117.91,404.134,48,416,54.14
seresnet269d,115.13,550.403,64,256,113.67
nf_regnet_b5,109.6,581.496,64,456,49.74
dm_nfnet_f1,109.28,583.486,64,320,132.63
vit_large_patch16_224,106.61,448.786,48,224,304.33
convnext_xlarge_in22ft1k,104.28,612.171,64,224,350.2
crossvit_15_dagger_408,103.01,308.973,32,408,28.5
efficientnetv2_rw_m,101.82,311.114,32,416,53.24
regnetz_e8,100.87,473.939,48,320,57.7
tresnet_l_448,99.64,1281.316,128,448,55.99
beit_large_patch16_224,98.41,485.874,48,224,304.43
resnetrs200,97.6,487.799,48,320,93.21
ig_resnext101_32x16d,96.96,988.697,96,224,194.03
swsl_resnext101_32x16d,96.88,989.444,96,224,194.03
ssl_resnext101_32x16d,96.83,989.971,96,224,194.03
xcit_small_24_p16_384_dist,92.8,514.221,48,384,47.67
eca_nfnet_l2,92.75,687.481,64,384,56.72
convnext_base_384_in22ft1k,92.09,519.541,48,384,88.59
deit_base_patch16_384,90.25,353.802,32,384,86.86
vit_base_patch16_384,90.2,353.987,32,384,86.86
deit_base_distilled_patch16_384,88.49,360.855,32,384,87.63
tf_efficientnetv2_m,88.2,359.811,32,480,54.14
tf_efficientnetv2_m_in21ft1k,88.09,360.345,32,480,54.14
xcit_tiny_12_p8_384_dist,87.2,548.793,48,384,6.71
cait_xxs24_384,86.38,368.153,32,384,12.03
resnetv2_101x1_bitm,85.15,374.386,32,448,44.54
resnetv2_152x2_bit_teacher,85.01,374.341,32,224,236.34
convmixer_768_32,84.46,1135.466,96,224,21.11
resnest200e,77.86,611.828,48,320,70.2
crossvit_18_dagger_408,77.59,307.496,24,408,44.61
beit_base_patch16_384,76.84,311.346,24,384,86.74
tresnet_xl_448,75.89,1261.775,96,448,78.44
efficientnet_b5,75.33,422.385,32,456,30.39
vit_large_r50_s32_384,73.95,430.573,32,384,329.09
tf_efficientnet_b5_ap,73.87,430.874,32,456,30.39
tf_efficientnet_b5_ns,73.84,431.06,32,456,30.39
tf_efficientnet_b5,73.76,431.443,32,456,30.39
regnetz_d8_evob,69.9,684.907,48,320,23.41
nfnet_f2,69.7,685.799,48,352,193.78
xcit_small_24_p8_224,69.53,457.315,32,224,47.63
xcit_small_24_p8_224_dist,69.49,457.517,32,224,47.63
nfnet_f2s,69.09,691.894,48,352,193.78
xcit_medium_24_p16_384_dist,67.17,473.231,32,384,84.4
resmlp_big_24_distilled_224,65.96,483.773,32,224,129.14
resmlp_big_24_224,65.95,483.848,32,224,129.14
resmlp_big_24_224_in22ft1k,65.95,483.866,32,224,129.14
regnetz_d8_evos,64.24,496.339,32,320,23.46
dm_nfnet_f2,62.49,765.031,48,352,193.78
ecaresnet269d,60.75,522.332,32,352,102.09
swin_base_patch4_window12_384,60.1,397.741,24,384,87.9
cait_xs24_384,59.89,398.428,24,384,26.67
pnasnet5large,59.35,535.626,32,331,86.06
resnetrs270,58.4,542.338,32,352,129.86
vit_base_patch8_224,58.07,274.782,16,224,86.58
nasnetalarge,57.07,556.194,32,331,88.75
cait_xxs36_384,56.42,422.115,24,384,17.37
convnext_large_384_in22ft1k,54.47,585.847,32,384,197.77
vit_base_r50_s16_384,54.27,293.3,16,384,98.95
vit_base_resnet50_384,54.19,293.736,16,384,98.95
xcit_medium_24_p8_224,51.72,615.776,32,224,84.32
xcit_medium_24_p8_224_dist,51.69,616.066,32,224,84.32
ig_resnext101_32x32d,51.67,617.889,32,224,468.53
eca_nfnet_l3,49.71,640.359,32,448,72.04
convmixer_1536_20,48.09,997.399,48,224,51.63
xcit_small_12_p8_384_dist,46.72,512.057,24,384,26.21
efficientnetv2_l,46.55,339.739,16,480,118.52
tf_efficientnetv2_l_in21ft1k,46.43,340.391,16,480,118.52
tf_efficientnetv2_l,46.41,340.685,16,480,118.52
xcit_tiny_24_p8_384_dist,44.75,712.159,32,384,12.11
cait_s24_384,44.7,355.627,16,384,47.06
efficientnet_b6,41.67,381.28,16,528,43.04
tf_efficientnet_b6_ap,41.13,386.3,16,528,43.04
tf_efficientnet_b6,41.11,386.574,16,528,43.04
tf_efficientnet_b6_ns,40.99,387.676,16,528,43.04
xcit_large_24_p16_384_dist,40.96,582.937,24,384,189.1
vit_huge_patch14_224,37.91,420.175,16,224,632.05
resnetrs350,36.74,646.103,24,384,163.96
swin_large_patch4_window12_384,36.6,435.55,16,384,196.74
nfnet_f3,34.36,694.502,24,416,254.92
nfnet_f3s,34.26,696.883,24,416,254.92
convnext_xlarge_384_in22ft1k,34.01,468.853,16,384,350.2
resnest269e,32.96,721.9,24,416,110.93
dm_nfnet_f3,31.6,755.444,24,416,254.92
resnetv2_50x3_bitm,31.14,513.017,16,448,217.32
xcit_large_24_p8_224_dist,30.37,523.887,16,224,188.93
xcit_large_24_p8_224,30.32,524.75,16,224,188.93
cait_s36_384,29.46,404.043,12,384,68.37
resnetv2_152x2_bit_teacher_384,29.25,408.083,12,384,236.34
tf_efficientnetv2_xl_in21ft1k,28.52,415.17,12,512,208.12
efficientnetv2_xl,28.5,415.628,12,512,208.12
ig_resnext101_32x48d,27.26,585.456,16,224,828.41
vit_large_patch16_384,26.62,299.076,8,384,304.72
efficientnet_b7,25.52,310.186,8,600,66.35
tf_efficientnet_b7,25.25,313.569,8,600,66.35
tf_efficientnet_b7_ap,25.18,314.571,8,600,66.35
tf_efficientnet_b7_ns,25.12,315.222,8,600,66.35
resnetrs420,24.94,632.993,16,416,191.89
xcit_small_24_p8_384_dist,23.58,505.912,12,384,47.63
beit_large_patch16_384,22.66,351.145,8,384,305.0
vit_giant_patch14_224,21.59,368.118,8,224,1012.61
resnetv2_152x2_bitm,21.27,373.944,8,448,236.34
nfnet_f4,18.45,645.682,12,512,316.07
nfnet_f4s,18.35,649.188,12,512,316.07
resnetv2_101x3_bitm,17.19,464.077,8,448,387.93
xcit_medium_24_p8_384_dist,17.03,466.878,8,384,84.32
dm_nfnet_f4,16.71,713.246,12,512,316.07
efficientnet_b8,12.85,463.389,6,672,87.41
tf_efficientnet_b8_ap,12.75,466.866,6,672,87.41
tf_efficientnet_b8,12.73,467.671,6,672,87.41
nfnet_f5,12.01,660.317,8,544,377.21
nfnet_f5s,11.94,664.267,8,544,377.21
cait_m36_384,11.32,526.589,6,384,271.22
dm_nfnet_f5,10.99,722.011,8,544,377.21
xcit_large_24_p8_384_dist,10.37,575.649,6,384,188.93
nfnet_f6,9.06,655.713,6,576,438.36
nfnet_f6s,9.04,657.589,6,576,438.36
tf_efficientnet_l2_ns_475,8.8,449.624,4,475,480.31
beit_large_patch16_512,8.36,356.829,3,512,305.67
dm_nfnet_f6,8.32,713.582,6,576,438.36
nfnet_f7,6.44,613.323,4,608,499.5
nfnet_f7s,6.38,619.793,4,608,499.5
cait_m48_448,4.6,430.517,2,448,356.46
resnetv2_152x4_bitm,4.42,450.22,2,480,936.53
tf_efficientnet_l2_ns,2.55,387.178,1,800,480.31
efficientnet_l2,2.52,391.57,1,800,480.31
1 model train_samples_per_sec train_step_time train_batch_size train_img_size param_count
2 tinynet_e 8788.08 57.613 512 106 2.04
3 mobilenetv3_small_050 6451.43 78.69 512 224 1.59
4 tf_mobilenetv3_small_minimal_100 6231.46 81.639 512 224 2.04
5 levit_128s 5345.65 94.962 512 224 7.78
6 lcnet_035 5326.51 95.668 512 224 1.64
7 mobilenetv3_small_075 5149.15 98.754 512 224 2.04
8 lcnet_050 4879.43 104.466 512 224 1.88
9 tf_mobilenetv3_small_075 4839.2 105.1 512 224 2.04
10 mobilenetv3_small_100 4741.69 107.315 512 224 2.54
11 tf_mobilenetv3_small_100 4474.54 113.738 512 224 2.54
12 tinynet_d 4465.05 113.942 512 152 2.34
13 mixer_s32_224 3957.52 128.859 512 224 19.1
14 levit_128 3868.72 131.359 512 224 9.21
15 lcnet_075 3674.09 138.887 512 224 2.36
16 vit_small_patch32_224 3657.34 139.252 512 224 22.88
17 vit_tiny_r_s16_p8_224 3407.13 149.506 512 224 6.34
18 regnetx_002 3372.01 151.204 512 224 2.68
19 levit_192 3357.19 151.505 512 224 10.95
20 mnasnet_small 3158.42 161.274 512 224 2.03
21 regnety_002 3119.2 163.263 512 224 3.16
22 lcnet_100 3075.72 165.994 512 224 2.95
23 mobilenetv2_035 3007.95 169.496 512 224 1.68
24 gernet_s 2901.28 175.82 512 224 8.17
25 gluon_resnet18_v1b 2671.91 191.302 512 224 11.69
26 swsl_resnet18 2669.15 191.488 512 224 11.69
27 ssl_resnet18 2668.71 191.54 512 224 11.69
28 resnet18 2662.83 191.947 512 224 11.69
29 mobilenetv2_050 2596.25 196.482 512 224 1.97
30 levit_256 2559.67 199.033 512 224 18.89
31 regnetx_004 2499.65 203.84 512 224 5.16
32 mnasnet_050 2494.39 204.524 512 224 2.22
33 semnasnet_050 2494.01 204.433 512 224 2.08
34 seresnet18 2463.7 207.356 512 224 11.78
35 ese_vovnet19b_slim_dw 2437.98 209.545 512 224 1.9
36 legacy_seresnet18 2413.99 211.632 512 224 11.78
37 tinynet_c 2389.53 213.326 512 184 2.46
38 tf_mobilenetv3_large_minimal_100 2278.25 224.06 512 224 3.92
39 lcnet_150 2248.73 227.214 512 224 4.5
40 mobilenetv3_large_075 2139.42 238.494 512 224 3.99
41 ghostnet_050 2137.33 238.296 512 224 2.59
42 resnet18d 2046.03 249.892 512 224 11.71
43 tf_mobilenetv3_large_075 2027.78 251.65 512 224 3.99
44 regnetx_006 2023.49 252.29 512 224 6.2
45 ese_vovnet19b_slim 1970.7 259.464 512 224 3.17
46 mobilenetv3_rw 1940.58 263.024 512 224 5.48
47 mobilenetv3_large_100 1936.66 263.562 512 224 5.48
48 mobilenetv3_large_100_miil 1936.5 263.563 512 224 5.48
49 mnasnet_075 1879.23 271.718 512 224 3.17
50 mobilenetv2_075 1868.15 273.318 512 224 2.64
51 tf_efficientnetv2_b0 1853.32 275.137 512 224 7.14
52 tf_mobilenetv3_large_100 1838.66 277.647 512 224 5.48
53 regnety_004 1819.61 280.334 512 224 4.34
54 resnetblur18 1808.21 282.828 512 224 11.69
55 semnasnet_075 1791.94 284.855 512 224 2.91
56 regnety_006 1780.58 286.558 512 224 6.06
57 mobilenetv2_100 1744.9 292.687 512 224 3.5
58 skresnet18 1742.33 293.29 512 224 11.96
59 mnasnet_b1 1686.64 302.825 512 224 4.38
60 mnasnet_100 1686.2 302.919 512 224 4.38
61 deit_tiny_patch16_224 1678.5 304.281 512 224 5.72
62 vit_tiny_patch16_224 1673.2 305.238 512 224 5.72
63 tinynet_b 1669.81 305.621 512 188 3.73
64 hardcorenas_a 1656.9 308.354 512 224 5.26
65 deit_tiny_distilled_patch16_224 1654.85 308.608 512 224 5.91
66 hardcorenas_b 1648.45 309.73 512 224 5.18
67 regnetx_008 1647.09 310.092 512 224 7.26
68 mnasnet_a1 1641.47 311.08 512 224 3.89
69 semnasnet_100 1640.14 311.319 512 224 3.89
70 levit_384 1616.42 315.722 512 224 39.13
71 gluon_resnet34_v1b 1598.5 319.781 512 224 21.8
72 resnet34 1593.87 320.693 512 224 21.8
73 tv_resnet34 1592.81 320.898 512 224 21.8
74 mixer_b32_224 1579.25 323.449 512 224 60.29
75 hardcorenas_c 1569.06 325.387 512 224 5.52
76 visformer_tiny 1563.94 326.764 512 224 10.32
77 spnasnet_100 1551.19 329.16 512 224 4.42
78 pit_ti_distilled_224 1547.24 330.092 512 224 5.1
79 pit_ti_224 1546.92 330.186 512 224 4.85
80 hardcorenas_d 1543.77 330.549 512 224 7.5
81 ghostnet_100 1541.6 330.868 512 224 5.18
82 vit_base_patch32_224 1529.64 333.95 512 224 88.22
83 vit_base_patch32_224_sam 1527.96 334.33 512 224 88.22
84 regnety_008 1525.57 334.693 512 224 6.26
85 resmlp_12_224 1513.1 337.634 512 224 15.35
86 resmlp_12_distilled_224 1513.05 337.643 512 224 15.35
87 seresnet34 1461.69 349.453 512 224 21.96
88 nf_regnet_b0 1450.04 351.967 512 256 8.76
89 legacy_seresnet34 1428.59 357.543 512 224 21.96
90 gernet_m 1427.9 357.926 512 224 21.14
91 ese_vovnet19b_dw 1411.33 362.322 512 224 6.54
92 nf_resnet26 1408.11 363.145 512 224 16.0
93 dla46_c 1390.48 367.526 512 224 1.3
94 tinynet_a 1383.86 368.826 512 192 6.19
95 efficientnet_lite0 1368.92 373.303 512 224 4.65
96 mobilenetv2_110d 1368.66 279.598 384 224 4.52
97 rexnetr_100 1367.14 279.805 384 224 4.88
98 rexnet_100 1361.11 281.051 384 224 4.8
99 resnet34d 1349.98 378.71 512 224 21.82
100 xcit_nano_12_p16_224_dist 1340.16 380.375 512 224 3.05
101 xcit_nano_12_p16_224 1338.66 380.8 512 224 3.05
102 hardcorenas_f 1338.06 381.608 512 224 8.2
103 selecsls42 1328.24 384.874 512 224 30.35
104 ghostnet_130 1326.71 384.661 512 224 7.36
105 selecsls42b 1324.18 386.065 512 224 32.46
106 mixer_s16_224 1308.0 390.939 512 224 18.53
107 resnet26 1305.0 391.896 512 224 16.0
108 tf_efficientnet_lite0 1300.06 393.134 512 224 4.65
109 hardcorenas_e 1287.59 396.566 512 224 8.07
110 hrnet_w18_small 1275.09 400.255 512 224 13.19
111 pit_xs_distilled_224 1245.81 410.186 512 224 11.0
112 pit_xs_224 1244.79 410.521 512 224 10.62
113 mnasnet_140 1243.07 411.14 512 224 7.12
114 fbnetc_100 1241.42 411.527 512 224 5.57
115 mobilenetv2_140 1236.2 309.893 384 224 6.11
116 vit_small_patch32_384 1210.76 422.138 512 384 22.92
117 efficientnet_b0 1207.97 316.872 384 224 5.29
118 semnasnet_140 1199.21 426.076 512 224 6.11
119 nf_seresnet26 1183.4 432.047 512 224 17.4
120 tf_efficientnetv2_b1 1182.26 323.455 384 240 8.14
121 vit_tiny_r_s16_p8_384 1176.2 325.707 384 384 6.36
122 repvgg_b0 1166.7 437.858 512 224 15.82
123 gmixer_12_224 1164.26 439.045 512 224 12.7
124 tf_efficientnet_b0_ns 1155.19 331.423 384 224 5.29
125 tf_efficientnet_b0 1155.11 331.42 384 224 5.29
126 tf_efficientnet_b0_ap 1154.21 331.702 384 224 5.29
127 efficientnet_b1_pruned 1141.45 447.176 512 240 6.33
128 selecsls60 1132.77 451.165 512 224 30.67
129 selecsls60b 1130.1 452.225 512 224 32.77
130 resnet26d 1110.35 460.655 512 224 16.01
131 nf_ecaresnet26 1108.5 461.384 512 224 16.0
132 rexnetr_130 1084.92 234.909 256 224 7.61
133 mixnet_s 1079.62 473.081 512 224 4.13
134 dla34 1055.21 484.649 512 224 15.74
135 dla46x_c 1044.64 489.413 512 224 1.07
136 rexnet_130 1038.12 245.54 256 224 7.56
137 regnetx_016 1027.51 497.437 512 224 9.19
138 mobilenetv2_120d 1026.61 248.294 256 224 5.83
139 ecaresnet50d_pruned 1019.51 501.3 512 224 19.94
140 tf_mixnet_s 1018.74 501.408 512 224 4.13
141 skresnet34 1008.77 506.519 512 224 22.28
142 dla60x_c 999.33 511.452 512 224 1.32
143 gernet_l 994.19 514.18 512 256 31.08
144 xcit_tiny_12_p16_224 974.89 523.578 512 224 6.72
145 xcit_tiny_12_p16_224_dist 974.56 523.762 512 224 6.72
146 rexnetr_150 962.03 265.059 256 224 9.78
147 crossvit_tiny_240 946.09 404.397 384 240 7.01
148 crossvit_9_240 942.0 406.34 384 240 8.55
149 rexnet_150 920.27 277.114 256 224 9.73
150 repvgg_a2 916.8 557.644 512 224 28.21
151 legacy_seresnext26_32x4d 911.29 561.256 512 224 16.79
152 efficientnet_lite1 907.6 281.167 256 240 5.42
153 crossvit_9_dagger_240 907.56 421.794 384 240 8.78
154 regnety_016 905.38 563.868 512 224 11.2
155 convit_tiny 905.04 423.366 384 224 5.71
156 gmlp_ti16_224 895.37 427.435 384 224 5.87
157 efficientnet_es_pruned 894.51 571.683 512 224 5.44
158 efficientnet_es 894.06 571.94 512 224 5.44
159 resnest14d 890.17 574.739 512 224 10.61
160 deit_small_patch16_224 879.08 436.051 384 224 22.05
161 vit_small_patch16_224 877.74 436.745 384 224 22.05
162 tf_efficientnet_es 877.36 582.875 512 224 5.44
163 resnetv2_50 874.29 584.904 512 224 25.55
164 resnext26ts 872.05 439.874 384 256 10.3
165 tf_efficientnet_lite1 870.75 293.088 256 240 5.42
166 resnet26t 867.6 589.639 512 256 16.01
167 tf_efficientnetv2_b2 866.58 294.02 256 260 10.1
168 deit_small_distilled_patch16_224 865.21 443.046 384 224 22.44
169 nf_regnet_b1 863.6 591.527 512 288 10.22
170 nf_regnet_b2 857.0 595.977 512 272 14.31
171 eca_resnext26ts 843.14 303.098 256 256 10.3
172 seresnext26ts 838.62 304.653 256 256 10.39
173 resnet50 826.48 463.878 384 224 25.56
174 ssl_resnet50 826.14 464.04 384 224 25.56
175 botnet26t_256 825.99 464.363 384 256 12.49
176 tv_resnet50 825.99 464.131 384 224 25.56
177 swsl_resnet50 825.61 464.367 384 224 25.56
178 gluon_resnet50_v1b 825.26 464.548 384 224 25.56
179 gcresnext26ts 820.94 311.077 256 256 10.48
180 seresnext26t_32x4d 819.08 468.216 384 224 16.81
181 seresnext26tn_32x4d 818.65 468.46 384 224 16.81
182 seresnext26d_32x4d 816.35 469.756 384 224 16.81
183 pit_s_224 814.17 313.637 256 224 23.46
184 pit_s_distilled_224 813.41 313.92 256 224 24.04
185 vgg11 798.47 641.065 512 224 132.86
186 halonet26t 796.75 481.425 384 256 12.48
187 eca_botnext26ts_256 794.14 321.819 256 256 10.59
188 resnetv2_50t 783.96 652.33 512 224 25.57
189 vit_small_resnet26d_224 783.75 488.981 384 224 63.61
190 ecaresnext26t_32x4d 782.53 490.179 384 224 15.41
191 ecaresnext50t_32x4d 782.45 490.233 384 224 15.41
192 resnetv2_50d 781.21 654.649 512 224 25.57
193 tresnet_m 773.27 659.381 512 224 31.39
194 fbnetv3_d 771.82 329.985 256 256 10.31
195 eca_halonext26ts 770.01 331.906 256 256 10.76
196 cspresnet50 766.64 500.094 384 256 21.62
197 fbnetv3_b 765.95 499.824 384 256 8.6
198 ecaresnet101d_pruned 765.08 667.536 512 224 24.88
199 vovnet39a 764.37 669.255 512 224 22.6
200 gluon_resnet50_v1c 761.03 503.811 384 224 25.58
201 cspresnext50 757.53 506.115 384 224 20.57
202 efficientnet_cc_b0_4e 755.85 506.911 384 224 13.31
203 ecaresnetlight 754.13 678.075 512 224 30.16
204 efficientnet_cc_b0_8e 750.91 510.273 384 224 24.01
205 mixnet_m 749.54 510.912 384 224 5.01
206 efficientnet_b2_pruned 748.23 340.751 256 260 8.31
207 resnet50t 745.06 514.605 384 224 25.57
208 resmlp_24_distilled_224 744.01 342.726 256 224 30.02
209 gluon_resnet50_v1d 743.98 515.37 384 224 25.58
210 resnet50d 743.76 515.521 384 224 25.58
211 resmlp_24_224 743.43 342.965 256 224 30.02
212 legacy_seresnet50 739.53 518.192 384 224 28.09
213 resnet32ts 736.71 346.942 256 256 17.96
214 ese_vovnet39b 733.2 523.109 384 224 24.57
215 tf_efficientnet_cc_b0_4e 732.92 522.856 384 224 13.31
216 tf_efficientnet_cc_b0_8e 731.1 524.143 384 224 24.01
217 dpn68b 728.18 526.241 384 224 12.61
218 resnet33ts 727.42 351.376 256 256 19.68
219 selecsls84 719.68 710.264 512 224 50.95
220 nf_seresnet50 718.4 533.452 384 224 28.09
221 rexnetr_200 715.44 267.313 192 224 16.52
222 visformer_small 713.57 537.5 384 224 40.22
223 res2net50_48w_2s 712.35 538.286 384 224 25.29
224 tf_mixnet_m 711.23 538.464 384 224 5.01
225 dpn68 711.07 539.016 384 224 12.61
226 seresnet50 710.55 539.38 384 224 28.09
227 lambda_resnet26t 708.6 541.35 384 256 10.96
228 bat_resnext26ts 706.1 361.453 256 256 10.73
229 tf_efficientnet_b1 705.85 361.254 256 240 7.79
230 tf_efficientnet_b1_ns 705.53 361.468 256 240 7.79
231 tf_efficientnet_b1_ap 705.49 361.504 256 240 7.79
232 seresnet33ts 700.84 364.51 256 256 19.78
233 eca_resnet33ts 700.22 364.953 256 256 19.68
234 resnetblur50 694.99 551.777 384 224 25.56
235 cspresnet50d 694.84 551.823 384 256 21.64
236 rexnet_200 693.57 275.752 192 224 16.37
237 cspresnet50w 686.88 558.19 384 256 28.12
238 eca_vovnet39b 683.56 748.398 512 224 22.6
239 densenet121 682.9 373.209 256 224 7.98
240 dla60 682.79 561.488 384 224 22.04
241 efficientnet_b1 682.7 373.555 256 256 7.79
242 tv_densenet121 681.14 374.178 256 224 7.98
243 gcresnet33ts 680.99 374.966 256 256 19.88
244 efficientnet_lite2 680.43 375.329 256 260 6.09
245 resnest26d 672.85 570.012 384 224 17.07
246 resnetaa50d 669.52 572.758 384 224 25.58
247 vgg11_bn 669.19 382.326 256 224 132.87
248 gluon_resnet50_v1s 664.71 576.903 384 224 25.68
249 nf_ecaresnet50 663.54 577.843 384 224 25.56
250 hrnet_w18_small_v2 663.05 769.959 512 224 15.6
251 tf_efficientnet_lite2 656.12 389.28 256 260 6.09
252 vit_small_r26_s32_224 654.12 390.216 256 224 36.43
253 lambda_resnet26rpt_256 650.73 196.144 128 256 10.99
254 vit_base_resnet26d_224 650.56 589.099 384 224 101.4
255 seresnet50t 645.62 395.439 256 224 28.1
256 gluon_resnext50_32x4d 643.1 596.344 384 224 25.03
257 ssl_resnext50_32x4d 642.69 596.753 384 224 25.03
258 resnext50_32x4d 642.56 596.871 384 224 25.03
259 tv_resnext50_32x4d 642.26 597.116 384 224 25.03
260 swsl_resnext50_32x4d 642.22 597.166 384 224 25.03
261 haloregnetz_b 639.45 398.971 256 224 11.68
262 resnetrs50 639.32 599.548 384 224 35.69
263 densenet121d 637.87 399.654 256 224 8.0
264 regnetx_032 635.48 603.11 384 224 15.3
265 resnetblur50d 634.6 402.62 256 224 25.58
266 res2net50_26w_4s 625.68 407.946 256 224 25.7
267 skresnet50 620.44 411.364 256 224 25.8
268 ese_vovnet57b 618.38 620.149 384 224 38.61
269 densenetblur121d 611.64 416.815 256 224 8.0
270 ecaresnet50d 601.62 424.614 256 224 25.58
271 adv_inception_v3 592.83 646.406 384 299 23.83
272 resnext50d_32x4d 590.58 432.684 256 224 25.05
273 gluon_inception_v3 589.45 650.157 384 299 23.83
274 inception_v3 588.55 651.172 384 299 23.83
275 tf_inception_v3 588.36 651.336 384 299 23.83
276 seresnetaa50d 587.38 434.758 256 224 28.11
277 sehalonet33ts 585.23 436.688 256 256 13.69
278 coat_lite_tiny 580.33 660.876 384 224 5.72
279 gmixer_24_224 577.9 441.611 256 224 24.72
280 gcresnet50t 577.25 442.093 256 256 25.9
281 efficientnet_b3_pruned 576.96 442.1 256 300 9.86
282 mixnet_l 576.91 442.34 256 224 7.33
283 resnetv2_50x1_bit_distilled 575.09 333.113 192 224 25.55
284 skresnet50d 572.85 445.576 256 224 25.82
285 vovnet57a 569.57 898.133 512 224 36.64
286 res2next50 568.73 448.924 256 224 24.67
287 seresnext50_32x4d 567.85 449.777 256 224 27.56
288 gluon_seresnext50_32x4d 567.27 450.224 256 224 27.56
289 cspresnext50_iabn 565.5 676.906 384 256 20.57
290 legacy_seresnext50_32x4d 565.44 451.632 256 224 27.56
291 resnest50d_1s4x24d 561.18 454.967 256 224 25.68
292 res2net50_14w_8s 560.86 454.355 256 224 25.06
293 repvgg_b1g4 560.47 912.475 512 224 39.97
294 densenet169 558.29 456.216 256 224 14.15
295 convnext_tiny 558.25 457.708 256 224 28.59
296 convnext_tiny_hnf 555.94 459.59 256 224 28.59
297 coat_lite_mini 554.95 691.16 384 224 11.01
298 regnety_040 554.71 690.835 384 224 20.65
299 dla60_res2net 550.65 463.555 256 224 20.85
300 darknet53 549.0 465.546 256 256 41.61
301 vgg13 548.68 699.681 384 224 133.05
302 tf_mixnet_l 547.03 466.516 256 224 7.33
303 dla60x 544.8 469.001 256 224 17.35
304 efficientnet_em 542.04 707.524 384 240 6.9
305 regnetx_040 541.76 707.76 384 224 22.12
306 nf_resnet101 541.63 707.493 384 224 44.55
307 tf_efficientnet_em 539.79 473.368 256 240 6.9
308 nf_regnet_b3 535.57 476.345 256 320 18.59
309 resnetv2_101 535.41 476.74 256 224 44.54
310 resnetv2_50d_gn 535.21 357.946 192 224 25.57
311 tf_efficientnet_b2_ns 534.14 358.079 192 260 9.11
312 tf_efficientnet_b2 533.77 358.295 192 260 9.11
313 tf_efficientnet_b2_ap 533.7 358.382 192 260 9.11
314 gcresnext50ts 533.26 358.674 192 256 15.67
315 tf_efficientnetv2_b3 532.84 358.667 192 300 14.36
316 nf_resnet50 527.83 726.718 384 288 25.56
317 sebotnet33ts_256 526.55 242.351 128 256 13.7
318 xcit_small_12_p16_224 524.79 486.162 256 224 26.25
319 xcit_small_12_p16_224_dist 524.65 486.278 256 224 26.25
320 resnet50_gn 521.4 367.482 192 224 25.56
321 efficientnetv2_rw_t 520.2 367.035 192 288 13.65
322 resnet101 518.79 491.983 256 224 44.55
323 tv_resnet101 518.57 492.187 256 224 44.55
324 gluon_resnet101_v1b 518.13 492.648 256 224 44.55
325 crossvit_small_240 516.01 370.633 192 240 26.86
326 twins_svt_small 514.62 496.02 256 224 24.06
327 vit_base_r26_s32_224 510.65 374.824 192 224 101.38
328 xcit_tiny_24_p16_224_dist 510.27 498.69 256 224 12.12
329 xcit_tiny_24_p16_224 510.13 498.896 256 224 12.12
330 gmlp_s16_224 504.22 379.373 192 224 19.42
331 resnetv2_101d 500.13 510.457 256 224 44.56
332 mixer_b16_224 499.95 511.32 256 224 59.88
333 mixer_b16_224_miil 499.11 512.204 256 224 59.88
334 vit_base_resnet50d_224 497.25 513.299 256 224 110.97
335 dla60_res2next 494.84 516.012 256 224 17.03
336 gluon_resnet101_v1c 492.43 518.398 256 224 44.57
337 vit_base_patch32_384 491.61 519.97 256 384 88.3
338 repvgg_b1 491.0 1041.743 512 224 57.42
339 resmlp_36_224 490.1 389.77 192 224 44.69
340 resmlp_36_distilled_224 490.1 389.707 192 224 44.69
341 efficientnet_b2 489.47 390.855 192 288 9.11
342 efficientnet_b2a 489.25 391.025 192 288 9.11
343 vit_large_patch32_224 487.93 523.265 256 224 306.54
344 gluon_resnet101_v1d 484.72 526.651 256 224 44.57
345 gc_efficientnetv2_rw_t 482.91 395.05 192 288 13.68
346 wide_resnet50_2 482.59 529.718 256 224 68.88
347 mixer_l32_224 479.14 399.369 192 224 206.94
348 cait_xxs24_224 478.85 532.347 256 224 11.96
349 cspdarknet53 478.49 534.051 256 256 27.64
350 efficientnet_cc_b1_8e 478.04 534.008 256 240 39.72
351 crossvit_15_240 474.46 403.054 192 240 27.53
352 resnest50d 473.17 539.784 256 224 27.48
353 res2net50_26w_6s 470.73 542.208 256 224 37.05
354 ecaresnet26t 470.49 543.574 256 320 16.01
355 dla102 468.81 544.602 256 224 33.27
356 regnetz_b16 467.13 409.622 192 288 9.72
357 xcit_nano_12_p16_384_dist 466.59 547.027 256 384 3.05
358 tf_efficientnet_cc_b1_8e 466.17 547.634 256 240 39.72
359 cspdarknet53_iabn 464.37 824.482 384 256 27.64
360 crossvit_15_dagger_240 458.06 417.527 192 240 28.21
361 regnetx_080 454.82 843.271 384 224 39.57
362 vgg13_bn 453.43 564.307 256 224 133.05
363 halonet50ts 453.0 422.945 192 256 22.73
364 lambda_resnet50ts 451.97 423.868 192 256 21.54
365 legacy_seresnet101 451.72 564.699 256 224 49.33
366 resnetaa101d 451.69 565.275 256 224 44.57
367 gluon_resnet101_v1s 450.48 566.841 256 224 44.67
368 vgg16 449.51 854.057 384 224 138.36
369 densenet201 445.52 428.155 192 224 20.01
370 swin_tiny_patch4_window7_224 443.41 576.481 256 224 28.29
371 seresnet101 441.23 578.213 256 224 49.33
372 nf_seresnet101 438.29 581.969 256 224 49.33
373 resnetblur101d 436.36 585.216 256 224 44.57
374 mixnet_xl 424.84 450.181 192 224 11.9
375 twins_pcpvt_small 421.68 605.595 256 224 24.11
376 nfnet_l0 417.1 612.673 256 288 35.07
377 botnet50ts_256 413.47 308.688 128 256 22.74
378 skresnext50_32x4d 412.36 619.54 256 224 27.48
379 gluon_resnext101_32x4d 412.2 619.638 256 224 44.18
380 swsl_resnext101_32x4d 412.03 619.892 256 224 44.18
381 resnext101_32x4d 411.85 620.12 256 224 44.18
382 ssl_resnext101_32x4d 411.66 620.437 256 224 44.18
383 vit_tiny_patch16_384 409.64 311.71 128 384 5.79
384 convit_small 402.9 475.62 192 224 27.78
385 halo2botnet50ts_256 398.01 320.711 128 256 22.64
386 res2net101_26w_4s 397.75 480.37 192 224 45.21
387 efficientnet_lite3 397.55 320.952 128 300 8.2
388 tresnet_l 396.67 1287.459 512 224 55.99
389 eca_nfnet_l0 396.55 644.544 256 288 24.14
390 resnet51q 393.31 487.375 192 288 35.7
391 nf_ecaresnet101 390.17 654.38 256 224 44.55
392 dla102x 386.87 494.82 192 224 26.31
393 res2net50_26w_8s 386.73 494.414 192 224 48.4
394 lamhalobotnet50ts_256 386.04 330.65 128 256 22.57
395 regnetx_064 385.03 664.106 256 224 26.21
396 regnety_032 384.2 665.001 256 288 19.44
397 tf_efficientnet_lite3 383.37 332.84 128 300 8.2
398 vgg19 381.34 1006.734 384 224 143.67
399 xception 378.7 337.254 128 299 22.86
400 vgg16_bn 376.28 680.016 256 224 138.37
401 ecaresnet101d 375.12 680.773 256 224 44.57
402 resnetv2_152 373.91 682.585 256 224 60.19
403 resnest50d_4s2x40d 372.15 686.672 256 224 30.42
404 regnety_064 370.51 689.324 256 224 30.58
405 repvgg_b2g4 370.32 1381.565 512 224 61.76
406 gluon_resnet152_v1b 366.03 522.463 192 224 60.19
407 tv_resnet152 365.47 523.229 192 224 60.19
408 resnet152 365.43 523.289 192 224 60.19
409 convnext_small 365.2 524.035 192 224 50.22
410 ese_vovnet99b 363.8 702.311 256 224 63.2
411 xcit_nano_12_p8_224 362.09 705.393 256 224 3.05
412 xcit_nano_12_p8_224_dist 361.89 705.792 256 224 3.05
413 gluon_seresnext101_32x4d 361.32 529.345 192 224 48.96
414 seresnext101_32x4d 361.3 529.348 192 224 48.96
415 legacy_seresnext101_32x4d 359.71 531.693 192 224 48.96
416 nfnet_f0 359.11 711.802 256 256 71.49
417 vit_base_patch16_224_miil 357.29 357.529 128 224 86.54
418 nest_tiny 356.86 357.893 128 224 17.06
419 nfnet_f0s 355.27 719.527 256 256 71.49
420 resnet61q 355.26 359.368 128 288 36.85
421 resnetv2_152d 355.12 538.597 192 224 60.2
422 jx_nest_tiny 353.84 360.954 128 224 17.06
423 vit_small_resnet50d_s16_224 353.26 542.366 192 224 57.53
424 hrnet_w18 352.9 721.041 256 224 21.3
425 deit_base_patch16_224 352.4 362.443 128 224 86.57
426 gluon_resnet152_v1c 352.05 543.263 192 224 60.21
427 vit_base_patch16_224_sam 351.83 363.047 128 224 86.57
428 vit_base_patch16_224 351.68 363.189 128 224 86.57
429 ese_vovnet99b_iabn 349.3 1096.435 384 224 63.2
430 gluon_resnet152_v1d 348.27 549.187 192 224 60.21
431 deit_base_distilled_patch16_224 347.05 368.041 128 224 87.34
432 crossvit_18_240 345.05 369.11 128 240 43.27
433 xcit_tiny_12_p16_384_dist 335.91 569.966 192 384 6.72
434 fbnetv3_g 335.24 379.926 128 288 16.62
435 regnety_080 335.22 762.533 256 224 39.18
436 crossvit_18_dagger_240 333.95 381.492 128 240 44.27
437 repvgg_b2 331.54 1543.333 512 224 89.02
438 gluon_resnet152_v1s 330.47 578.863 192 224 60.32
439 densenet161 327.93 388.08 128 224 28.68
440 dla169 324.94 588.567 192 224 53.39
441 beit_base_patch16_224 323.79 394.303 128 224 86.53
442 coat_lite_small 323.43 592.328 192 224 19.84
443 vgg19_bn 322.88 792.477 256 224 143.68
444 dm_nfnet_f0 322.48 792.711 256 256 71.49
445 repvgg_b3g4 313.61 1223.425 384 224 83.83
446 tf_efficientnet_b3 313.56 406.655 128 300 12.23
447 tf_efficientnet_b3_ns 313.33 406.959 128 300 12.23
448 tf_efficientnet_b3_ap 313.29 407.029 128 300 12.23
449 legacy_seresnet152 311.06 614.195 192 224 66.82
450 ese_vovnet39b_evos 310.12 411.897 128 224 24.58
451 dpn92 308.99 827.182 256 224 37.67
452 efficientnet_b3 306.79 415.635 128 320 12.23
453 efficientnet_b3a 306.46 416.067 128 320 12.23
454 cait_xxs36_224 306.42 414.392 128 224 17.3
455 inception_v4 305.84 625.702 192 299 42.68
456 twins_pcpvt_base 304.9 627.165 192 224 43.83
457 regnetx_120 304.64 839.453 256 224 46.11
458 ecaresnet50t 303.47 420.896 128 320 25.57
459 hrnet_w32 302.01 631.305 192 224 41.23
460 seresnet152 300.58 422.864 128 224 66.82
461 hrnet_w30 299.22 851.13 256 224 37.71
462 convmixer_1024_20_ks9_p14 299.07 855.183 256 224 24.38
463 regnetz_c16 296.99 429.626 128 320 13.46
464 twins_svt_base 296.14 430.333 128 224 56.07
465 tresnet_xl 289.45 1323.227 384 224 78.44
466 swin_small_patch4_window7_224 288.11 442.639 128 224 49.61
467 xception41 287.58 443.922 128 299 26.97
468 regnety_120 286.74 668.36 192 224 51.82
469 pit_b_224 286.53 334.205 96 224 73.76
470 pit_b_distilled_224 285.98 334.794 96 224 74.79
471 nf_regnet_b4 282.38 451.32 128 384 30.21
472 wide_resnet101_2 281.79 679.914 192 224 126.89
473 mixnet_xxl 275.31 463.133 128 224 23.96
474 resnetv2_50d_evob 273.52 700.95 192 224 25.59
475 xcit_small_24_p16_224_dist 271.4 468.703 128 224 47.67
476 xcit_small_24_p16_224 271.18 469.033 128 224 47.67
477 repvgg_b3 269.71 1422.719 384 224 123.09
478 convnext_base 268.96 474.28 128 224 88.59
479 convnext_base_in22ft1k 268.69 474.728 128 224 88.59
480 resnetv2_50d_evos 264.08 483.67 128 224 25.59
481 resnext101_64x4d 263.45 484.429 128 224 83.46
482 gluon_resnext101_64x4d 263.28 484.77 128 224 83.46
483 cait_s24_224 262.8 484.707 128 224 46.92
484 dla102x2 261.95 487.178 128 224 41.28
485 resnet200 261.21 487.362 128 224 64.67
486 xcit_tiny_12_p8_224_dist 256.95 496.557 128 224 6.71
487 xcit_tiny_12_p8_224 256.64 497.116 128 224 6.71
488 regnetx_160 256.42 747.752 192 224 54.28
489 inception_resnet_v2 251.21 761.158 192 299 55.84
490 ens_adv_inception_resnet_v2 251.14 761.388 192 299 55.84
491 resnetrs101 248.83 512.362 128 288 63.62
492 efficientnet_el 248.41 514.229 128 300 10.59
493 efficientnet_el_pruned 248.32 514.423 128 300 10.59
494 swsl_resnext101_32x8d 247.35 516.019 128 224 88.79
495 ssl_resnext101_32x8d 247.27 516.22 128 224 88.79
496 ig_resnext101_32x8d 247.19 516.409 128 224 88.79
497 resnext101_32x8d 247.05 516.7 128 224 88.79
498 dpn98 245.05 520.932 128 224 61.57
499 tf_efficientnet_el 244.21 523.119 128 300 10.59
500 efficientnetv2_s 241.82 394.917 96 384 21.46
501 tf_efficientnetv2_s_in21ft1k 240.69 396.775 96 384 21.46
502 tf_efficientnetv2_s 239.59 398.639 96 384 21.46
503 gluon_seresnext101_64x4d 239.24 533.021 128 224 88.23
504 gmlp_b16_224 238.08 401.808 96 224 73.08
505 resnet101d 236.98 538.626 128 320 44.57
506 nest_small 234.62 407.715 96 224 38.35
507 resnest101e 234.0 544.633 128 256 48.28
508 jx_nest_small 233.02 410.534 96 224 38.35
509 crossvit_base_240 230.08 415.793 96 240 105.03
510 efficientnetv2_rw_s 228.99 277.434 64 384 23.94
511 twins_svt_large 227.78 560.084 128 224 99.27
512 coat_tiny 226.98 562.116 128 224 5.5
513 seresnext101_32x8d 225.04 566.768 128 224 93.57
514 vit_large_r50_s32_224 220.85 432.537 96 224 328.99
515 swin_base_patch4_window7_224 220.19 579.694 128 224 87.77
516 gluon_xception65 220.18 434.23 96 299 39.92
517 xception65 219.22 436.124 96 299 39.92
518 hrnet_w40 218.29 875.173 192 224 57.56
519 twins_pcpvt_large 217.89 583.814 128 224 60.99
520 vit_small_r26_s32_384 215.21 296.22 64 384 36.47
521 convit_base 210.68 454.737 96 224 86.54
522 vit_small_patch16_384 209.29 305.058 64 384 22.2
523 hrnet_w44 203.55 938.867 192 224 67.06
524 hrnet_w48 203.33 625.151 128 224 77.47
525 tresnet_m_448 203.05 943.084 192 448 31.39
526 efficientnet_lite4 198.26 321.517 64 380 13.01
527 xcit_medium_24_p16_224 197.63 482.811 96 224 84.4
528 xcit_medium_24_p16_224_dist 197.58 482.902 96 224 84.4
529 vit_base_r50_s16_224 195.39 326.034 64 224 98.66
530 densenet264 194.98 488.653 96 224 72.69
531 regnetz_d8 194.31 327.837 64 320 23.37
532 tf_efficientnet_lite4 192.8 330.742 64 380 13.01
533 coat_mini 192.49 663.128 128 224 10.34
534 eca_nfnet_l1 186.17 685.824 128 320 41.41
535 regnetz_d32 182.61 348.904 64 320 27.58
536 dpn131 182.53 699.301 128 224 79.25
537 xcit_small_12_p16_384_dist 180.8 529.313 96 384 26.25
538 nest_base 176.01 362.129 64 224 67.72
539 xcit_tiny_24_p16_384_dist 175.08 545.374 96 384 12.12
540 jx_nest_base 174.7 364.806 64 224 67.72
541 dpn107 171.15 746.299 128 224 86.92
542 resnet152d 170.12 562.135 96 320 60.21
543 hrnet_w64 167.69 758.827 128 224 128.06
544 efficientnet_b4 167.63 379.88 64 384 19.34
545 xception71 165.9 383.797 64 299 42.34
546 densenet264d_iabn 163.71 1168.658 192 224 72.74
547 regnety_320 163.11 783.43 128 224 145.05
548 halonet_h1 161.83 394.259 64 256 8.1
549 convnext_large 159.74 599.371 96 224 197.77
550 convnext_large_in22ft1k 159.74 599.406 96 224 197.77
551 tnt_s_patch16_224 158.13 807.71 128 224 23.76
552 tf_efficientnet_b4 157.41 404.653 64 380 19.34
553 tf_efficientnet_b4_ap 157.35 404.822 64 380 19.34
554 tf_efficientnet_b4_ns 157.32 404.855 64 380 19.34
555 mixer_l16_224 155.84 614.71 96 224 208.2
556 seresnet200d 155.16 614.74 96 256 71.86
557 ecaresnet200d 149.12 640.536 96 256 64.69
558 vit_large_patch32_384 146.32 435.992 64 384 306.63
559 regnetx_320 143.56 890.603 128 224 107.81
560 seresnet152d 141.0 450.755 64 320 66.84
561 resnetrs152 139.45 455.893 64 320 86.62
562 resnetv2_50x1_bitm 138.96 344.659 48 448 25.55
563 gluon_senet154 138.12 691.987 96 224 115.09
564 senet154 138.03 692.329 96 224 115.09
565 xcit_small_12_p8_224_dist 137.35 464.322 64 224 26.21
566 xcit_small_12_p8_224 137.29 464.503 64 224 26.21
567 legacy_senet154 136.93 698.045 96 224 115.09
568 regnety_160 133.17 719.682 96 288 83.59
569 xcit_tiny_24_p8_224 132.53 721.469 96 224 12.11
570 xcit_tiny_24_p8_224_dist 132.41 722.018 96 224 12.11
571 swin_large_patch4_window7_224 132.32 482.098 64 224 196.53
572 tnt_b_patch16_224 126.43 757.574 96 224 65.41
573 resnet200d 123.09 517.162 64 320 64.69
574 nfnet_f1 122.2 783.645 96 320 132.63
575 xcit_nano_12_p8_384_dist 122.19 522.144 64 384 3.05
576 nfnet_f1s 121.0 791.465 96 320 132.63
577 xcit_large_24_p16_224_dist 119.24 533.65 64 224 189.1
578 xcit_large_24_p16_224 119.19 534.033 64 224 189.1
579 efficientnetv2_m 117.91 404.134 48 416 54.14
580 seresnet269d 115.13 550.403 64 256 113.67
581 nf_regnet_b5 109.6 581.496 64 456 49.74
582 dm_nfnet_f1 109.28 583.486 64 320 132.63
583 vit_large_patch16_224 106.61 448.786 48 224 304.33
584 convnext_xlarge_in22ft1k 104.28 612.171 64 224 350.2
585 crossvit_15_dagger_408 103.01 308.973 32 408 28.5
586 efficientnetv2_rw_m 101.82 311.114 32 416 53.24
587 regnetz_e8 100.87 473.939 48 320 57.7
588 tresnet_l_448 99.64 1281.316 128 448 55.99
589 beit_large_patch16_224 98.41 485.874 48 224 304.43
590 resnetrs200 97.6 487.799 48 320 93.21
591 ig_resnext101_32x16d 96.96 988.697 96 224 194.03
592 swsl_resnext101_32x16d 96.88 989.444 96 224 194.03
593 ssl_resnext101_32x16d 96.83 989.971 96 224 194.03
594 xcit_small_24_p16_384_dist 92.8 514.221 48 384 47.67
595 eca_nfnet_l2 92.75 687.481 64 384 56.72
596 convnext_base_384_in22ft1k 92.09 519.541 48 384 88.59
597 deit_base_patch16_384 90.25 353.802 32 384 86.86
598 vit_base_patch16_384 90.2 353.987 32 384 86.86
599 deit_base_distilled_patch16_384 88.49 360.855 32 384 87.63
600 tf_efficientnetv2_m 88.2 359.811 32 480 54.14
601 tf_efficientnetv2_m_in21ft1k 88.09 360.345 32 480 54.14
602 xcit_tiny_12_p8_384_dist 87.2 548.793 48 384 6.71
603 cait_xxs24_384 86.38 368.153 32 384 12.03
604 resnetv2_101x1_bitm 85.15 374.386 32 448 44.54
605 resnetv2_152x2_bit_teacher 85.01 374.341 32 224 236.34
606 convmixer_768_32 84.46 1135.466 96 224 21.11
607 resnest200e 77.86 611.828 48 320 70.2
608 crossvit_18_dagger_408 77.59 307.496 24 408 44.61
609 beit_base_patch16_384 76.84 311.346 24 384 86.74
610 tresnet_xl_448 75.89 1261.775 96 448 78.44
611 efficientnet_b5 75.33 422.385 32 456 30.39
612 vit_large_r50_s32_384 73.95 430.573 32 384 329.09
613 tf_efficientnet_b5_ap 73.87 430.874 32 456 30.39
614 tf_efficientnet_b5_ns 73.84 431.06 32 456 30.39
615 tf_efficientnet_b5 73.76 431.443 32 456 30.39
616 regnetz_d8_evob 69.9 684.907 48 320 23.41
617 nfnet_f2 69.7 685.799 48 352 193.78
618 xcit_small_24_p8_224 69.53 457.315 32 224 47.63
619 xcit_small_24_p8_224_dist 69.49 457.517 32 224 47.63
620 nfnet_f2s 69.09 691.894 48 352 193.78
621 xcit_medium_24_p16_384_dist 67.17 473.231 32 384 84.4
622 resmlp_big_24_distilled_224 65.96 483.773 32 224 129.14
623 resmlp_big_24_224 65.95 483.848 32 224 129.14
624 resmlp_big_24_224_in22ft1k 65.95 483.866 32 224 129.14
625 regnetz_d8_evos 64.24 496.339 32 320 23.46
626 dm_nfnet_f2 62.49 765.031 48 352 193.78
627 ecaresnet269d 60.75 522.332 32 352 102.09
628 swin_base_patch4_window12_384 60.1 397.741 24 384 87.9
629 cait_xs24_384 59.89 398.428 24 384 26.67
630 pnasnet5large 59.35 535.626 32 331 86.06
631 resnetrs270 58.4 542.338 32 352 129.86
632 vit_base_patch8_224 58.07 274.782 16 224 86.58
633 nasnetalarge 57.07 556.194 32 331 88.75
634 cait_xxs36_384 56.42 422.115 24 384 17.37
635 convnext_large_384_in22ft1k 54.47 585.847 32 384 197.77
636 vit_base_r50_s16_384 54.27 293.3 16 384 98.95
637 vit_base_resnet50_384 54.19 293.736 16 384 98.95
638 xcit_medium_24_p8_224 51.72 615.776 32 224 84.32
639 xcit_medium_24_p8_224_dist 51.69 616.066 32 224 84.32
640 ig_resnext101_32x32d 51.67 617.889 32 224 468.53
641 eca_nfnet_l3 49.71 640.359 32 448 72.04
642 convmixer_1536_20 48.09 997.399 48 224 51.63
643 xcit_small_12_p8_384_dist 46.72 512.057 24 384 26.21
644 efficientnetv2_l 46.55 339.739 16 480 118.52
645 tf_efficientnetv2_l_in21ft1k 46.43 340.391 16 480 118.52
646 tf_efficientnetv2_l 46.41 340.685 16 480 118.52
647 xcit_tiny_24_p8_384_dist 44.75 712.159 32 384 12.11
648 cait_s24_384 44.7 355.627 16 384 47.06
649 efficientnet_b6 41.67 381.28 16 528 43.04
650 tf_efficientnet_b6_ap 41.13 386.3 16 528 43.04
651 tf_efficientnet_b6 41.11 386.574 16 528 43.04
652 tf_efficientnet_b6_ns 40.99 387.676 16 528 43.04
653 xcit_large_24_p16_384_dist 40.96 582.937 24 384 189.1
654 vit_huge_patch14_224 37.91 420.175 16 224 632.05
655 resnetrs350 36.74 646.103 24 384 163.96
656 swin_large_patch4_window12_384 36.6 435.55 16 384 196.74
657 nfnet_f3 34.36 694.502 24 416 254.92
658 nfnet_f3s 34.26 696.883 24 416 254.92
659 convnext_xlarge_384_in22ft1k 34.01 468.853 16 384 350.2
660 resnest269e 32.96 721.9 24 416 110.93
661 dm_nfnet_f3 31.6 755.444 24 416 254.92
662 resnetv2_50x3_bitm 31.14 513.017 16 448 217.32
663 xcit_large_24_p8_224_dist 30.37 523.887 16 224 188.93
664 xcit_large_24_p8_224 30.32 524.75 16 224 188.93
665 cait_s36_384 29.46 404.043 12 384 68.37
666 resnetv2_152x2_bit_teacher_384 29.25 408.083 12 384 236.34
667 tf_efficientnetv2_xl_in21ft1k 28.52 415.17 12 512 208.12
668 efficientnetv2_xl 28.5 415.628 12 512 208.12
669 ig_resnext101_32x48d 27.26 585.456 16 224 828.41
670 vit_large_patch16_384 26.62 299.076 8 384 304.72
671 efficientnet_b7 25.52 310.186 8 600 66.35
672 tf_efficientnet_b7 25.25 313.569 8 600 66.35
673 tf_efficientnet_b7_ap 25.18 314.571 8 600 66.35
674 tf_efficientnet_b7_ns 25.12 315.222 8 600 66.35
675 resnetrs420 24.94 632.993 16 416 191.89
676 xcit_small_24_p8_384_dist 23.58 505.912 12 384 47.63
677 beit_large_patch16_384 22.66 351.145 8 384 305.0
678 vit_giant_patch14_224 21.59 368.118 8 224 1012.61
679 resnetv2_152x2_bitm 21.27 373.944 8 448 236.34
680 nfnet_f4 18.45 645.682 12 512 316.07
681 nfnet_f4s 18.35 649.188 12 512 316.07
682 resnetv2_101x3_bitm 17.19 464.077 8 448 387.93
683 xcit_medium_24_p8_384_dist 17.03 466.878 8 384 84.32
684 dm_nfnet_f4 16.71 713.246 12 512 316.07
685 efficientnet_b8 12.85 463.389 6 672 87.41
686 tf_efficientnet_b8_ap 12.75 466.866 6 672 87.41
687 tf_efficientnet_b8 12.73 467.671 6 672 87.41
688 nfnet_f5 12.01 660.317 8 544 377.21
689 nfnet_f5s 11.94 664.267 8 544 377.21
690 cait_m36_384 11.32 526.589 6 384 271.22
691 dm_nfnet_f5 10.99 722.011 8 544 377.21
692 xcit_large_24_p8_384_dist 10.37 575.649 6 384 188.93
693 nfnet_f6 9.06 655.713 6 576 438.36
694 nfnet_f6s 9.04 657.589 6 576 438.36
695 tf_efficientnet_l2_ns_475 8.8 449.624 4 475 480.31
696 beit_large_patch16_512 8.36 356.829 3 512 305.67
697 dm_nfnet_f6 8.32 713.582 6 576 438.36
698 nfnet_f7 6.44 613.323 4 608 499.5
699 nfnet_f7s 6.38 619.793 4 608 499.5
700 cait_m48_448 4.6 430.517 2 448 356.46
701 resnetv2_152x4_bitm 4.42 450.22 2 480 936.53
702 tf_efficientnet_l2_ns 2.55 387.178 1 800 480.31
703 efficientnet_l2 2.52 391.57 1 800 480.31

@ -0,0 +1,699 @@
model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
mobilenetv3_small_050,10124.2,49.732,512,224,1.59
lcnet_035,9556.39,53.122,512,224,1.64
tinynet_e,9259.87,53.163,512,106,2.04
lcnet_050,8204.13,61.959,512,224,1.88
tf_mobilenetv3_small_minimal_100,8115.25,62.582,512,224,2.04
tinynet_d,7842.33,64.591,512,152,2.34
mobilenetv3_small_075,7645.76,66.298,512,224,2.04
mobilenetv3_small_100,7391.62,68.594,512,224,2.54
tf_mobilenetv3_small_075,7004.36,72.432,512,224,2.04
tf_mobilenetv3_small_100,6775.26,74.902,512,224,2.54
levit_128s,6249.41,81.115,512,224,7.78
lcnet_075,5706.1,89.275,512,224,2.36
lcnet_100,5288.36,96.356,512,224,2.95
mnasnet_small,4565.41,111.287,512,224,2.03
mnasnet_050,4479.4,113.569,512,224,2.22
levit_128,4339.94,116.946,512,224,9.21
mobilenetv2_035,4152.95,122.551,512,224,1.68
mixer_s32_224,4131.98,123.414,512,224,19.1
tinynet_c,4071.23,124.835,512,184,2.46
gernet_s,4039.7,126.102,512,224,8.17
semnasnet_050,3974.04,127.989,512,224,2.08
levit_192,3756.95,135.269,512,224,10.95
vit_small_patch32_224,3671.47,138.697,512,224,22.88
lcnet_150,3645.88,139.972,512,224,4.5
ssl_resnet18,3513.72,145.398,512,224,11.69
gluon_resnet18_v1b,3512.34,145.445,512,224,11.69
swsl_resnet18,3508.0,145.642,512,224,11.69
mobilenetv2_050,3501.31,145.498,512,224,1.97
resnet18,3496.86,146.104,512,224,11.69
vit_tiny_r_s16_p8_224,3335.03,152.754,512,224,6.34
mobilenetv3_large_075,3296.24,154.517,512,224,3.99
ese_vovnet19b_slim_dw,3204.84,159.295,512,224,1.9
tf_mobilenetv3_large_minimal_100,3175.97,160.546,512,224,3.92
seresnet18,3165.67,161.261,512,224,11.78
legacy_seresnet18,3081.19,165.707,512,224,11.78
mnasnet_075,3078.06,165.602,512,224,3.17
tf_mobilenetv3_large_075,3052.6,166.905,512,224,3.99
ghostnet_050,2997.88,169.549,512,224,2.59
mobilenetv3_rw,2978.91,171.068,512,224,5.48
mobilenetv3_large_100_miil,2954.41,172.485,512,224,5.48
mobilenetv3_large_100,2949.17,172.802,512,224,5.48
levit_256,2853.53,178.318,512,224,18.89
hardcorenas_a,2833.05,180.061,512,224,5.26
mnasnet_100,2794.45,182.489,512,224,4.38
mnasnet_b1,2793.42,182.553,512,224,4.38
resnet18d,2767.22,184.677,512,224,11.71
tf_mobilenetv3_large_100,2745.99,185.646,512,224,5.48
tinynet_b,2724.35,186.966,512,188,3.73
hardcorenas_b,2641.84,192.939,512,224,5.18
semnasnet_075,2627.64,193.989,512,224,2.91
hardcorenas_c,2601.15,195.937,512,224,5.52
ese_vovnet19b_slim,2546.07,200.754,512,224,3.17
mobilenetv2_075,2520.15,202.421,512,224,2.64
spnasnet_100,2483.13,205.29,512,224,4.42
tf_efficientnetv2_b0,2462.53,206.791,512,224,7.14
hardcorenas_d,2435.65,209.09,512,224,7.5
mnasnet_a1,2380.33,214.252,512,224,3.89
semnasnet_100,2377.81,214.477,512,224,3.89
mobilenetv2_100,2363.97,215.837,512,224,3.5
regnetx_002,2307.34,221.263,512,224,2.68
tinynet_a,2237.07,227.684,512,192,6.19
fbnetc_100,2225.25,229.187,512,224,5.57
ghostnet_100,2211.89,230.191,512,224,5.18
regnety_002,2204.25,231.393,512,224,3.16
efficientnet_lite0,2189.09,233.188,512,224,4.65
hardcorenas_f,2163.36,235.624,512,224,8.2
hardcorenas_e,2139.96,238.217,512,224,8.07
tf_efficientnet_lite0,2037.27,250.609,512,224,4.65
resnet34,2037.12,250.805,512,224,21.8
tv_resnet34,2035.85,250.969,512,224,21.8
gluon_resnet34_v1b,2035.71,250.984,512,224,21.8
skresnet18,2026.36,252.064,512,224,11.96
resnet26,1936.86,263.917,512,224,16.0
resnetblur18,1923.61,265.837,512,224,11.69
gernet_m,1870.52,273.071,512,224,21.14
ese_vovnet19b_dw,1864.29,274.174,512,224,6.54
nf_resnet26,1859.82,274.839,512,224,16.0
mnasnet_140,1826.42,279.575,512,224,7.12
hrnet_w18_small,1825.55,279.196,512,224,13.19
seresnet34,1821.14,280.321,512,224,21.96
legacy_seresnet34,1770.27,288.393,512,224,21.96
efficientnet_b0,1763.31,216.796,384,224,5.29
resnet34d,1762.59,289.926,512,224,21.82
mobilenetv2_110d,1752.61,218.148,384,224,4.52
levit_384,1748.52,291.817,512,224,39.13
rexnetr_100,1739.4,219.681,384,224,4.88
selecsls42,1711.06,298.631,512,224,30.35
selecsls42b,1703.95,299.892,512,224,32.46
vit_tiny_patch16_224,1668.2,306.156,512,224,5.72
deit_tiny_patch16_224,1666.4,306.494,512,224,5.72
tf_efficientnet_b0_ap,1660.27,230.299,384,224,5.29
tf_efficientnet_b0_ns,1659.78,230.359,384,224,5.29
tf_efficientnet_b0,1659.12,230.455,384,224,5.29
deit_tiny_distilled_patch16_224,1644.53,310.585,512,224,5.91
semnasnet_140,1620.0,315.219,512,224,6.11
efficientnet_es_pruned,1595.68,320.161,512,224,5.44
efficientnet_es,1595.53,320.205,512,224,5.44
mixer_b32_224,1593.71,320.545,512,224,60.29
tf_efficientnet_es,1592.03,320.904,512,224,5.44
resnet26d,1580.82,323.426,512,224,16.01
tf_efficientnetv2_b1,1573.77,242.663,384,240,8.14
ghostnet_130,1567.24,325.459,512,224,7.36
repvgg_b0,1556.65,327.916,512,224,15.82
pit_ti_distilled_224,1537.82,332.146,512,224,5.1
pit_ti_224,1535.4,332.663,512,224,4.85
mobilenetv2_140,1525.48,250.968,384,224,6.11
resmlp_12_224,1516.23,336.972,512,224,15.35
resmlp_12_distilled_224,1515.94,336.996,512,224,15.35
vit_base_patch32_224,1491.42,342.538,512,224,88.22
vit_base_patch32_224_sam,1490.35,342.802,512,224,88.22
selecsls60,1450.31,352.185,512,224,30.67
selecsls60b,1447.33,352.935,512,224,32.77
nf_seresnet26,1446.95,353.243,512,224,17.4
xcit_nano_12_p16_224,1428.16,356.892,512,224,3.05
xcit_nano_12_p16_224_dist,1427.07,357.115,512,224,3.05
efficientnet_lite1,1402.56,181.634,256,240,5.42
mixer_s16_224,1387.35,368.544,512,224,18.53
efficientnet_b1_pruned,1356.21,376.138,512,240,6.33
dla46_c,1347.45,379.298,512,224,1.3
rexnetr_130,1335.58,190.622,256,224,7.61
nf_ecaresnet26,1333.44,383.458,512,224,16.0
tf_efficientnet_lite1,1330.92,191.449,256,240,5.42
mobilenetv2_120d,1299.02,195.983,256,224,5.83
resnetv2_50,1277.54,400.053,512,224,25.55
rexnet_100,1259.4,303.853,384,224,4.8
gernet_l,1250.61,408.582,512,256,31.08
pit_xs_224,1243.31,411.016,512,224,10.62
pit_xs_distilled_224,1243.18,411.013,512,224,11.0
gmixer_12_224,1232.53,414.68,512,224,12.7
resnet26t,1208.16,423.321,512,256,16.01
vit_small_patch32_384,1207.26,423.36,512,384,22.92
tv_resnet50,1193.37,321.016,384,224,25.56
ssl_resnet50,1193.18,321.076,384,224,25.56
gluon_resnet50_v1b,1192.95,321.138,384,224,25.56
resnet50,1192.59,321.245,384,224,25.56
swsl_resnet50,1191.64,321.479,384,224,25.56
rexnetr_150,1171.61,217.431,256,224,9.78
fbnetv3_b,1163.46,328.537,384,256,8.6
regnetx_004,1151.84,443.491,512,224,5.16
botnet26t_256,1146.74,334.32,384,256,12.49
skresnet34,1146.67,445.45,512,224,22.28
tf_efficientnetv2_b2,1137.18,223.699,256,260,10.1
fbnetv3_d,1123.37,226.192,256,256,10.31
resnetv2_50t,1114.22,458.774,512,224,25.57
gluon_resnet50_v1c,1114.01,343.942,384,224,25.58
repvgg_a2,1113.97,458.853,512,224,28.21
resnetv2_50d,1111.46,459.91,512,224,25.57
halonet26t,1094.74,350.238,384,256,12.48
efficientnet_lite2,1085.96,234.846,256,260,6.09
dla34,1074.06,476.134,512,224,15.74
mixnet_s,1060.15,481.782,512,224,4.13
resnet50d,1054.46,363.395,384,224,25.58
gluon_resnet50_v1d,1053.62,363.691,384,224,25.58
resnet50t,1052.26,364.133,384,224,25.57
vit_tiny_r_s16_p8_384,1046.6,366.108,384,384,6.36
xcit_tiny_12_p16_224_dist,1038.68,491.328,512,224,6.72
xcit_tiny_12_p16_224,1038.06,491.593,512,224,6.72
tf_efficientnet_lite2,1032.64,247.013,256,260,6.09
legacy_seresnext26_32x4d,1027.19,497.849,512,224,16.79
resnet32ts,1007.64,253.517,256,256,17.96
tf_efficientnet_b1_ap,1002.39,253.996,256,240,7.79
tf_efficientnet_b1,1001.08,254.315,256,240,7.79
tf_efficientnet_b1_ns,1000.92,254.412,256,240,7.79
res2net50_48w_2s,999.09,383.596,384,224,25.29
vit_small_resnet26d_224,998.6,383.579,384,224,63.61
resnet33ts,996.53,256.337,256,256,19.68
seresnext26d_32x4d,978.97,391.64,384,224,16.81
vovnet39a,977.19,523.377,512,224,22.6
seresnext26tn_32x4d,976.94,392.442,384,224,16.81
seresnext26t_32x4d,976.02,392.82,384,224,16.81
legacy_seresnet50,965.33,396.753,384,224,28.09
tf_efficientnet_em,961.83,265.228,256,240,6.9
efficientnet_em,961.29,265.407,256,240,6.9
tf_mixnet_s,959.89,532.224,512,224,4.13
dla46x_c,958.36,533.526,512,224,1.07
eca_resnet33ts,943.71,270.653,256,256,19.68
seresnet33ts,943.39,270.622,256,256,19.78
crossvit_tiny_240,940.57,406.823,384,240,7.01
crossvit_9_240,937.22,408.452,384,240,8.55
efficientnet_b1,935.15,272.379,256,256,7.79
seresnet50,934.74,409.786,384,224,28.09
regnety_004,931.71,548.466,512,224,4.34
gluon_resnet50_v1s,928.8,412.664,384,224,25.68
ecaresnetlight,928.23,550.753,512,224,30.16
resnetaa50d,926.64,413.614,384,224,25.58
dla60,926.01,413.77,384,224,22.04
ecaresnext26t_32x4d,925.94,414.209,384,224,15.41
ecaresnext50t_32x4d,924.8,414.714,384,224,15.41
ese_vovnet39b,919.92,555.952,512,224,24.57
convit_tiny,917.45,417.618,384,224,5.71
crossvit_9_dagger_240,912.87,419.313,384,240,8.78
gcresnet33ts,912.73,279.541,256,256,19.88
eca_vovnet39b,910.35,561.796,512,224,22.6
gmlp_ti16_224,908.97,421.011,384,224,5.87
dla60x_c,908.47,562.709,512,224,1.32
cspresnet50,908.32,421.991,384,256,21.62
vgg11,905.72,565.146,512,224,132.86
resnetblur50,901.02,425.435,384,224,25.56
ecaresnet50d_pruned,892.5,572.812,512,224,19.94
rexnetr_200,885.64,215.704,192,224,16.52
lambda_resnet26rpt_256,881.41,217.279,192,256,10.99
vit_small_patch16_224,879.33,435.939,384,224,22.05
deit_small_patch16_224,876.6,437.293,384,224,22.05
nf_seresnet50,866.69,441.953,384,224,28.09
dpn68b,864.02,443.325,384,224,12.61
cspresnext50,863.58,443.862,384,224,20.57
cspresnet50w,858.14,446.674,384,256,28.12
selecsls84,852.15,599.694,512,224,50.95
hrnet_w18_small_v2,849.12,600.799,512,224,15.6
cspresnet50d,847.29,452.406,384,256,21.64
deit_small_distilled_patch16_224,846.66,301.609,256,224,22.44
seresnet50t,844.69,453.54,384,224,28.1
resnetrs50,843.24,454.312,384,224,35.69
densenet121,835.01,304.929,256,224,7.98
tv_densenet121,832.77,305.766,256,224,7.98
rexnet_150,829.93,307.376,256,224,9.73
gluon_resnext50_32x4d,824.86,464.778,384,224,25.03
tv_resnext50_32x4d,824.06,465.237,384,224,25.03
ssl_resnext50_32x4d,823.91,465.298,384,224,25.03
swsl_resnext50_32x4d,823.16,465.728,384,224,25.03
resnext50_32x4d,823.01,465.815,384,224,25.03
dpn68,816.18,469.476,384,224,12.61
res2net50_26w_4s,812.07,314.023,256,224,25.7
efficientnet_b2_pruned,811.6,314.06,256,260,8.31
resnetblur50d,810.86,314.915,256,224,25.58
pit_s_224,809.09,315.601,256,224,23.46
pit_s_distilled_224,808.91,315.694,256,224,24.04
vovnet57a,808.86,473.9,384,224,36.64
skresnet50,802.36,317.782,256,224,25.8
densenet121d,795.36,320.183,256,224,8.0
vit_base_resnet26d_224,792.88,483.157,384,224,101.4
rexnet_130,792.23,322.087,256,224,7.56
tf_efficientnet_b2_ns,791.33,241.259,192,260,9.11
tf_efficientnet_b2_ap,790.24,241.588,192,260,9.11
regnetx_006,788.72,648.389,512,224,6.2
tf_efficientnet_b2,788.26,242.2,192,260,9.11
vgg11_bn,788.01,487.085,384,224,132.87
nf_ecaresnet50,787.45,486.772,384,224,25.56
regnety_006,779.2,656.099,512,224,6.06
gcresnet50t,776.26,328.367,256,256,25.9
ese_vovnet57b,765.66,500.673,384,224,38.61
ecaresnet50d,761.68,335.218,256,224,25.58
resnext50d_32x4d,757.82,337.002,256,224,25.05
adv_inception_v3,756.25,506.326,384,299,23.83
tf_inception_v3,754.65,507.545,384,299,23.83
gluon_inception_v3,754.57,507.572,384,299,23.83
inception_v3,753.64,508.224,384,299,23.83
seresnetaa50d,751.96,339.385,256,224,28.11
res2net50_14w_8s,748.86,339.851,256,224,25.06
resmlp_24_distilled_224,743.59,342.912,256,224,30.02
resmlp_24_224,743.53,342.937,256,224,30.02
resnetv2_101,742.52,343.363,256,224,44.54
skresnet50d,738.05,345.557,256,224,25.82
sehalonet33ts,731.23,349.345,256,256,13.69
dla60_res2net,730.56,349.091,256,224,20.85
densenetblur121d,729.66,349.185,256,224,8.0
resnet101,719.46,354.412,256,224,44.55
tv_resnet101,719.29,354.488,256,224,44.55
gluon_resnet101_v1b,719.28,354.459,256,224,44.55
efficientnet_b2a,707.95,269.81,192,288,9.11
efficientnet_b2,707.16,270.111,192,288,9.11
nf_resnet50,703.13,545.338,384,288,25.56
nf_resnet101,702.76,544.906,384,224,44.55
ecaresnet101d_pruned,698.97,730.858,512,224,24.88
densenet169,691.74,367.774,256,224,14.15
gluon_resnet101_v1c,690.21,369.47,256,224,44.57
gluon_seresnext50_32x4d,689.84,370.073,256,224,27.56
legacy_seresnext50_32x4d,688.18,370.947,256,224,27.56
seresnext50_32x4d,688.18,370.943,256,224,27.56
resnetv2_101d,686.94,371.274,256,224,44.56
darknet53,683.71,373.676,256,256,41.61
dla60x,680.87,375.081,256,224,17.35
nf_regnet_b0,680.47,751.331,512,256,8.76
efficientnetv2_rw_t,669.58,284.789,192,288,13.65
gluon_resnet101_v1d,666.43,382.723,256,224,44.57
regnety_008,657.21,778.104,512,224,6.26
rexnet_200,656.53,291.353,192,224,16.37
tf_efficientnetv2_b3,655.17,291.405,192,300,14.36
vit_small_r26_s32_224,652.39,391.27,256,224,36.43
sebotnet33ts_256,650.1,196.14,128,256,13.7
wide_resnet50_2,646.12,593.536,384,224,68.88
vgg13,645.37,594.836,384,224,133.05
dla102,640.56,398.166,256,224,33.27
vit_base_resnet50d_224,619.99,411.439,256,224,110.97
gluon_resnet101_v1s,614.53,415.124,256,224,44.67
resnext26ts,613.38,625.548,384,256,10.3
resnetaa101d,612.93,416.224,256,224,44.57
ecaresnet26t,610.98,418.477,256,320,16.01
gmixer_24_224,610.59,417.896,256,224,24.72
repvgg_b1,609.77,838.686,512,224,57.42
eca_botnext26ts_256,609.21,419.672,256,256,10.59
eca_halonext26ts,598.72,427.016,256,256,10.76
gc_efficientnetv2_rw_t,597.14,319.011,192,288,13.68
eca_resnext26ts,593.02,431.168,256,256,10.3
seresnext26ts,592.35,431.553,256,256,10.39
regnetx_008,587.44,870.811,512,224,7.26
convnext_tiny,584.52,437.049,256,224,28.59
coat_lite_tiny,583.0,657.845,384,224,5.72
convnext_tiny_hnf,582.18,438.813,256,224,28.59
gcresnext26ts,581.42,439.532,256,256,10.48
mixnet_m,576.71,664.443,384,224,5.01
resnetv2_50x1_bit_distilled,575.65,332.777,192,224,25.55
cspdarknet53,570.42,447.825,256,256,27.64
legacy_seresnet101,570.04,447.058,256,224,49.33
halonet50ts,568.71,336.701,192,256,22.73
res2net50_26w_6s,565.22,451.306,256,224,37.05
resnetblur101d,562.81,453.413,256,224,44.57
seresnet101,560.51,454.74,256,224,49.33
coat_lite_mini,559.21,685.884,384,224,11.01
xcit_small_12_p16_224,558.21,456.962,256,224,26.25
xcit_small_12_p16_224_dist,557.95,457.219,256,224,26.25
vgg13_bn,555.55,460.55,256,224,133.05
tf_efficientnet_lite3,555.29,229.476,128,300,8.2
efficientnet_lite3,554.26,229.924,128,300,8.2
tf_mixnet_m,541.25,708.06,384,224,5.01
ssl_resnext101_32x4d,539.66,472.944,256,224,44.18
gluon_resnext101_32x4d,539.41,473.189,256,224,44.18
xcit_tiny_24_p16_224_dist,538.89,472.126,256,224,12.12
resnext101_32x4d,538.71,473.739,256,224,44.18
xcit_tiny_24_p16_224,538.48,472.477,256,224,12.12
swsl_resnext101_32x4d,538.39,474.037,256,224,44.18
densenet201,535.33,355.953,192,224,20.01
bat_resnext26ts,534.3,478.021,256,256,10.73
twins_svt_small,529.33,482.243,256,224,24.06
mixer_b16_224,521.89,489.791,256,224,59.88
mixer_b16_224_miil,521.33,490.33,256,224,59.88
vgg16,519.79,738.569,384,224,138.36
nf_seresnet101,519.55,490.612,256,224,49.33
gmlp_s16_224,514.57,371.708,192,224,19.42
crossvit_small_240,514.25,371.914,192,240,26.86
resnetv2_152,514.07,495.927,256,224,60.19
res2net101_26w_4s,512.94,372.022,192,224,45.21
resnetv2_50d_gn,512.21,374.104,192,224,25.57
botnet50ts_256,511.02,249.57,128,256,22.74
vit_base_r26_s32_224,509.73,375.506,192,224,101.38
efficientnet_b3_pruned,508.52,501.852,256,300,9.86
nf_regnet_b3,507.41,502.895,256,320,18.59
res2next50,500.31,510.5,256,224,24.67
xcit_nano_12_p16_384_dist,497.26,513.197,256,384,3.05
dla102x,495.72,385.887,192,224,26.31
resnet50_gn,494.95,387.141,192,224,25.56
mixnet_l,494.74,516.045,256,224,7.33
resnet152,490.44,389.409,192,224,60.19
gluon_resnet152_v1b,490.37,389.44,192,224,60.19
tv_resnet152,490.23,389.552,192,224,60.19
resmlp_36_224,489.84,389.965,192,224,44.69
resmlp_36_distilled_224,489.66,390.129,192,224,44.69
cait_xxs24_224,487.15,523.231,256,224,11.96
mixer_l32_224,486.19,393.553,192,224,206.94
halo2botnet50ts_256,485.95,262.504,128,256,22.64
xception,485.66,262.809,128,299,22.86
visformer_tiny,484.78,1055.527,512,224,10.32
vit_large_patch32_224,484.04,527.472,256,224,306.54
vit_base_patch32_384,478.93,533.779,256,384,88.3
gluon_resnet152_v1c,476.8,400.507,192,224,60.21
resnetv2_152d,475.38,401.77,192,224,60.2
crossvit_15_240,472.73,404.523,192,240,27.53
res2net50_26w_8s,468.77,407.558,192,224,48.4
tf_mixnet_l,466.85,546.936,256,224,7.33
gluon_resnet152_v1d,465.06,410.759,192,224,60.21
ecaresnet101d,462.22,552.191,256,224,44.57
crossvit_15_dagger_240,459.76,415.927,192,240,28.21
vgg16_bn,457.66,559.065,256,224,138.37
nf_ecaresnet101,455.3,560.589,256,224,44.55
efficientnet_el,446.2,285.844,128,300,10.59
efficientnet_el_pruned,445.59,286.28,128,300,10.59
swin_tiny_patch4_window7_224,445.53,573.708,256,224,28.29
fbnetv3_g,442.28,287.506,128,288,16.62
gluon_resnet152_v1s,441.14,433.129,192,224,60.32
gluon_seresnext101_32x4d,437.16,437.191,192,224,48.96
tf_efficientnet_el,436.28,292.36,128,300,10.59
vgg19,436.15,880.196,384,224,143.67
seresnext101_32x4d,435.91,438.42,192,224,48.96
legacy_seresnext101_32x4d,435.08,439.246,192,224,48.96
ese_vovnet99b,434.62,587.614,256,224,63.2
skresnext50_32x4d,434.44,587.996,256,224,27.48
twins_pcpvt_small,433.1,589.648,256,224,24.11
dla60_res2next,431.53,591.928,256,224,17.03
dla169,427.18,447.18,192,224,53.39
hrnet_w32,426.75,445.609,192,224,41.23
hrnet_w18,413.83,614.275,256,224,21.3
convit_small,409.81,467.592,192,224,27.78
vit_tiny_patch16_384,409.3,311.972,128,384,5.79
vit_small_resnet50d_s16_224,407.06,470.553,192,224,57.53
vgg19_bn,388.74,658.182,256,224,143.68
ecaresnet50t,388.23,328.837,128,320,25.57
inception_v4,387.4,493.551,192,299,42.68
tf_efficientnet_b3_ap,384.58,331.269,128,300,12.23
tf_efficientnet_b3_ns,384.22,331.517,128,300,12.23
tf_efficientnet_b3,384.13,331.655,128,300,12.23
legacy_seresnet152,382.75,498.635,192,224,66.82
xcit_nano_12_p8_224,378.83,674.154,256,224,3.05
xcit_nano_12_p8_224_dist,378.65,674.433,256,224,3.05
convnext_small,375.68,509.406,192,224,50.22
regnetx_016,374.21,1367.378,512,224,9.19
haloregnetz_b,372.27,686.285,256,224,11.68
densenet161,371.62,342.252,128,224,28.68
repvgg_b2,368.23,1389.47,512,224,89.02
seresnet152,362.95,349.646,128,224,66.82
nest_tiny,356.9,357.853,128,224,17.06
dla102x2,355.19,358.924,128,224,41.28
vit_base_patch16_224_miil,354.79,360.089,128,224,86.54
jx_nest_tiny,353.97,360.795,128,224,17.06
gluon_resnext101_64x4d,352.72,361.493,128,224,83.46
resnext101_64x4d,352.52,361.632,128,224,83.46
wide_resnet101_2,350.85,545.834,192,224,126.89
regnety_016,350.64,1458.484,512,224,11.2
vit_base_patch16_224,349.4,365.598,128,224,86.57
deit_base_patch16_224,349.3,365.691,128,224,86.57
vit_base_patch16_224_sam,349.14,365.832,128,224,86.57
resnet200,348.81,364.208,128,224,64.67
resnest14d,348.25,1469.78,512,224,10.61
efficientnet_b3a,345.42,368.96,128,320,12.23
efficientnet_b3,345.06,369.413,128,320,12.23
deit_base_distilled_patch16_224,344.74,370.536,128,224,87.34
crossvit_18_240,343.92,370.408,128,240,43.27
hrnet_w30,341.47,745.312,256,224,37.71
crossvit_18_dagger_240,335.19,380.045,128,240,44.27
ens_adv_inception_resnet_v2,334.4,571.029,192,299,55.84
inception_resnet_v2,334.03,571.741,192,299,55.84
efficientnet_lite4,332.63,191.154,64,380,13.01
nf_regnet_b1,327.56,1561.741,512,288,10.22
coat_lite_small,324.96,589.506,192,224,19.84
dpn92,324.94,786.493,256,224,37.67
resnet101d,323.43,394.33,128,320,44.57
mixnet_xl,322.47,593.686,192,224,11.9
beit_base_patch16_224,320.26,398.682,128,224,86.53
tf_efficientnet_lite4,319.09,199.328,64,380,13.01
gcresnext50ts,314.79,608.558,192,256,15.67
resnetrs101,314.59,404.798,128,288,63.62
twins_pcpvt_base,313.15,610.712,192,224,43.83
cait_xxs36_224,311.89,407.079,128,224,17.3
gluon_seresnext101_64x4d,306.13,416.102,128,224,88.23
twins_svt_base,305.99,416.477,128,224,56.07
repvgg_b3,305.95,1254.102,384,224,123.09
regnetz_d8,301.65,210.668,64,320,23.37
xception41,300.49,424.83,128,299,26.97
efficientnetv2_s,292.99,325.601,96,384,21.46
ese_vovnet39b_evos,292.82,436.279,128,224,24.58
efficientnetv2_rw_s,289.9,218.715,64,384,23.94
tf_efficientnetv2_s,289.31,329.828,96,384,21.46
swin_small_patch4_window7_224,289.15,441.029,128,224,49.61
tf_efficientnetv2_s_in21ft1k,288.88,330.272,96,384,21.46
xcit_small_24_p16_224_dist,286.23,444.237,128,224,47.67
xcit_small_24_p16_224,286.12,444.416,128,224,47.67
hrnet_w40,285.91,667.227,192,224,57.56
pit_b_224,285.13,335.829,96,224,73.76
pit_b_distilled_224,284.45,336.631,96,224,74.79
nf_regnet_b2,282.04,1813.867,512,272,14.31
gluon_xception65,276.41,345.52,96,299,39.92
dpn98,275.63,462.928,128,224,61.57
xception65,275.32,346.928,96,299,39.92
resnet51q,274.2,699.442,192,288,35.7
convnext_base_in22ft1k,273.43,466.529,128,224,88.59
convnext_base,272.92,467.434,128,224,88.59
nf_regnet_b4,271.03,470.306,128,384,30.21
xcit_tiny_12_p8_224,270.45,471.679,128,224,6.71
xcit_tiny_12_p8_224_dist,270.45,471.621,128,224,6.71
cait_s24_224,268.53,474.373,128,224,46.92
hrnet_w48,262.05,484.085,128,224,77.47
resnest26d,254.99,1505.218,384,224,17.07
resnest50d_1s4x24d,252.9,1011.055,256,224,25.68
resnetv2_50d_evob,247.25,775.496,192,224,25.59
regnetz_c16,246.47,517.947,128,320,13.46
gmlp_b16_224,243.4,392.966,96,224,73.08
hrnet_w44,236.76,806.557,192,224,67.06
twins_svt_large,235.13,542.578,128,224,99.27
efficientnet_b4,234.85,270.618,64,384,19.34
nest_small,234.57,407.797,96,224,38.35
resnetv2_50d_evos,233.18,410.671,96,224,25.59
jx_nest_small,233.1,410.378,96,224,38.35
tf_efficientnet_b4_ap,230.34,275.916,64,380,19.34
tresnet_m,230.07,2222.819,512,224,31.39
tf_efficientnet_b4,229.98,276.378,64,380,19.34
tf_efficientnet_b4_ns,229.78,276.586,64,380,19.34
visformer_small,229.49,1672.652,384,224,40.22
crossvit_base_240,228.79,418.11,96,240,105.03
nfnet_l0,228.47,1119.415,256,288,35.07
coat_tiny,228.08,559.427,128,224,5.5
twins_pcpvt_large,223.01,570.431,128,224,60.99
eca_nfnet_l0,221.69,1153.795,256,288,24.14
vit_large_r50_s32_224,220.98,432.297,96,224,328.99
swin_base_patch4_window7_224,220.89,577.812,128,224,87.77
densenet264,217.72,437.317,96,224,72.69
hrnet_w64,216.27,587.374,128,224,128.06
convit_base,213.62,448.479,96,224,86.54
xception71,212.24,299.632,64,299,42.34
vit_small_r26_s32_384,211.98,300.724,64,384,36.47
resnet152d,211.82,451.04,96,320,60.21
vit_small_patch16_384,209.76,304.352,64,384,22.2
xcit_medium_24_p16_224,207.55,459.61,96,224,84.4
xcit_medium_24_p16_224_dist,207.03,460.529,96,224,84.4
resnet61q,206.68,618.373,128,288,36.85
dpn131,205.78,620.133,128,224,79.25
mixnet_xxl,204.23,624.949,128,224,23.96
vit_base_r50_s16_224,195.65,325.59,64,224,98.66
coat_mini,193.15,660.874,128,224,10.34
regnety_040,193.14,1986.785,384,224,20.65
xcit_small_12_p16_384_dist,191.88,498.659,96,384,26.25
xcit_tiny_24_p16_384_dist,186.09,512.939,96,384,12.12
seresnet200d,185.21,514.398,96,256,71.86
regnetz_b16,179.85,1066.235,192,288,9.72
repvgg_b1g4,177.2,2888.374,512,224,39.97
ecaresnet200d,176.34,541.265,96,256,64.69
nest_base,176.15,361.868,64,224,67.72
convmixer_768_32,176.07,543.988,96,224,21.11
resnest50d,175.35,1458.742,256,224,27.48
regnety_032,175.25,1459.459,256,288,19.44
jx_nest_base,174.95,364.333,64,224,67.72
regnetx_032,174.19,2203.311,384,224,15.3
senet154,172.45,553.616,96,224,115.09
gluon_senet154,172.38,553.858,96,224,115.09
legacy_senet154,171.96,555.183,96,224,115.09
seresnet152d,169.43,374.655,64,320,66.84
resnetrs152,167.98,377.927,64,320,86.62
dpn107,167.97,570.011,96,224,86.92
halonet_h1,167.27,381.366,64,256,8.1
resnest50d_4s2x40d,163.46,1564.906,256,224,30.42
resnet200d,163.41,388.892,64,320,64.69
mixer_l16_224,161.74,592.23,96,224,208.2
convnext_large_in22ft1k,160.8,595.418,96,224,197.77
convnext_large,160.72,595.739,96,224,197.77
regnetx_040,160.66,2389.077,384,224,22.12
tnt_s_patch16_224,157.73,809.855,128,224,23.76
regnetx_080,148.64,1721.236,256,224,39.57
efficientnetv2_m,147.13,323.331,48,416,54.14
resnext101_32x8d,145.87,876.049,128,224,88.79
ssl_resnext101_32x8d,145.3,879.52,128,224,88.79
vit_large_patch32_384,145.15,439.536,64,384,306.63
xcit_small_12_p8_224_dist,145.04,439.625,64,224,26.21
xcit_small_12_p8_224,144.99,439.796,64,224,26.21
ig_resnext101_32x8d,144.47,884.578,128,224,88.79
swsl_resnext101_32x8d,144.29,885.69,128,224,88.79
regnetz_e8,140.91,338.814,48,320,57.7
xcit_tiny_24_p8_224,139.28,686.242,96,224,12.11
xcit_tiny_24_p8_224_dist,139.16,686.889,96,224,12.11
resnetv2_50x1_bitm,137.88,347.358,48,448,25.55
seresnext101_32x8d,136.74,934.059,128,224,93.57
seresnet269d,134.18,471.701,64,256,113.67
swin_large_patch4_window7_224,132.61,480.998,64,224,196.53
xcit_nano_12_p8_384_dist,128.43,496.705,64,384,3.05
tnt_b_patch16_224,126.43,757.626,96,224,65.41
xcit_large_24_p16_224,124.67,510.437,64,224,189.1
xcit_large_24_p16_224_dist,124.62,510.639,64,224,189.1
efficientnetv2_rw_m,123.91,255.105,32,416,53.24
regnetx_064,123.53,2071.55,256,224,26.21
regnety_064,121.34,2108.152,256,224,30.58
resnetrs200,119.86,396.447,48,320,93.21
nfnet_f0,119.25,2145.698,256,256,71.49
nfnet_f0s,118.17,2165.276,256,256,71.49
ese_vovnet99b_iabn,116.31,3298.583,384,224,63.2
regnety_080,108.46,2359.307,256,224,39.18
tf_efficientnetv2_m_in21ft1k,107.43,294.9,32,480,54.14
vit_large_patch16_224,106.81,447.947,48,224,304.33
tf_efficientnetv2_m,106.77,296.673,32,480,54.14
convnext_xlarge_in22ft1k,104.57,610.376,64,224,350.2
crossvit_15_dagger_408,103.52,307.452,32,408,28.5
tresnet_l,102.9,4972.484,512,224,55.99
repvgg_b2g4,101.39,5048.708,512,224,61.76
eca_nfnet_l1,99.64,1282.96,128,320,41.41
xcit_small_24_p16_384_dist,98.62,483.709,48,384,47.67
beit_large_patch16_224,98.03,487.815,48,224,304.43
cspdarknet53_iabn,97.55,3934.064,384,256,27.64
efficientnet_b5,94.83,335.072,32,456,30.39
convnext_base_384_in22ft1k,93.76,510.323,48,384,88.59
tf_efficientnet_b5_ap,93.42,340.255,32,456,30.39
tf_efficientnet_b5_ns,93.39,340.338,32,456,30.39
tf_efficientnet_b5,93.26,340.852,32,456,30.39
xcit_tiny_12_p8_384_dist,92.1,519.585,48,384,6.71
tresnet_xl,89.9,4267.972,384,224,78.44
vit_base_patch16_384,89.9,355.185,32,384,86.86
deit_base_patch16_384,89.87,355.331,32,384,86.86
cspresnext50_iabn,89.4,4293.09,384,256,20.57
resnest101e,88.34,1446.581,128,256,48.28
deit_base_distilled_patch16_384,88.07,362.54,32,384,87.63
cait_xxs24_384,87.17,364.76,32,384,12.03
nf_regnet_b5,85.26,748.171,64,456,49.74
resnetv2_101x1_bitm,84.86,375.607,32,448,44.54
resnetv2_152x2_bit_teacher,84.71,375.537,32,224,236.34
repvgg_b3g4,83.61,4591.876,384,224,83.83
crossvit_18_dagger_408,77.92,306.154,24,408,44.61
regnetx_120,77.0,3323.571,256,224,46.11
beit_base_patch16_384,76.44,313.03,24,384,86.74
ecaresnet269d,75.74,418.332,32,352,102.09
regnety_120,75.43,2544.024,192,224,51.82
pnasnet5large,75.01,423.127,32,331,86.06
vit_large_r50_s32_384,73.58,432.769,32,384,329.09
xcit_small_24_p8_224_dist,73.31,433.498,32,224,47.63
xcit_small_24_p8_224,73.25,433.859,32,224,47.63
resnetrs270,73.04,432.615,32,352,129.86
regnetz_d32,71.19,897.484,64,320,27.58
dm_nfnet_f0,71.06,3601.578,256,256,71.49
xcit_medium_24_p16_384_dist,70.95,448.036,32,384,84.4
regnetz_d8_evob,68.84,695.406,48,320,23.41
regnety_320,66.69,1917.907,128,224,145.05
nasnetalarge,65.99,480.339,32,331,88.75
resmlp_big_24_distilled_224,65.96,483.761,32,224,129.14
resmlp_big_24_224,65.94,483.953,32,224,129.14
resmlp_big_24_224_in22ft1k,65.9,484.194,32,224,129.14
regnetx_160,65.04,2951.208,192,224,54.28
regnetz_d8_evos,62.47,510.384,32,320,23.46
cait_xs24_384,60.56,393.982,24,384,26.67
swin_base_patch4_window12_384,60.23,396.845,24,384,87.9
efficientnetv2_l,60.16,261.949,16,480,118.52
convmixer_1024_20_ks9_p14,59.76,4282.918,256,224,24.38
tf_efficientnetv2_l,59.73,263.768,16,480,118.52
tf_efficientnetv2_l_in21ft1k,59.31,265.732,16,480,118.52
vit_base_patch8_224,57.86,275.754,16,224,86.58
cait_xxs36_384,56.92,418.29,24,384,17.37
convnext_large_384_in22ft1k,54.87,581.624,32,384,197.77
xcit_medium_24_p8_224,54.52,584.052,32,224,84.32
xcit_medium_24_p8_224_dist,54.51,583.988,32,224,84.32
vit_base_resnet50_384,54.07,294.48,16,384,98.95
vit_base_r50_s16_384,54.05,294.543,16,384,98.95
tresnet_m_448,52.97,3622.14,192,448,31.39
xcit_small_12_p8_384_dist,49.7,481.259,24,384,26.21
regnety_160,48.21,1990.283,96,288,83.59
xcit_tiny_24_p8_384_dist,47.11,676.387,32,384,12.11
ssl_resnext101_32x16d,46.89,2045.721,96,224,194.03
swsl_resnext101_32x16d,46.82,2049.006,96,224,194.03
ig_resnext101_32x16d,46.52,2062.16,96,224,194.03
resnetrs350,46.21,512.496,24,384,163.96
regnetx_320,45.87,2789.512,128,224,107.81
cait_s24_384,45.29,350.995,16,384,47.06
eca_nfnet_l2,44.43,1438.009,64,384,56.72
xcit_large_24_p16_384_dist,43.19,552.749,24,384,189.1
efficientnet_b6,42.65,372.523,16,528,43.04
tf_efficientnet_b6,41.27,384.949,16,528,43.04
tf_efficientnet_b6_ap,41.07,386.945,16,528,43.04
tf_efficientnet_b6_ns,41.05,387.133,16,528,43.04
nfnet_f1,39.56,2424.624,96,320,132.63
nfnet_f1s,39.31,2439.907,96,320,132.63
vit_huge_patch14_224,38.12,417.919,16,224,632.05
dm_nfnet_f1,37.77,1692.371,64,320,132.63
swin_large_patch4_window12_384,36.73,433.938,16,384,196.74
efficientnet_b7,35.91,219.519,8,600,66.35
efficientnetv2_xl,35.88,329.245,12,512,208.12
tf_efficientnetv2_xl_in21ft1k,35.79,329.897,12,512,208.12
tf_efficientnet_b7,35.55,221.854,8,600,66.35
tf_efficientnet_b7_ns,35.4,222.801,8,600,66.35
tf_efficientnet_b7_ap,35.26,223.637,8,600,66.35
convnext_xlarge_384_in22ft1k,34.11,467.466,16,384,350.2
resnetrs420,31.93,492.311,16,416,191.89
xcit_large_24_p8_224_dist,31.85,499.415,16,224,188.93
xcit_large_24_p8_224,31.84,499.581,16,224,188.93
resnest200e,31.57,1515.738,48,320,70.2
densenet264d_iabn,31.46,4064.91,128,224,72.74
resnetv2_50x3_bitm,30.97,515.904,16,448,217.32
cait_s36_384,29.83,399.013,12,384,68.37
resnetv2_152x2_bit_teacher_384,29.16,409.309,12,384,236.34
vit_large_patch16_384,26.62,299.125,8,384,304.72
tresnet_l_448,25.11,5094.194,128,448,55.99
xcit_small_24_p8_384_dist,25.02,476.66,12,384,47.63
eca_nfnet_l3,23.96,1332.494,32,448,72.04
efficientnet_cc_b0_4e,23.7,40.469,1,224,13.31
efficientnet_cc_b0_8e,22.94,41.864,1,224,24.01
tresnet_xl_448,22.71,4223.097,96,448,78.44
beit_large_patch16_384,22.62,351.795,8,384,305.0
tf_efficientnet_cc_b0_8e,22.37,43.033,1,224,24.01
nfnet_f2,21.97,2181.861,48,352,193.78
nfnet_f2s,21.86,2193.026,48,352,193.78
vit_giant_patch14_224,21.57,368.599,8,224,1012.61
dm_nfnet_f2,21.42,1490.949,32,352,193.78
tf_efficientnet_cc_b0_4e,21.27,45.221,1,224,13.31
resnetv2_152x2_bitm,21.19,375.422,8,448,236.34
efficientnet_cc_b1_8e,18.42,52.102,1,240,39.72
xcit_medium_24_p8_384_dist,17.99,441.849,8,384,84.32
ig_resnext101_32x32d,17.78,1798.209,32,224,468.53
tf_efficientnet_cc_b1_8e,17.44,55.209,1,240,39.72
resnetv2_101x3_bitm,17.08,466.831,8,448,387.93
resnest269e,12.91,1852.797,24,416,110.93
nfnet_f3,11.98,1999.215,24,416,254.92
nfnet_f3s,11.88,2016.77,24,416,254.92
dm_nfnet_f3,11.64,2058.707,24,416,254.92
cait_m36_384,11.45,520.837,6,384,271.22
efficientnet_b8,11.27,528.827,6,672,87.41
tf_efficientnet_b8_ap,11.23,530.563,6,672,87.41
tf_efficientnet_b8,11.1,537.074,6,672,87.41
xcit_large_24_p8_384_dist,10.94,545.597,6,384,188.93
convmixer_1536_20,9.34,5136.027,48,224,51.63
tf_efficientnet_l2_ns_475,8.79,336.325,3,475,480.31
ig_resnext101_32x48d,8.38,1908.292,16,224,828.41
beit_large_patch16_512,8.36,357.133,3,512,305.67
nfnet_f4s,6.37,1879.688,12,512,316.07
nfnet_f4,6.34,1889.493,12,512,316.07
dm_nfnet_f4,6.16,1941.969,12,512,316.07
cait_m48_448,4.64,426.785,2,448,356.46
nfnet_f5,4.56,1747.564,8,544,377.21
nfnet_f5s,4.56,1750.367,8,544,377.21
dm_nfnet_f5,4.43,1800.804,8,544,377.21
nfnet_f6,3.47,1722.95,6,576,438.36
nfnet_f6s,3.47,1723.469,6,576,438.36
dm_nfnet_f6,3.34,1791.721,6,576,438.36
resnetv2_152x4_bitm,3.17,313.58,1,480,936.53
nfnet_f7,2.64,1507.256,4,608,499.5
nfnet_f7s,2.63,1515.779,4,608,499.5
efficientnet_l2,2.07,477.813,1,800,480.31
tf_efficientnet_l2_ns,2.03,488.22,1,800,480.31
1 model train_samples_per_sec train_step_time train_batch_size train_img_size param_count
2 mobilenetv3_small_050 10124.2 49.732 512 224 1.59
3 lcnet_035 9556.39 53.122 512 224 1.64
4 tinynet_e 9259.87 53.163 512 106 2.04
5 lcnet_050 8204.13 61.959 512 224 1.88
6 tf_mobilenetv3_small_minimal_100 8115.25 62.582 512 224 2.04
7 tinynet_d 7842.33 64.591 512 152 2.34
8 mobilenetv3_small_075 7645.76 66.298 512 224 2.04
9 mobilenetv3_small_100 7391.62 68.594 512 224 2.54
10 tf_mobilenetv3_small_075 7004.36 72.432 512 224 2.04
11 tf_mobilenetv3_small_100 6775.26 74.902 512 224 2.54
12 levit_128s 6249.41 81.115 512 224 7.78
13 lcnet_075 5706.1 89.275 512 224 2.36
14 lcnet_100 5288.36 96.356 512 224 2.95
15 mnasnet_small 4565.41 111.287 512 224 2.03
16 mnasnet_050 4479.4 113.569 512 224 2.22
17 levit_128 4339.94 116.946 512 224 9.21
18 mobilenetv2_035 4152.95 122.551 512 224 1.68
19 mixer_s32_224 4131.98 123.414 512 224 19.1
20 tinynet_c 4071.23 124.835 512 184 2.46
21 gernet_s 4039.7 126.102 512 224 8.17
22 semnasnet_050 3974.04 127.989 512 224 2.08
23 levit_192 3756.95 135.269 512 224 10.95
24 vit_small_patch32_224 3671.47 138.697 512 224 22.88
25 lcnet_150 3645.88 139.972 512 224 4.5
26 ssl_resnet18 3513.72 145.398 512 224 11.69
27 gluon_resnet18_v1b 3512.34 145.445 512 224 11.69
28 swsl_resnet18 3508.0 145.642 512 224 11.69
29 mobilenetv2_050 3501.31 145.498 512 224 1.97
30 resnet18 3496.86 146.104 512 224 11.69
31 vit_tiny_r_s16_p8_224 3335.03 152.754 512 224 6.34
32 mobilenetv3_large_075 3296.24 154.517 512 224 3.99
33 ese_vovnet19b_slim_dw 3204.84 159.295 512 224 1.9
34 tf_mobilenetv3_large_minimal_100 3175.97 160.546 512 224 3.92
35 seresnet18 3165.67 161.261 512 224 11.78
36 legacy_seresnet18 3081.19 165.707 512 224 11.78
37 mnasnet_075 3078.06 165.602 512 224 3.17
38 tf_mobilenetv3_large_075 3052.6 166.905 512 224 3.99
39 ghostnet_050 2997.88 169.549 512 224 2.59
40 mobilenetv3_rw 2978.91 171.068 512 224 5.48
41 mobilenetv3_large_100_miil 2954.41 172.485 512 224 5.48
42 mobilenetv3_large_100 2949.17 172.802 512 224 5.48
43 levit_256 2853.53 178.318 512 224 18.89
44 hardcorenas_a 2833.05 180.061 512 224 5.26
45 mnasnet_100 2794.45 182.489 512 224 4.38
46 mnasnet_b1 2793.42 182.553 512 224 4.38
47 resnet18d 2767.22 184.677 512 224 11.71
48 tf_mobilenetv3_large_100 2745.99 185.646 512 224 5.48
49 tinynet_b 2724.35 186.966 512 188 3.73
50 hardcorenas_b 2641.84 192.939 512 224 5.18
51 semnasnet_075 2627.64 193.989 512 224 2.91
52 hardcorenas_c 2601.15 195.937 512 224 5.52
53 ese_vovnet19b_slim 2546.07 200.754 512 224 3.17
54 mobilenetv2_075 2520.15 202.421 512 224 2.64
55 spnasnet_100 2483.13 205.29 512 224 4.42
56 tf_efficientnetv2_b0 2462.53 206.791 512 224 7.14
57 hardcorenas_d 2435.65 209.09 512 224 7.5
58 mnasnet_a1 2380.33 214.252 512 224 3.89
59 semnasnet_100 2377.81 214.477 512 224 3.89
60 mobilenetv2_100 2363.97 215.837 512 224 3.5
61 regnetx_002 2307.34 221.263 512 224 2.68
62 tinynet_a 2237.07 227.684 512 192 6.19
63 fbnetc_100 2225.25 229.187 512 224 5.57
64 ghostnet_100 2211.89 230.191 512 224 5.18
65 regnety_002 2204.25 231.393 512 224 3.16
66 efficientnet_lite0 2189.09 233.188 512 224 4.65
67 hardcorenas_f 2163.36 235.624 512 224 8.2
68 hardcorenas_e 2139.96 238.217 512 224 8.07
69 tf_efficientnet_lite0 2037.27 250.609 512 224 4.65
70 resnet34 2037.12 250.805 512 224 21.8
71 tv_resnet34 2035.85 250.969 512 224 21.8
72 gluon_resnet34_v1b 2035.71 250.984 512 224 21.8
73 skresnet18 2026.36 252.064 512 224 11.96
74 resnet26 1936.86 263.917 512 224 16.0
75 resnetblur18 1923.61 265.837 512 224 11.69
76 gernet_m 1870.52 273.071 512 224 21.14
77 ese_vovnet19b_dw 1864.29 274.174 512 224 6.54
78 nf_resnet26 1859.82 274.839 512 224 16.0
79 mnasnet_140 1826.42 279.575 512 224 7.12
80 hrnet_w18_small 1825.55 279.196 512 224 13.19
81 seresnet34 1821.14 280.321 512 224 21.96
82 legacy_seresnet34 1770.27 288.393 512 224 21.96
83 efficientnet_b0 1763.31 216.796 384 224 5.29
84 resnet34d 1762.59 289.926 512 224 21.82
85 mobilenetv2_110d 1752.61 218.148 384 224 4.52
86 levit_384 1748.52 291.817 512 224 39.13
87 rexnetr_100 1739.4 219.681 384 224 4.88
88 selecsls42 1711.06 298.631 512 224 30.35
89 selecsls42b 1703.95 299.892 512 224 32.46
90 vit_tiny_patch16_224 1668.2 306.156 512 224 5.72
91 deit_tiny_patch16_224 1666.4 306.494 512 224 5.72
92 tf_efficientnet_b0_ap 1660.27 230.299 384 224 5.29
93 tf_efficientnet_b0_ns 1659.78 230.359 384 224 5.29
94 tf_efficientnet_b0 1659.12 230.455 384 224 5.29
95 deit_tiny_distilled_patch16_224 1644.53 310.585 512 224 5.91
96 semnasnet_140 1620.0 315.219 512 224 6.11
97 efficientnet_es_pruned 1595.68 320.161 512 224 5.44
98 efficientnet_es 1595.53 320.205 512 224 5.44
99 mixer_b32_224 1593.71 320.545 512 224 60.29
100 tf_efficientnet_es 1592.03 320.904 512 224 5.44
101 resnet26d 1580.82 323.426 512 224 16.01
102 tf_efficientnetv2_b1 1573.77 242.663 384 240 8.14
103 ghostnet_130 1567.24 325.459 512 224 7.36
104 repvgg_b0 1556.65 327.916 512 224 15.82
105 pit_ti_distilled_224 1537.82 332.146 512 224 5.1
106 pit_ti_224 1535.4 332.663 512 224 4.85
107 mobilenetv2_140 1525.48 250.968 384 224 6.11
108 resmlp_12_224 1516.23 336.972 512 224 15.35
109 resmlp_12_distilled_224 1515.94 336.996 512 224 15.35
110 vit_base_patch32_224 1491.42 342.538 512 224 88.22
111 vit_base_patch32_224_sam 1490.35 342.802 512 224 88.22
112 selecsls60 1450.31 352.185 512 224 30.67
113 selecsls60b 1447.33 352.935 512 224 32.77
114 nf_seresnet26 1446.95 353.243 512 224 17.4
115 xcit_nano_12_p16_224 1428.16 356.892 512 224 3.05
116 xcit_nano_12_p16_224_dist 1427.07 357.115 512 224 3.05
117 efficientnet_lite1 1402.56 181.634 256 240 5.42
118 mixer_s16_224 1387.35 368.544 512 224 18.53
119 efficientnet_b1_pruned 1356.21 376.138 512 240 6.33
120 dla46_c 1347.45 379.298 512 224 1.3
121 rexnetr_130 1335.58 190.622 256 224 7.61
122 nf_ecaresnet26 1333.44 383.458 512 224 16.0
123 tf_efficientnet_lite1 1330.92 191.449 256 240 5.42
124 mobilenetv2_120d 1299.02 195.983 256 224 5.83
125 resnetv2_50 1277.54 400.053 512 224 25.55
126 rexnet_100 1259.4 303.853 384 224 4.8
127 gernet_l 1250.61 408.582 512 256 31.08
128 pit_xs_224 1243.31 411.016 512 224 10.62
129 pit_xs_distilled_224 1243.18 411.013 512 224 11.0
130 gmixer_12_224 1232.53 414.68 512 224 12.7
131 resnet26t 1208.16 423.321 512 256 16.01
132 vit_small_patch32_384 1207.26 423.36 512 384 22.92
133 tv_resnet50 1193.37 321.016 384 224 25.56
134 ssl_resnet50 1193.18 321.076 384 224 25.56
135 gluon_resnet50_v1b 1192.95 321.138 384 224 25.56
136 resnet50 1192.59 321.245 384 224 25.56
137 swsl_resnet50 1191.64 321.479 384 224 25.56
138 rexnetr_150 1171.61 217.431 256 224 9.78
139 fbnetv3_b 1163.46 328.537 384 256 8.6
140 regnetx_004 1151.84 443.491 512 224 5.16
141 botnet26t_256 1146.74 334.32 384 256 12.49
142 skresnet34 1146.67 445.45 512 224 22.28
143 tf_efficientnetv2_b2 1137.18 223.699 256 260 10.1
144 fbnetv3_d 1123.37 226.192 256 256 10.31
145 resnetv2_50t 1114.22 458.774 512 224 25.57
146 gluon_resnet50_v1c 1114.01 343.942 384 224 25.58
147 repvgg_a2 1113.97 458.853 512 224 28.21
148 resnetv2_50d 1111.46 459.91 512 224 25.57
149 halonet26t 1094.74 350.238 384 256 12.48
150 efficientnet_lite2 1085.96 234.846 256 260 6.09
151 dla34 1074.06 476.134 512 224 15.74
152 mixnet_s 1060.15 481.782 512 224 4.13
153 resnet50d 1054.46 363.395 384 224 25.58
154 gluon_resnet50_v1d 1053.62 363.691 384 224 25.58
155 resnet50t 1052.26 364.133 384 224 25.57
156 vit_tiny_r_s16_p8_384 1046.6 366.108 384 384 6.36
157 xcit_tiny_12_p16_224_dist 1038.68 491.328 512 224 6.72
158 xcit_tiny_12_p16_224 1038.06 491.593 512 224 6.72
159 tf_efficientnet_lite2 1032.64 247.013 256 260 6.09
160 legacy_seresnext26_32x4d 1027.19 497.849 512 224 16.79
161 resnet32ts 1007.64 253.517 256 256 17.96
162 tf_efficientnet_b1_ap 1002.39 253.996 256 240 7.79
163 tf_efficientnet_b1 1001.08 254.315 256 240 7.79
164 tf_efficientnet_b1_ns 1000.92 254.412 256 240 7.79
165 res2net50_48w_2s 999.09 383.596 384 224 25.29
166 vit_small_resnet26d_224 998.6 383.579 384 224 63.61
167 resnet33ts 996.53 256.337 256 256 19.68
168 seresnext26d_32x4d 978.97 391.64 384 224 16.81
169 vovnet39a 977.19 523.377 512 224 22.6
170 seresnext26tn_32x4d 976.94 392.442 384 224 16.81
171 seresnext26t_32x4d 976.02 392.82 384 224 16.81
172 legacy_seresnet50 965.33 396.753 384 224 28.09
173 tf_efficientnet_em 961.83 265.228 256 240 6.9
174 efficientnet_em 961.29 265.407 256 240 6.9
175 tf_mixnet_s 959.89 532.224 512 224 4.13
176 dla46x_c 958.36 533.526 512 224 1.07
177 eca_resnet33ts 943.71 270.653 256 256 19.68
178 seresnet33ts 943.39 270.622 256 256 19.78
179 crossvit_tiny_240 940.57 406.823 384 240 7.01
180 crossvit_9_240 937.22 408.452 384 240 8.55
181 efficientnet_b1 935.15 272.379 256 256 7.79
182 seresnet50 934.74 409.786 384 224 28.09
183 regnety_004 931.71 548.466 512 224 4.34
184 gluon_resnet50_v1s 928.8 412.664 384 224 25.68
185 ecaresnetlight 928.23 550.753 512 224 30.16
186 resnetaa50d 926.64 413.614 384 224 25.58
187 dla60 926.01 413.77 384 224 22.04
188 ecaresnext26t_32x4d 925.94 414.209 384 224 15.41
189 ecaresnext50t_32x4d 924.8 414.714 384 224 15.41
190 ese_vovnet39b 919.92 555.952 512 224 24.57
191 convit_tiny 917.45 417.618 384 224 5.71
192 crossvit_9_dagger_240 912.87 419.313 384 240 8.78
193 gcresnet33ts 912.73 279.541 256 256 19.88
194 eca_vovnet39b 910.35 561.796 512 224 22.6
195 gmlp_ti16_224 908.97 421.011 384 224 5.87
196 dla60x_c 908.47 562.709 512 224 1.32
197 cspresnet50 908.32 421.991 384 256 21.62
198 vgg11 905.72 565.146 512 224 132.86
199 resnetblur50 901.02 425.435 384 224 25.56
200 ecaresnet50d_pruned 892.5 572.812 512 224 19.94
201 rexnetr_200 885.64 215.704 192 224 16.52
202 lambda_resnet26rpt_256 881.41 217.279 192 256 10.99
203 vit_small_patch16_224 879.33 435.939 384 224 22.05
204 deit_small_patch16_224 876.6 437.293 384 224 22.05
205 nf_seresnet50 866.69 441.953 384 224 28.09
206 dpn68b 864.02 443.325 384 224 12.61
207 cspresnext50 863.58 443.862 384 224 20.57
208 cspresnet50w 858.14 446.674 384 256 28.12
209 selecsls84 852.15 599.694 512 224 50.95
210 hrnet_w18_small_v2 849.12 600.799 512 224 15.6
211 cspresnet50d 847.29 452.406 384 256 21.64
212 deit_small_distilled_patch16_224 846.66 301.609 256 224 22.44
213 seresnet50t 844.69 453.54 384 224 28.1
214 resnetrs50 843.24 454.312 384 224 35.69
215 densenet121 835.01 304.929 256 224 7.98
216 tv_densenet121 832.77 305.766 256 224 7.98
217 rexnet_150 829.93 307.376 256 224 9.73
218 gluon_resnext50_32x4d 824.86 464.778 384 224 25.03
219 tv_resnext50_32x4d 824.06 465.237 384 224 25.03
220 ssl_resnext50_32x4d 823.91 465.298 384 224 25.03
221 swsl_resnext50_32x4d 823.16 465.728 384 224 25.03
222 resnext50_32x4d 823.01 465.815 384 224 25.03
223 dpn68 816.18 469.476 384 224 12.61
224 res2net50_26w_4s 812.07 314.023 256 224 25.7
225 efficientnet_b2_pruned 811.6 314.06 256 260 8.31
226 resnetblur50d 810.86 314.915 256 224 25.58
227 pit_s_224 809.09 315.601 256 224 23.46
228 pit_s_distilled_224 808.91 315.694 256 224 24.04
229 vovnet57a 808.86 473.9 384 224 36.64
230 skresnet50 802.36 317.782 256 224 25.8
231 densenet121d 795.36 320.183 256 224 8.0
232 vit_base_resnet26d_224 792.88 483.157 384 224 101.4
233 rexnet_130 792.23 322.087 256 224 7.56
234 tf_efficientnet_b2_ns 791.33 241.259 192 260 9.11
235 tf_efficientnet_b2_ap 790.24 241.588 192 260 9.11
236 regnetx_006 788.72 648.389 512 224 6.2
237 tf_efficientnet_b2 788.26 242.2 192 260 9.11
238 vgg11_bn 788.01 487.085 384 224 132.87
239 nf_ecaresnet50 787.45 486.772 384 224 25.56
240 regnety_006 779.2 656.099 512 224 6.06
241 gcresnet50t 776.26 328.367 256 256 25.9
242 ese_vovnet57b 765.66 500.673 384 224 38.61
243 ecaresnet50d 761.68 335.218 256 224 25.58
244 resnext50d_32x4d 757.82 337.002 256 224 25.05
245 adv_inception_v3 756.25 506.326 384 299 23.83
246 tf_inception_v3 754.65 507.545 384 299 23.83
247 gluon_inception_v3 754.57 507.572 384 299 23.83
248 inception_v3 753.64 508.224 384 299 23.83
249 seresnetaa50d 751.96 339.385 256 224 28.11
250 res2net50_14w_8s 748.86 339.851 256 224 25.06
251 resmlp_24_distilled_224 743.59 342.912 256 224 30.02
252 resmlp_24_224 743.53 342.937 256 224 30.02
253 resnetv2_101 742.52 343.363 256 224 44.54
254 skresnet50d 738.05 345.557 256 224 25.82
255 sehalonet33ts 731.23 349.345 256 256 13.69
256 dla60_res2net 730.56 349.091 256 224 20.85
257 densenetblur121d 729.66 349.185 256 224 8.0
258 resnet101 719.46 354.412 256 224 44.55
259 tv_resnet101 719.29 354.488 256 224 44.55
260 gluon_resnet101_v1b 719.28 354.459 256 224 44.55
261 efficientnet_b2a 707.95 269.81 192 288 9.11
262 efficientnet_b2 707.16 270.111 192 288 9.11
263 nf_resnet50 703.13 545.338 384 288 25.56
264 nf_resnet101 702.76 544.906 384 224 44.55
265 ecaresnet101d_pruned 698.97 730.858 512 224 24.88
266 densenet169 691.74 367.774 256 224 14.15
267 gluon_resnet101_v1c 690.21 369.47 256 224 44.57
268 gluon_seresnext50_32x4d 689.84 370.073 256 224 27.56
269 legacy_seresnext50_32x4d 688.18 370.947 256 224 27.56
270 seresnext50_32x4d 688.18 370.943 256 224 27.56
271 resnetv2_101d 686.94 371.274 256 224 44.56
272 darknet53 683.71 373.676 256 256 41.61
273 dla60x 680.87 375.081 256 224 17.35
274 nf_regnet_b0 680.47 751.331 512 256 8.76
275 efficientnetv2_rw_t 669.58 284.789 192 288 13.65
276 gluon_resnet101_v1d 666.43 382.723 256 224 44.57
277 regnety_008 657.21 778.104 512 224 6.26
278 rexnet_200 656.53 291.353 192 224 16.37
279 tf_efficientnetv2_b3 655.17 291.405 192 300 14.36
280 vit_small_r26_s32_224 652.39 391.27 256 224 36.43
281 sebotnet33ts_256 650.1 196.14 128 256 13.7
282 wide_resnet50_2 646.12 593.536 384 224 68.88
283 vgg13 645.37 594.836 384 224 133.05
284 dla102 640.56 398.166 256 224 33.27
285 vit_base_resnet50d_224 619.99 411.439 256 224 110.97
286 gluon_resnet101_v1s 614.53 415.124 256 224 44.67
287 resnext26ts 613.38 625.548 384 256 10.3
288 resnetaa101d 612.93 416.224 256 224 44.57
289 ecaresnet26t 610.98 418.477 256 320 16.01
290 gmixer_24_224 610.59 417.896 256 224 24.72
291 repvgg_b1 609.77 838.686 512 224 57.42
292 eca_botnext26ts_256 609.21 419.672 256 256 10.59
293 eca_halonext26ts 598.72 427.016 256 256 10.76
294 gc_efficientnetv2_rw_t 597.14 319.011 192 288 13.68
295 eca_resnext26ts 593.02 431.168 256 256 10.3
296 seresnext26ts 592.35 431.553 256 256 10.39
297 regnetx_008 587.44 870.811 512 224 7.26
298 convnext_tiny 584.52 437.049 256 224 28.59
299 coat_lite_tiny 583.0 657.845 384 224 5.72
300 convnext_tiny_hnf 582.18 438.813 256 224 28.59
301 gcresnext26ts 581.42 439.532 256 256 10.48
302 mixnet_m 576.71 664.443 384 224 5.01
303 resnetv2_50x1_bit_distilled 575.65 332.777 192 224 25.55
304 cspdarknet53 570.42 447.825 256 256 27.64
305 legacy_seresnet101 570.04 447.058 256 224 49.33
306 halonet50ts 568.71 336.701 192 256 22.73
307 res2net50_26w_6s 565.22 451.306 256 224 37.05
308 resnetblur101d 562.81 453.413 256 224 44.57
309 seresnet101 560.51 454.74 256 224 49.33
310 coat_lite_mini 559.21 685.884 384 224 11.01
311 xcit_small_12_p16_224 558.21 456.962 256 224 26.25
312 xcit_small_12_p16_224_dist 557.95 457.219 256 224 26.25
313 vgg13_bn 555.55 460.55 256 224 133.05
314 tf_efficientnet_lite3 555.29 229.476 128 300 8.2
315 efficientnet_lite3 554.26 229.924 128 300 8.2
316 tf_mixnet_m 541.25 708.06 384 224 5.01
317 ssl_resnext101_32x4d 539.66 472.944 256 224 44.18
318 gluon_resnext101_32x4d 539.41 473.189 256 224 44.18
319 xcit_tiny_24_p16_224_dist 538.89 472.126 256 224 12.12
320 resnext101_32x4d 538.71 473.739 256 224 44.18
321 xcit_tiny_24_p16_224 538.48 472.477 256 224 12.12
322 swsl_resnext101_32x4d 538.39 474.037 256 224 44.18
323 densenet201 535.33 355.953 192 224 20.01
324 bat_resnext26ts 534.3 478.021 256 256 10.73
325 twins_svt_small 529.33 482.243 256 224 24.06
326 mixer_b16_224 521.89 489.791 256 224 59.88
327 mixer_b16_224_miil 521.33 490.33 256 224 59.88
328 vgg16 519.79 738.569 384 224 138.36
329 nf_seresnet101 519.55 490.612 256 224 49.33
330 gmlp_s16_224 514.57 371.708 192 224 19.42
331 crossvit_small_240 514.25 371.914 192 240 26.86
332 resnetv2_152 514.07 495.927 256 224 60.19
333 res2net101_26w_4s 512.94 372.022 192 224 45.21
334 resnetv2_50d_gn 512.21 374.104 192 224 25.57
335 botnet50ts_256 511.02 249.57 128 256 22.74
336 vit_base_r26_s32_224 509.73 375.506 192 224 101.38
337 efficientnet_b3_pruned 508.52 501.852 256 300 9.86
338 nf_regnet_b3 507.41 502.895 256 320 18.59
339 res2next50 500.31 510.5 256 224 24.67
340 xcit_nano_12_p16_384_dist 497.26 513.197 256 384 3.05
341 dla102x 495.72 385.887 192 224 26.31
342 resnet50_gn 494.95 387.141 192 224 25.56
343 mixnet_l 494.74 516.045 256 224 7.33
344 resnet152 490.44 389.409 192 224 60.19
345 gluon_resnet152_v1b 490.37 389.44 192 224 60.19
346 tv_resnet152 490.23 389.552 192 224 60.19
347 resmlp_36_224 489.84 389.965 192 224 44.69
348 resmlp_36_distilled_224 489.66 390.129 192 224 44.69
349 cait_xxs24_224 487.15 523.231 256 224 11.96
350 mixer_l32_224 486.19 393.553 192 224 206.94
351 halo2botnet50ts_256 485.95 262.504 128 256 22.64
352 xception 485.66 262.809 128 299 22.86
353 visformer_tiny 484.78 1055.527 512 224 10.32
354 vit_large_patch32_224 484.04 527.472 256 224 306.54
355 vit_base_patch32_384 478.93 533.779 256 384 88.3
356 gluon_resnet152_v1c 476.8 400.507 192 224 60.21
357 resnetv2_152d 475.38 401.77 192 224 60.2
358 crossvit_15_240 472.73 404.523 192 240 27.53
359 res2net50_26w_8s 468.77 407.558 192 224 48.4
360 tf_mixnet_l 466.85 546.936 256 224 7.33
361 gluon_resnet152_v1d 465.06 410.759 192 224 60.21
362 ecaresnet101d 462.22 552.191 256 224 44.57
363 crossvit_15_dagger_240 459.76 415.927 192 240 28.21
364 vgg16_bn 457.66 559.065 256 224 138.37
365 nf_ecaresnet101 455.3 560.589 256 224 44.55
366 efficientnet_el 446.2 285.844 128 300 10.59
367 efficientnet_el_pruned 445.59 286.28 128 300 10.59
368 swin_tiny_patch4_window7_224 445.53 573.708 256 224 28.29
369 fbnetv3_g 442.28 287.506 128 288 16.62
370 gluon_resnet152_v1s 441.14 433.129 192 224 60.32
371 gluon_seresnext101_32x4d 437.16 437.191 192 224 48.96
372 tf_efficientnet_el 436.28 292.36 128 300 10.59
373 vgg19 436.15 880.196 384 224 143.67
374 seresnext101_32x4d 435.91 438.42 192 224 48.96
375 legacy_seresnext101_32x4d 435.08 439.246 192 224 48.96
376 ese_vovnet99b 434.62 587.614 256 224 63.2
377 skresnext50_32x4d 434.44 587.996 256 224 27.48
378 twins_pcpvt_small 433.1 589.648 256 224 24.11
379 dla60_res2next 431.53 591.928 256 224 17.03
380 dla169 427.18 447.18 192 224 53.39
381 hrnet_w32 426.75 445.609 192 224 41.23
382 hrnet_w18 413.83 614.275 256 224 21.3
383 convit_small 409.81 467.592 192 224 27.78
384 vit_tiny_patch16_384 409.3 311.972 128 384 5.79
385 vit_small_resnet50d_s16_224 407.06 470.553 192 224 57.53
386 vgg19_bn 388.74 658.182 256 224 143.68
387 ecaresnet50t 388.23 328.837 128 320 25.57
388 inception_v4 387.4 493.551 192 299 42.68
389 tf_efficientnet_b3_ap 384.58 331.269 128 300 12.23
390 tf_efficientnet_b3_ns 384.22 331.517 128 300 12.23
391 tf_efficientnet_b3 384.13 331.655 128 300 12.23
392 legacy_seresnet152 382.75 498.635 192 224 66.82
393 xcit_nano_12_p8_224 378.83 674.154 256 224 3.05
394 xcit_nano_12_p8_224_dist 378.65 674.433 256 224 3.05
395 convnext_small 375.68 509.406 192 224 50.22
396 regnetx_016 374.21 1367.378 512 224 9.19
397 haloregnetz_b 372.27 686.285 256 224 11.68
398 densenet161 371.62 342.252 128 224 28.68
399 repvgg_b2 368.23 1389.47 512 224 89.02
400 seresnet152 362.95 349.646 128 224 66.82
401 nest_tiny 356.9 357.853 128 224 17.06
402 dla102x2 355.19 358.924 128 224 41.28
403 vit_base_patch16_224_miil 354.79 360.089 128 224 86.54
404 jx_nest_tiny 353.97 360.795 128 224 17.06
405 gluon_resnext101_64x4d 352.72 361.493 128 224 83.46
406 resnext101_64x4d 352.52 361.632 128 224 83.46
407 wide_resnet101_2 350.85 545.834 192 224 126.89
408 regnety_016 350.64 1458.484 512 224 11.2
409 vit_base_patch16_224 349.4 365.598 128 224 86.57
410 deit_base_patch16_224 349.3 365.691 128 224 86.57
411 vit_base_patch16_224_sam 349.14 365.832 128 224 86.57
412 resnet200 348.81 364.208 128 224 64.67
413 resnest14d 348.25 1469.78 512 224 10.61
414 efficientnet_b3a 345.42 368.96 128 320 12.23
415 efficientnet_b3 345.06 369.413 128 320 12.23
416 deit_base_distilled_patch16_224 344.74 370.536 128 224 87.34
417 crossvit_18_240 343.92 370.408 128 240 43.27
418 hrnet_w30 341.47 745.312 256 224 37.71
419 crossvit_18_dagger_240 335.19 380.045 128 240 44.27
420 ens_adv_inception_resnet_v2 334.4 571.029 192 299 55.84
421 inception_resnet_v2 334.03 571.741 192 299 55.84
422 efficientnet_lite4 332.63 191.154 64 380 13.01
423 nf_regnet_b1 327.56 1561.741 512 288 10.22
424 coat_lite_small 324.96 589.506 192 224 19.84
425 dpn92 324.94 786.493 256 224 37.67
426 resnet101d 323.43 394.33 128 320 44.57
427 mixnet_xl 322.47 593.686 192 224 11.9
428 beit_base_patch16_224 320.26 398.682 128 224 86.53
429 tf_efficientnet_lite4 319.09 199.328 64 380 13.01
430 gcresnext50ts 314.79 608.558 192 256 15.67
431 resnetrs101 314.59 404.798 128 288 63.62
432 twins_pcpvt_base 313.15 610.712 192 224 43.83
433 cait_xxs36_224 311.89 407.079 128 224 17.3
434 gluon_seresnext101_64x4d 306.13 416.102 128 224 88.23
435 twins_svt_base 305.99 416.477 128 224 56.07
436 repvgg_b3 305.95 1254.102 384 224 123.09
437 regnetz_d8 301.65 210.668 64 320 23.37
438 xception41 300.49 424.83 128 299 26.97
439 efficientnetv2_s 292.99 325.601 96 384 21.46
440 ese_vovnet39b_evos 292.82 436.279 128 224 24.58
441 efficientnetv2_rw_s 289.9 218.715 64 384 23.94
442 tf_efficientnetv2_s 289.31 329.828 96 384 21.46
443 swin_small_patch4_window7_224 289.15 441.029 128 224 49.61
444 tf_efficientnetv2_s_in21ft1k 288.88 330.272 96 384 21.46
445 xcit_small_24_p16_224_dist 286.23 444.237 128 224 47.67
446 xcit_small_24_p16_224 286.12 444.416 128 224 47.67
447 hrnet_w40 285.91 667.227 192 224 57.56
448 pit_b_224 285.13 335.829 96 224 73.76
449 pit_b_distilled_224 284.45 336.631 96 224 74.79
450 nf_regnet_b2 282.04 1813.867 512 272 14.31
451 gluon_xception65 276.41 345.52 96 299 39.92
452 dpn98 275.63 462.928 128 224 61.57
453 xception65 275.32 346.928 96 299 39.92
454 resnet51q 274.2 699.442 192 288 35.7
455 convnext_base_in22ft1k 273.43 466.529 128 224 88.59
456 convnext_base 272.92 467.434 128 224 88.59
457 nf_regnet_b4 271.03 470.306 128 384 30.21
458 xcit_tiny_12_p8_224 270.45 471.679 128 224 6.71
459 xcit_tiny_12_p8_224_dist 270.45 471.621 128 224 6.71
460 cait_s24_224 268.53 474.373 128 224 46.92
461 hrnet_w48 262.05 484.085 128 224 77.47
462 resnest26d 254.99 1505.218 384 224 17.07
463 resnest50d_1s4x24d 252.9 1011.055 256 224 25.68
464 resnetv2_50d_evob 247.25 775.496 192 224 25.59
465 regnetz_c16 246.47 517.947 128 320 13.46
466 gmlp_b16_224 243.4 392.966 96 224 73.08
467 hrnet_w44 236.76 806.557 192 224 67.06
468 twins_svt_large 235.13 542.578 128 224 99.27
469 efficientnet_b4 234.85 270.618 64 384 19.34
470 nest_small 234.57 407.797 96 224 38.35
471 resnetv2_50d_evos 233.18 410.671 96 224 25.59
472 jx_nest_small 233.1 410.378 96 224 38.35
473 tf_efficientnet_b4_ap 230.34 275.916 64 380 19.34
474 tresnet_m 230.07 2222.819 512 224 31.39
475 tf_efficientnet_b4 229.98 276.378 64 380 19.34
476 tf_efficientnet_b4_ns 229.78 276.586 64 380 19.34
477 visformer_small 229.49 1672.652 384 224 40.22
478 crossvit_base_240 228.79 418.11 96 240 105.03
479 nfnet_l0 228.47 1119.415 256 288 35.07
480 coat_tiny 228.08 559.427 128 224 5.5
481 twins_pcpvt_large 223.01 570.431 128 224 60.99
482 eca_nfnet_l0 221.69 1153.795 256 288 24.14
483 vit_large_r50_s32_224 220.98 432.297 96 224 328.99
484 swin_base_patch4_window7_224 220.89 577.812 128 224 87.77
485 densenet264 217.72 437.317 96 224 72.69
486 hrnet_w64 216.27 587.374 128 224 128.06
487 convit_base 213.62 448.479 96 224 86.54
488 xception71 212.24 299.632 64 299 42.34
489 vit_small_r26_s32_384 211.98 300.724 64 384 36.47
490 resnet152d 211.82 451.04 96 320 60.21
491 vit_small_patch16_384 209.76 304.352 64 384 22.2
492 xcit_medium_24_p16_224 207.55 459.61 96 224 84.4
493 xcit_medium_24_p16_224_dist 207.03 460.529 96 224 84.4
494 resnet61q 206.68 618.373 128 288 36.85
495 dpn131 205.78 620.133 128 224 79.25
496 mixnet_xxl 204.23 624.949 128 224 23.96
497 vit_base_r50_s16_224 195.65 325.59 64 224 98.66
498 coat_mini 193.15 660.874 128 224 10.34
499 regnety_040 193.14 1986.785 384 224 20.65
500 xcit_small_12_p16_384_dist 191.88 498.659 96 384 26.25
501 xcit_tiny_24_p16_384_dist 186.09 512.939 96 384 12.12
502 seresnet200d 185.21 514.398 96 256 71.86
503 regnetz_b16 179.85 1066.235 192 288 9.72
504 repvgg_b1g4 177.2 2888.374 512 224 39.97
505 ecaresnet200d 176.34 541.265 96 256 64.69
506 nest_base 176.15 361.868 64 224 67.72
507 convmixer_768_32 176.07 543.988 96 224 21.11
508 resnest50d 175.35 1458.742 256 224 27.48
509 regnety_032 175.25 1459.459 256 288 19.44
510 jx_nest_base 174.95 364.333 64 224 67.72
511 regnetx_032 174.19 2203.311 384 224 15.3
512 senet154 172.45 553.616 96 224 115.09
513 gluon_senet154 172.38 553.858 96 224 115.09
514 legacy_senet154 171.96 555.183 96 224 115.09
515 seresnet152d 169.43 374.655 64 320 66.84
516 resnetrs152 167.98 377.927 64 320 86.62
517 dpn107 167.97 570.011 96 224 86.92
518 halonet_h1 167.27 381.366 64 256 8.1
519 resnest50d_4s2x40d 163.46 1564.906 256 224 30.42
520 resnet200d 163.41 388.892 64 320 64.69
521 mixer_l16_224 161.74 592.23 96 224 208.2
522 convnext_large_in22ft1k 160.8 595.418 96 224 197.77
523 convnext_large 160.72 595.739 96 224 197.77
524 regnetx_040 160.66 2389.077 384 224 22.12
525 tnt_s_patch16_224 157.73 809.855 128 224 23.76
526 regnetx_080 148.64 1721.236 256 224 39.57
527 efficientnetv2_m 147.13 323.331 48 416 54.14
528 resnext101_32x8d 145.87 876.049 128 224 88.79
529 ssl_resnext101_32x8d 145.3 879.52 128 224 88.79
530 vit_large_patch32_384 145.15 439.536 64 384 306.63
531 xcit_small_12_p8_224_dist 145.04 439.625 64 224 26.21
532 xcit_small_12_p8_224 144.99 439.796 64 224 26.21
533 ig_resnext101_32x8d 144.47 884.578 128 224 88.79
534 swsl_resnext101_32x8d 144.29 885.69 128 224 88.79
535 regnetz_e8 140.91 338.814 48 320 57.7
536 xcit_tiny_24_p8_224 139.28 686.242 96 224 12.11
537 xcit_tiny_24_p8_224_dist 139.16 686.889 96 224 12.11
538 resnetv2_50x1_bitm 137.88 347.358 48 448 25.55
539 seresnext101_32x8d 136.74 934.059 128 224 93.57
540 seresnet269d 134.18 471.701 64 256 113.67
541 swin_large_patch4_window7_224 132.61 480.998 64 224 196.53
542 xcit_nano_12_p8_384_dist 128.43 496.705 64 384 3.05
543 tnt_b_patch16_224 126.43 757.626 96 224 65.41
544 xcit_large_24_p16_224 124.67 510.437 64 224 189.1
545 xcit_large_24_p16_224_dist 124.62 510.639 64 224 189.1
546 efficientnetv2_rw_m 123.91 255.105 32 416 53.24
547 regnetx_064 123.53 2071.55 256 224 26.21
548 regnety_064 121.34 2108.152 256 224 30.58
549 resnetrs200 119.86 396.447 48 320 93.21
550 nfnet_f0 119.25 2145.698 256 256 71.49
551 nfnet_f0s 118.17 2165.276 256 256 71.49
552 ese_vovnet99b_iabn 116.31 3298.583 384 224 63.2
553 regnety_080 108.46 2359.307 256 224 39.18
554 tf_efficientnetv2_m_in21ft1k 107.43 294.9 32 480 54.14
555 vit_large_patch16_224 106.81 447.947 48 224 304.33
556 tf_efficientnetv2_m 106.77 296.673 32 480 54.14
557 convnext_xlarge_in22ft1k 104.57 610.376 64 224 350.2
558 crossvit_15_dagger_408 103.52 307.452 32 408 28.5
559 tresnet_l 102.9 4972.484 512 224 55.99
560 repvgg_b2g4 101.39 5048.708 512 224 61.76
561 eca_nfnet_l1 99.64 1282.96 128 320 41.41
562 xcit_small_24_p16_384_dist 98.62 483.709 48 384 47.67
563 beit_large_patch16_224 98.03 487.815 48 224 304.43
564 cspdarknet53_iabn 97.55 3934.064 384 256 27.64
565 efficientnet_b5 94.83 335.072 32 456 30.39
566 convnext_base_384_in22ft1k 93.76 510.323 48 384 88.59
567 tf_efficientnet_b5_ap 93.42 340.255 32 456 30.39
568 tf_efficientnet_b5_ns 93.39 340.338 32 456 30.39
569 tf_efficientnet_b5 93.26 340.852 32 456 30.39
570 xcit_tiny_12_p8_384_dist 92.1 519.585 48 384 6.71
571 tresnet_xl 89.9 4267.972 384 224 78.44
572 vit_base_patch16_384 89.9 355.185 32 384 86.86
573 deit_base_patch16_384 89.87 355.331 32 384 86.86
574 cspresnext50_iabn 89.4 4293.09 384 256 20.57
575 resnest101e 88.34 1446.581 128 256 48.28
576 deit_base_distilled_patch16_384 88.07 362.54 32 384 87.63
577 cait_xxs24_384 87.17 364.76 32 384 12.03
578 nf_regnet_b5 85.26 748.171 64 456 49.74
579 resnetv2_101x1_bitm 84.86 375.607 32 448 44.54
580 resnetv2_152x2_bit_teacher 84.71 375.537 32 224 236.34
581 repvgg_b3g4 83.61 4591.876 384 224 83.83
582 crossvit_18_dagger_408 77.92 306.154 24 408 44.61
583 regnetx_120 77.0 3323.571 256 224 46.11
584 beit_base_patch16_384 76.44 313.03 24 384 86.74
585 ecaresnet269d 75.74 418.332 32 352 102.09
586 regnety_120 75.43 2544.024 192 224 51.82
587 pnasnet5large 75.01 423.127 32 331 86.06
588 vit_large_r50_s32_384 73.58 432.769 32 384 329.09
589 xcit_small_24_p8_224_dist 73.31 433.498 32 224 47.63
590 xcit_small_24_p8_224 73.25 433.859 32 224 47.63
591 resnetrs270 73.04 432.615 32 352 129.86
592 regnetz_d32 71.19 897.484 64 320 27.58
593 dm_nfnet_f0 71.06 3601.578 256 256 71.49
594 xcit_medium_24_p16_384_dist 70.95 448.036 32 384 84.4
595 regnetz_d8_evob 68.84 695.406 48 320 23.41
596 regnety_320 66.69 1917.907 128 224 145.05
597 nasnetalarge 65.99 480.339 32 331 88.75
598 resmlp_big_24_distilled_224 65.96 483.761 32 224 129.14
599 resmlp_big_24_224 65.94 483.953 32 224 129.14
600 resmlp_big_24_224_in22ft1k 65.9 484.194 32 224 129.14
601 regnetx_160 65.04 2951.208 192 224 54.28
602 regnetz_d8_evos 62.47 510.384 32 320 23.46
603 cait_xs24_384 60.56 393.982 24 384 26.67
604 swin_base_patch4_window12_384 60.23 396.845 24 384 87.9
605 efficientnetv2_l 60.16 261.949 16 480 118.52
606 convmixer_1024_20_ks9_p14 59.76 4282.918 256 224 24.38
607 tf_efficientnetv2_l 59.73 263.768 16 480 118.52
608 tf_efficientnetv2_l_in21ft1k 59.31 265.732 16 480 118.52
609 vit_base_patch8_224 57.86 275.754 16 224 86.58
610 cait_xxs36_384 56.92 418.29 24 384 17.37
611 convnext_large_384_in22ft1k 54.87 581.624 32 384 197.77
612 xcit_medium_24_p8_224 54.52 584.052 32 224 84.32
613 xcit_medium_24_p8_224_dist 54.51 583.988 32 224 84.32
614 vit_base_resnet50_384 54.07 294.48 16 384 98.95
615 vit_base_r50_s16_384 54.05 294.543 16 384 98.95
616 tresnet_m_448 52.97 3622.14 192 448 31.39
617 xcit_small_12_p8_384_dist 49.7 481.259 24 384 26.21
618 regnety_160 48.21 1990.283 96 288 83.59
619 xcit_tiny_24_p8_384_dist 47.11 676.387 32 384 12.11
620 ssl_resnext101_32x16d 46.89 2045.721 96 224 194.03
621 swsl_resnext101_32x16d 46.82 2049.006 96 224 194.03
622 ig_resnext101_32x16d 46.52 2062.16 96 224 194.03
623 resnetrs350 46.21 512.496 24 384 163.96
624 regnetx_320 45.87 2789.512 128 224 107.81
625 cait_s24_384 45.29 350.995 16 384 47.06
626 eca_nfnet_l2 44.43 1438.009 64 384 56.72
627 xcit_large_24_p16_384_dist 43.19 552.749 24 384 189.1
628 efficientnet_b6 42.65 372.523 16 528 43.04
629 tf_efficientnet_b6 41.27 384.949 16 528 43.04
630 tf_efficientnet_b6_ap 41.07 386.945 16 528 43.04
631 tf_efficientnet_b6_ns 41.05 387.133 16 528 43.04
632 nfnet_f1 39.56 2424.624 96 320 132.63
633 nfnet_f1s 39.31 2439.907 96 320 132.63
634 vit_huge_patch14_224 38.12 417.919 16 224 632.05
635 dm_nfnet_f1 37.77 1692.371 64 320 132.63
636 swin_large_patch4_window12_384 36.73 433.938 16 384 196.74
637 efficientnet_b7 35.91 219.519 8 600 66.35
638 efficientnetv2_xl 35.88 329.245 12 512 208.12
639 tf_efficientnetv2_xl_in21ft1k 35.79 329.897 12 512 208.12
640 tf_efficientnet_b7 35.55 221.854 8 600 66.35
641 tf_efficientnet_b7_ns 35.4 222.801 8 600 66.35
642 tf_efficientnet_b7_ap 35.26 223.637 8 600 66.35
643 convnext_xlarge_384_in22ft1k 34.11 467.466 16 384 350.2
644 resnetrs420 31.93 492.311 16 416 191.89
645 xcit_large_24_p8_224_dist 31.85 499.415 16 224 188.93
646 xcit_large_24_p8_224 31.84 499.581 16 224 188.93
647 resnest200e 31.57 1515.738 48 320 70.2
648 densenet264d_iabn 31.46 4064.91 128 224 72.74
649 resnetv2_50x3_bitm 30.97 515.904 16 448 217.32
650 cait_s36_384 29.83 399.013 12 384 68.37
651 resnetv2_152x2_bit_teacher_384 29.16 409.309 12 384 236.34
652 vit_large_patch16_384 26.62 299.125 8 384 304.72
653 tresnet_l_448 25.11 5094.194 128 448 55.99
654 xcit_small_24_p8_384_dist 25.02 476.66 12 384 47.63
655 eca_nfnet_l3 23.96 1332.494 32 448 72.04
656 efficientnet_cc_b0_4e 23.7 40.469 1 224 13.31
657 efficientnet_cc_b0_8e 22.94 41.864 1 224 24.01
658 tresnet_xl_448 22.71 4223.097 96 448 78.44
659 beit_large_patch16_384 22.62 351.795 8 384 305.0
660 tf_efficientnet_cc_b0_8e 22.37 43.033 1 224 24.01
661 nfnet_f2 21.97 2181.861 48 352 193.78
662 nfnet_f2s 21.86 2193.026 48 352 193.78
663 vit_giant_patch14_224 21.57 368.599 8 224 1012.61
664 dm_nfnet_f2 21.42 1490.949 32 352 193.78
665 tf_efficientnet_cc_b0_4e 21.27 45.221 1 224 13.31
666 resnetv2_152x2_bitm 21.19 375.422 8 448 236.34
667 efficientnet_cc_b1_8e 18.42 52.102 1 240 39.72
668 xcit_medium_24_p8_384_dist 17.99 441.849 8 384 84.32
669 ig_resnext101_32x32d 17.78 1798.209 32 224 468.53
670 tf_efficientnet_cc_b1_8e 17.44 55.209 1 240 39.72
671 resnetv2_101x3_bitm 17.08 466.831 8 448 387.93
672 resnest269e 12.91 1852.797 24 416 110.93
673 nfnet_f3 11.98 1999.215 24 416 254.92
674 nfnet_f3s 11.88 2016.77 24 416 254.92
675 dm_nfnet_f3 11.64 2058.707 24 416 254.92
676 cait_m36_384 11.45 520.837 6 384 271.22
677 efficientnet_b8 11.27 528.827 6 672 87.41
678 tf_efficientnet_b8_ap 11.23 530.563 6 672 87.41
679 tf_efficientnet_b8 11.1 537.074 6 672 87.41
680 xcit_large_24_p8_384_dist 10.94 545.597 6 384 188.93
681 convmixer_1536_20 9.34 5136.027 48 224 51.63
682 tf_efficientnet_l2_ns_475 8.79 336.325 3 475 480.31
683 ig_resnext101_32x48d 8.38 1908.292 16 224 828.41
684 beit_large_patch16_512 8.36 357.133 3 512 305.67
685 nfnet_f4s 6.37 1879.688 12 512 316.07
686 nfnet_f4 6.34 1889.493 12 512 316.07
687 dm_nfnet_f4 6.16 1941.969 12 512 316.07
688 cait_m48_448 4.64 426.785 2 448 356.46
689 nfnet_f5 4.56 1747.564 8 544 377.21
690 nfnet_f5s 4.56 1750.367 8 544 377.21
691 dm_nfnet_f5 4.43 1800.804 8 544 377.21
692 nfnet_f6 3.47 1722.95 6 576 438.36
693 nfnet_f6s 3.47 1723.469 6 576 438.36
694 dm_nfnet_f6 3.34 1791.721 6 576 438.36
695 resnetv2_152x4_bitm 3.17 313.58 1 480 936.53
696 nfnet_f7 2.64 1507.256 4 608 499.5
697 nfnet_f7s 2.63 1515.779 4 608 499.5
698 efficientnet_l2 2.07 477.813 1 800 480.31
699 tf_efficientnet_l2_ns 2.03 488.22 1 800 480.31

@ -1,528 +0,0 @@
model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tf_mobilenetv3_small_minimal_100,23813.47,10.741,256,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,20226.39,12.646,256,224,0.05,1.3,2.04
tf_mobilenetv3_small_100,18228.81,14.034,256,224,0.06,1.42,2.54
levit_128s,17210.0,14.865,256,224,0.31,1.88,7.78
regnetx_002,14906.56,17.164,256,224,0.2,2.16,2.68
regnety_002,13464.5,19.003,256,224,0.2,2.17,3.16
levit_128,11979.62,21.36,256,224,0.41,2.71,9.21
levit_192,10405.48,24.593,256,224,0.66,3.2,10.95
gernet_s,10172.35,25.156,256,224,0.75,2.65,8.17
vit_small_patch32_224,9285.97,27.555,256,224,1.15,2.5,22.88
regnetx_004,9188.53,27.851,256,224,0.4,3.14,5.16
tf_mobilenetv3_large_minimal_100,9164.35,27.924,256,224,0.22,4.4,3.92
tf_mobilenetv3_large_075,8667.81,29.525,256,224,0.16,4.0,3.99
mobilenetv3_rw,8631.08,29.65,256,224,0.23,4.41,5.48
vit_tiny_r_s16_p8_224,8546.38,29.942,256,224,0.44,2.06,6.34
mobilenetv3_large_100_miil,8526.13,30.015,256,224,0.23,4.41,5.48
mobilenetv3_large_100,8496.93,30.118,256,224,0.23,4.41,5.48
gluon_resnet18_v1b,8461.63,30.244,256,224,1.82,2.48,11.69
ssl_resnet18,8460.21,30.249,256,224,1.82,2.48,11.69
resnet18,8386.36,30.514,256,224,1.82,2.48,11.69
swsl_resnet18,8382.52,30.528,256,224,1.82,2.48,11.69
ghostnet_100,8276.88,30.92,256,224,0.15,3.55,5.18
levit_256,7773.58,32.922,256,224,1.13,4.23,18.89
legacy_seresnet18,7701.78,33.229,256,224,1.82,2.49,11.78
tf_mobilenetv3_large_100,7680.05,33.323,256,224,0.23,4.41,5.48
regnetx_006,7603.45,33.658,256,224,0.61,3.98,6.2
mobilenetv2_100,7541.65,33.934,256,224,0.31,6.68,3.5
regnety_004,7368.29,34.733,256,224,0.41,3.89,4.34
hardcorenas_a,7264.93,35.227,256,224,0.23,4.38,5.26
hardcorenas_b,7208.11,35.505,256,224,0.26,5.09,5.18
mnasnet_100,7142.85,35.829,256,224,0.33,5.46,4.38
resnet18d,7065.39,36.221,256,224,2.06,3.29,11.71
semnasnet_100,6753.19,37.897,256,224,0.32,6.23,3.89
hardcorenas_c,6746.84,37.933,256,224,0.28,5.01,5.52
spnasnet_100,6739.75,37.973,256,224,0.35,6.03,4.42
regnety_006,6693.6,38.235,256,224,0.61,4.33,6.06
hardcorenas_d,6572.55,38.939,256,224,0.3,4.93,7.5
tf_efficientnetv2_b0,6314.13,40.533,256,224,0.73,4.77,7.14
regnetx_008,6079.04,42.101,256,224,0.81,5.15,7.26
efficientnet_lite0,5804.98,44.09,256,224,0.4,6.74,4.65
dla46_c,5780.94,44.273,256,224,0.58,4.5,1.3
mobilenetv2_110d,5723.57,44.717,256,224,0.45,8.71,4.52
rexnet_100,5717.93,44.761,256,224,0.41,7.44,4.8
hardcorenas_f,5617.23,45.564,256,224,0.35,5.57,8.2
regnety_008,5508.59,46.462,256,224,0.81,5.25,6.26
hardcorenas_e,5410.0,47.31,256,224,0.35,5.65,8.07
fbnetc_100,5329.99,48.02,256,224,0.4,6.51,5.57
skresnet18,5316.12,48.145,256,224,1.82,3.24,11.96
tf_efficientnet_lite0,5240.32,48.842,256,224,0.4,6.74,4.65
mobilenetv2_140,5070.12,50.481,256,224,0.6,9.57,6.11
efficientnet_b0,5059.78,50.585,256,224,0.4,6.75,5.29
ese_vovnet19b_dw,5050.16,50.68,256,224,1.34,8.25,6.54
gluon_resnet34_v1b,4958.52,51.618,256,224,3.67,3.74,21.8
efficientnet_b1_pruned,4954.57,51.658,256,240,0.4,6.21,6.33
tv_resnet34,4937.29,51.84,256,224,3.67,3.74,21.8
resnet34,4928.32,51.933,256,224,3.67,3.74,21.8
hrnet_w18_small,4869.58,52.561,256,224,1.61,5.72,13.19
levit_384,4654.29,54.993,256,224,2.36,6.26,39.13
tf_efficientnet_b0_ap,4650.22,55.039,256,224,0.4,6.75,5.29
tf_efficientnet_b0_ns,4646.91,55.076,256,224,0.4,6.75,5.29
tf_efficientnet_b0,4644.25,55.108,256,224,0.4,6.75,5.29
dla46x_c,4605.94,55.57,256,224,0.54,5.66,1.07
selecsls42b,4570.17,56.005,256,224,2.98,4.62,32.46
deit_tiny_patch16_224,4543.82,56.329,256,224,1.26,5.97,5.72
vit_tiny_patch16_224,4538.07,56.399,256,224,1.26,5.97,5.72
gernet_m,4516.03,56.676,256,224,3.02,5.24,21.14
deit_tiny_distilled_patch16_224,4481.69,57.11,256,224,1.27,6.01,5.91
legacy_seresnet34,4474.42,57.204,256,224,3.67,3.74,21.96
resnet34d,4448.37,57.538,256,224,3.91,4.54,21.82
pit_ti_distilled_224,4332.05,59.084,256,224,0.71,6.23,5.1
pit_ti_224,4322.46,59.215,256,224,0.7,6.19,4.85
dla60x_c,4302.12,59.495,256,224,0.59,6.01,1.32
mixnet_s,4297.51,59.559,256,224,0.25,6.25,4.13
rexnet_130,4236.61,60.415,256,224,0.68,9.71,7.56
tf_efficientnetv2_b1,4231.93,60.481,256,240,1.21,7.34,8.14
xcit_nano_12_p16_224_dist,4191.2,61.067,256,224,0.56,4.17,3.05
xcit_nano_12_p16_224,4188.12,61.112,256,224,0.56,4.17,3.05
resmlp_12_distilled_224,4137.96,61.855,256,224,3.01,5.5,15.35
resmlp_12_224,4137.2,61.867,256,224,3.01,5.5,15.35
resnet26,4135.23,61.895,256,224,2.36,7.35,16.0
vit_base_patch32_sam_224,4111.04,62.261,256,224,4.41,5.01,88.22
mobilenetv2_120d,4108.55,62.299,256,224,0.69,11.97,5.83
vit_base_patch32_224,4102.94,62.384,256,224,4.41,5.01,88.22
tf_mixnet_s,4033.2,63.462,256,224,0.25,6.25,4.13
repvgg_b0,4020.07,63.669,256,224,3.41,6.15,15.82
selecsls60b,3957.1,64.683,256,224,3.63,5.52,32.77
selecsls60,3955.58,64.708,256,224,3.59,5.52,30.67
resnet26d,3771.71,67.862,256,224,2.6,8.15,16.01
dla34,3751.8,68.222,256,224,3.07,5.02,15.74
rexnet_150,3693.73,69.295,256,224,0.9,11.21,9.73
ecaresnet50d_pruned,3635.83,70.4,256,224,2.53,6.43,19.94
tf_efficientnet_lite1,3541.96,72.266,256,240,0.62,10.14,5.42
pit_xs_224,3506.77,72.991,256,224,1.4,7.71,10.62
regnetx_016,3495.21,73.233,256,224,1.62,7.93,9.19
pit_xs_distilled_224,3481.48,73.522,256,224,1.41,7.76,11.0
efficientnet_es_pruned,3365.67,76.052,256,224,1.81,8.73,5.44
efficientnet_es,3358.26,76.219,256,224,1.81,8.73,5.44
efficientnet_b2_pruned,3344.4,76.535,256,260,0.73,9.13,8.31
tf_efficientnet_es,3248.35,78.797,256,224,1.81,8.73,5.44
tf_efficientnetv2_b2,3221.93,79.444,256,260,1.72,9.84,10.1
resnest14d,3200.62,79.966,256,224,2.76,7.33,10.61
gernet_l,3173.09,80.669,256,256,4.57,8.0,31.08
regnety_016,3082.19,83.046,256,224,1.63,8.04,11.2
tf_efficientnet_cc_b0_8e,3079.83,83.109,256,224,0.42,9.42,24.01
tf_efficientnet_cc_b0_4e,3072.34,83.31,256,224,0.41,9.42,13.31
mixnet_m,3041.78,84.15,256,224,0.36,8.19,5.01
skresnet34,3025.8,84.595,256,224,3.67,5.13,22.28
resnext26ts,2999.56,85.335,256,256,2.43,10.52,10.3
repvgg_a2,2997.36,85.397,256,224,5.7,6.26,28.21
legacy_seresnext26_32x4d,2982.2,85.832,256,224,2.49,9.39,16.79
vit_tiny_r_s16_p8_384,2981.27,85.856,256,384,1.34,6.49,6.36
vit_small_patch32_384,2975.01,86.035,256,384,3.45,8.25,22.92
xcit_tiny_12_p16_224_dist,2962.26,86.406,256,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,2958.87,86.506,256,224,1.24,6.29,6.72
resnet26t,2949.15,86.793,256,256,3.35,10.52,16.01
seresnext26ts,2930.19,87.355,256,256,2.43,10.52,10.39
eca_resnext26ts,2927.39,87.439,256,256,2.43,10.52,10.3
tf_mixnet_m,2918.13,87.716,256,224,0.36,8.19,5.01
tf_efficientnet_b1_ap,2909.55,87.973,256,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,2907.96,88.021,256,240,0.71,10.88,7.79
tf_efficientnet_b1,2906.46,88.066,256,240,0.71,10.88,7.79
gcresnext26ts,2860.35,89.489,256,256,2.43,10.53,10.48
ecaresnet101d_pruned,2832.39,90.373,256,224,3.48,7.69,24.88
efficientnet_b1,2816.51,90.881,256,256,0.77,12.22,7.79
seresnext26t_32x4d,2803.22,91.313,256,224,2.7,10.09,16.81
seresnext26d_32x4d,2791.34,91.701,256,224,2.73,10.19,16.81
ecaresnetlight,2748.62,93.127,256,224,4.11,8.42,30.16
tf_efficientnet_lite2,2734.03,93.624,256,260,0.89,12.9,6.09
nf_regnet_b1,2722.26,94.028,256,288,1.02,9.2,10.22
crossvit_tiny_240,2719.71,94.117,256,240,1.57,9.08,7.01
rexnet_200,2697.36,94.896,256,224,1.56,14.91,16.37
resnetv2_50,2678.82,95.552,256,224,4.11,11.11,25.55
crossvit_9_240,2675.71,95.665,256,240,1.85,9.52,8.55
eca_botnext26ts_256,2660.24,96.221,256,256,2.46,11.6,10.59
tresnet_m,2657.69,96.314,256,224,5.74,7.31,31.39
botnet26t_256,2648.39,96.646,256,256,3.32,11.98,12.49
halonet26t,2611.82,98.005,256,256,3.19,11.69,12.48
vgg11,2609.78,98.082,256,224,7.61,7.44,132.86
eca_halonext26ts,2603.27,98.327,256,256,2.44,11.46,10.76
gluon_resnet50_v1b,2595.8,98.61,256,224,4.11,11.11,25.56
ssl_resnet50,2595.55,98.619,256,224,4.11,11.11,25.56
efficientnet_b3_pruned,2594.03,98.677,256,300,1.04,11.86,9.86
tv_resnet50,2591.78,98.763,256,224,4.11,11.11,25.56
crossvit_9_dagger_240,2589.88,98.836,256,240,1.99,9.97,8.78
resnet50,2584.36,99.039,256,224,4.11,11.11,25.56
swsl_resnet50,2581.65,99.146,256,224,4.11,11.11,25.56
convit_tiny,2544.18,100.61,256,224,1.26,7.94,5.71
hrnet_w18_small_v2,2509.11,102.017,256,224,2.62,9.65,15.6
resnet32ts,2503.04,102.263,256,256,4.63,11.58,17.96
bat_resnext26ts,2479.68,103.219,256,256,2.53,12.51,10.73
resnet33ts,2467.72,103.728,256,256,4.76,11.66,19.68
gluon_resnet50_v1c,2464.02,103.884,256,224,4.35,11.92,25.58
ese_vovnet39b,2457.44,104.162,256,224,7.09,6.74,24.57
cspresnet50,2453.31,104.338,256,256,4.54,11.5,21.62
cspresnext50,2451.69,104.407,256,224,3.1,12.14,20.57
gluon_resnet50_v1d,2445.69,104.663,256,224,4.35,11.92,25.58
resnet50d,2441.21,104.85,256,224,4.35,11.92,25.58
dpn68b,2432.24,105.242,256,224,2.35,10.47,12.61
legacy_seresnet50,2426.52,105.49,256,224,3.88,10.6,28.09
dpn68,2406.35,106.374,256,224,2.35,10.47,12.61
eca_resnet33ts,2405.01,106.434,256,256,4.76,11.66,19.68
seresnet33ts,2402.61,106.54,256,256,4.76,11.66,19.78
vgg11_bn,2383.08,107.413,256,224,7.62,7.44,132.87
mixnet_l,2358.59,108.528,256,224,0.58,10.84,7.33
lambda_resnet26t,2358.23,108.545,256,256,3.02,11.87,10.96
gcresnet33ts,2347.52,109.04,256,256,4.76,11.68,19.88
pit_s_224,2332.08,109.763,256,224,2.88,11.56,23.46
dla60,2324.78,110.106,256,224,4.26,10.16,22.04
seresnet50,2316.85,110.484,256,224,4.11,11.13,28.09
resnest26d,2313.67,110.634,256,224,3.64,9.97,17.07
pit_s_distilled_224,2311.97,110.718,256,224,2.9,11.64,24.04
deit_small_patch16_224,2297.25,111.426,256,224,4.61,11.95,22.05
vit_small_patch16_224,2293.15,111.622,256,224,4.61,11.95,22.05
deit_small_distilled_patch16_224,2268.28,112.85,256,224,4.63,12.02,22.44
tf_mixnet_l,2264.19,113.053,256,224,0.58,10.84,7.33
tf_efficientnet_b2_ns,2256.47,113.438,256,260,1.02,13.83,9.11
tf_efficientnet_b2_ap,2256.28,113.446,256,260,1.02,13.83,9.11
tf_efficientnet_b2,2253.15,113.605,256,260,1.02,13.83,9.11
tv_densenet121,2246.15,113.963,256,224,2.87,6.9,7.98
densenet121,2241.22,114.212,256,224,2.87,6.9,7.98
res2net50_48w_2s,2234.19,114.57,256,224,4.18,11.72,25.29
ecaresnet50d,2188.72,116.953,256,224,4.35,11.93,25.58
resnetblur50,2181.37,117.342,256,224,5.16,12.02,25.56
haloregnetz_b,2181.01,117.365,256,224,1.97,11.94,11.68
resnetrs50,2148.74,119.126,256,224,4.48,12.14,35.69
gluon_resnet50_v1s,2124.85,120.469,256,224,5.47,13.52,25.68
visformer_small,2123.42,120.549,256,224,4.88,11.43,40.22
gluon_inception_v3,2118.5,120.829,256,299,5.73,8.97,23.83
regnetx_032,2117.36,120.894,256,224,3.2,11.37,15.3
resmlp_24_distilled_224,2109.62,121.337,256,224,5.96,10.91,30.02
efficientnet_b2,2109.39,121.351,256,288,1.12,16.2,9.11
inception_v3,2109.39,121.351,256,299,5.73,8.97,23.83
resmlp_24_224,2108.77,121.386,256,224,5.96,10.91,30.02
tf_inception_v3,2108.49,121.403,256,299,5.73,8.97,23.83
adv_inception_v3,2107.2,121.474,256,299,5.73,8.97,23.83
efficientnet_em,2078.91,123.131,256,240,3.04,14.34,6.9
densenetblur121d,2047.01,125.05,256,224,3.11,7.9,8.0
tf_efficientnet_em,2016.96,126.911,256,240,3.04,14.34,6.9
ssl_resnext50_32x4d,2014.12,127.092,256,224,4.26,14.4,25.03
tv_resnext50_32x4d,2011.71,127.243,256,224,4.26,14.4,25.03
gluon_resnext50_32x4d,2008.79,127.428,256,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2003.89,127.736,256,224,4.26,14.4,25.03
resnext50_32x4d,2003.33,127.776,256,224,4.26,14.4,25.03
tf_efficientnet_cc_b1_8e,1964.78,130.28,256,240,0.75,15.44,39.72
dla60x,1943.55,131.706,256,224,3.54,13.8,17.35
regnetx_040,1936.18,132.208,256,224,3.99,12.2,22.12
resnext50d_32x4d,1913.78,133.755,256,224,4.5,15.2,25.05
res2net50_26w_4s,1909.57,134.046,256,224,4.28,12.61,25.7
tf_efficientnetv2_b3,1906.95,134.234,256,300,3.04,15.74,14.36
regnety_040,1904.74,134.39,256,224,4.0,12.29,20.65
gcresnet50t,1896.24,134.993,256,256,5.42,14.67,25.9
efficientnetv2_rw_t,1892.3,135.274,256,288,3.19,16.42,13.65
sehalonet33ts,1872.56,136.7,256,256,3.55,14.7,13.69
lambda_resnet26rpt_256,1852.61,138.171,256,256,3.16,11.87,10.99
seresnext50_32x4d,1837.91,139.277,256,224,4.26,14.42,27.56
gcresnext50ts,1836.72,139.369,256,256,3.75,15.46,15.67
dla60_res2net,1835.75,139.44,256,224,4.15,12.34,20.85
gluon_seresnext50_32x4d,1834.84,139.511,256,224,4.26,14.42,27.56
legacy_seresnext50_32x4d,1831.03,139.801,256,224,4.26,14.42,27.56
resnest50d_1s4x24d,1825.82,140.197,256,224,4.43,13.57,25.68
repvgg_b1g4,1816.81,140.887,256,224,8.15,10.64,39.97
densenet169,1814.19,141.099,256,224,3.4,7.3,14.15
gc_efficientnetv2_rw_t,1807.69,141.606,256,288,3.2,16.45,13.68
coat_lite_tiny,1802.74,141.995,256,224,1.6,11.65,5.72
res2net50_14w_8s,1801.06,142.125,256,224,4.21,13.28,25.06
res2next50,1791.13,142.906,256,224,4.2,13.71,24.67
cspdarknet53,1752.54,146.063,256,256,6.57,16.81,27.64
ecaresnet26t,1748.79,146.375,256,320,5.24,16.44,16.01
dla60_res2next,1747.0,146.526,256,224,3.49,13.17,17.03
regnetz_b,1724.41,148.443,256,288,2.39,16.43,9.72
coat_lite_mini,1717.23,149.066,256,224,2.0,12.25,11.01
vgg13,1708.54,149.824,256,224,11.31,12.25,133.05
mixnet_xl,1689.17,151.542,256,224,0.93,14.57,11.9
vit_small_r26_s32_224,1664.97,153.742,256,224,3.56,9.85,36.43
resnetv2_101,1630.91,156.956,256,224,7.83,16.23,44.54
tf_efficientnet_lite3,1624.63,157.563,256,300,1.65,21.85,8.2
gluon_resnet101_v1b,1601.1,159.88,256,224,7.83,16.23,44.55
tv_resnet101,1599.29,160.06,256,224,7.83,16.23,44.55
repvgg_b1,1595.1,160.478,256,224,13.16,10.64,57.42
xcit_tiny_24_p16_224_dist,1586.68,161.331,256,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,1585.43,161.458,256,224,2.34,11.82,12.12
xcit_small_12_p16_224_dist,1555.24,164.59,256,224,4.82,12.58,26.25
lambda_resnet50ts,1554.98,164.616,256,256,5.07,17.48,21.54
xcit_small_12_p16_224,1551.41,164.997,256,224,4.82,12.58,26.25
gluon_resnet101_v1c,1549.68,165.185,256,224,8.08,17.04,44.57
resnest50d,1546.83,165.485,256,224,5.4,14.36,27.48
vgg13_bn,1545.98,165.579,256,224,11.33,12.25,133.05
gluon_resnet101_v1d,1542.35,165.971,256,224,8.08,17.04,44.57
dla102,1540.91,166.124,256,224,7.19,14.18,33.27
twins_svt_small,1527.64,167.568,256,224,2.94,13.75,24.06
wide_resnet50_2,1520.69,168.321,256,224,11.43,14.4,68.88
gmixer_24_224,1499.34,170.73,256,224,5.28,14.45,24.72
resnetv2_50x1_bit_distilled,1491.85,171.586,256,224,4.23,11.11,25.55
regnetx_080,1490.15,171.784,256,224,8.02,14.06,39.57
xcit_nano_12_p16_384_dist,1485.24,172.347,256,384,1.64,12.15,3.05
crossvit_small_240,1471.27,173.987,256,240,5.63,18.17,26.86
legacy_seresnet101,1468.28,174.342,256,224,7.61,15.74,49.33
halonet50ts,1455.95,175.819,256,256,5.3,19.2,22.73
res2net50_26w_6s,1449.56,176.589,256,224,6.33,15.28,37.05
regnetx_064,1419.83,180.292,256,224,6.49,16.37,26.21
densenet201,1416.48,180.718,256,224,4.34,7.85,20.01
resmlp_36_224,1415.51,180.84,256,224,8.91,16.33,44.69
resmlp_36_distilled_224,1415.35,180.86,256,224,8.91,16.33,44.69
gluon_resnet101_v1s,1409.69,181.589,256,224,9.19,18.64,44.67
lamhalobotnet50ts_256,1390.79,184.054,256,256,5.02,18.44,22.57
nf_resnet50,1387.87,184.443,256,288,6.88,18.37,25.56
ecaresnet101d,1376.05,186.03,256,224,8.08,17.07,44.57
vgg16,1375.43,186.112,256,224,15.47,13.56,138.36
hrnet_w18,1354.53,188.983,256,224,4.32,16.31,21.3
regnety_032,1350.9,189.492,256,288,5.29,18.61,19.44
crossvit_15_240,1346.42,190.123,256,240,5.81,19.77,27.53
tresnet_l,1344.95,190.33,256,224,10.88,11.9,55.99
gmlp_s16_224,1341.95,190.757,256,224,4.42,15.1,19.42
tf_efficientnet_b3_ap,1337.77,191.35,256,300,1.87,23.83,12.23
tf_efficientnet_b3_ns,1337.21,191.429,256,300,1.87,23.83,12.23
tf_efficientnet_b3,1336.96,191.464,256,300,1.87,23.83,12.23
resnet51q,1335.36,191.695,256,288,8.07,20.94,35.7
vit_base_patch32_384,1326.76,192.939,256,384,13.06,16.5,88.3
xception,1314.05,194.804,256,299,8.4,35.83,22.86
crossvit_15_dagger_240,1305.29,196.113,256,240,6.13,20.43,28.21
dla102x,1302.51,196.531,256,224,5.89,19.42,26.31
efficientnet_b3,1297.94,197.224,256,320,2.01,26.52,12.23
mixer_b16_224,1285.59,199.119,256,224,12.62,14.53,59.88
mixer_b16_224_miil,1283.81,199.396,256,224,12.62,14.53,59.88
cait_xxs24_224,1283.41,199.457,256,224,2.53,20.29,11.96
skresnext50_32x4d,1277.72,200.346,256,224,4.5,17.18,27.48
regnety_064,1270.37,201.504,256,224,6.39,16.41,30.58
ssl_resnext101_32x4d,1267.29,201.994,256,224,8.01,21.23,44.18
gluon_resnext101_32x4d,1262.61,202.744,256,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1261.41,202.934,256,224,8.01,21.23,44.18
vgg16_bn,1259.82,203.192,256,224,15.5,13.56,138.37
repvgg_b2g4,1250.78,204.659,256,224,12.63,12.9,61.76
halo2botnet50ts_256,1247.07,205.268,256,256,5.02,21.78,22.64
swin_tiny_patch4_window7_224,1247.03,205.273,256,224,4.51,17.06,28.29
twins_pcpvt_small,1237.18,206.911,256,224,3.83,18.08,24.11
regnety_080,1231.05,207.941,256,224,8.0,17.97,39.18
resnest50d_4s2x40d,1214.34,210.797,256,224,4.4,17.94,30.42
resnet61q,1207.53,211.987,256,288,9.87,21.52,36.85
eca_nfnet_l0,1191.71,214.805,256,288,7.12,17.29,24.14
nfnet_l0,1190.26,215.067,256,288,7.13,17.29,35.07
res2net101_26w_4s,1183.89,216.22,256,224,8.1,18.45,45.21
dpn92,1178.74,217.169,256,224,6.54,18.21,37.67
res2net50_26w_8s,1178.7,217.174,256,224,8.37,17.95,48.4
vit_tiny_patch16_384,1172.78,218.271,256,384,4.7,25.39,5.79
convit_small,1169.52,218.881,256,224,5.76,17.87,27.78
vgg19,1152.13,222.185,256,224,19.63,14.86,143.67
gluon_seresnext101_32x4d,1150.11,222.576,256,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1149.76,222.644,256,224,8.02,21.26,48.96
hrnet_w32,1142.24,224.109,256,224,8.97,22.02,41.23
xcit_nano_12_p8_224,1127.98,226.94,256,224,2.16,15.71,3.05
xcit_nano_12_p8_224_dist,1127.81,226.974,256,224,2.16,15.71,3.05
hrnet_w30,1123.84,227.778,256,224,8.15,21.21,37.71
tv_resnet152,1116.24,229.331,256,224,11.56,22.56,60.19
gluon_resnet152_v1b,1115.71,229.44,256,224,11.56,22.56,60.19
ecaresnet50t,1110.54,230.506,256,320,8.82,24.13,25.57
regnetz_c,1099.38,232.841,256,320,3.92,25.88,13.46
gluon_resnet152_v1c,1090.66,234.708,256,224,11.8,23.36,60.21
gluon_resnet152_v1d,1087.27,235.441,256,224,11.8,23.36,60.21
repvgg_b2,1087.24,235.444,256,224,20.45,12.9,89.02
xception41,1068.11,239.655,256,299,9.28,39.86,26.97
densenet161,1067.45,239.812,256,224,7.79,11.06,28.68
vgg19_bn,1061.8,241.089,256,224,19.66,14.86,143.68
inception_v4,1057.29,242.117,256,299,12.28,15.09,42.68
convmixer_1024_20_ks9_p14,1048.55,244.135,256,224,5.55,5.51,24.38
dla169,1043.9,245.223,256,224,11.6,20.2,53.39
xcit_tiny_12_p16_384_dist,1041.14,245.869,256,384,3.64,18.26,6.72
regnetx_120,1028.54,248.883,256,224,12.13,21.37,46.11
gluon_resnet152_v1s,1019.74,251.033,256,224,12.92,24.96,60.32
coat_lite_small,1019.01,251.211,256,224,3.96,22.09,19.84
vit_base_patch16_224_miil,1015.29,252.134,256,224,17.58,23.9,86.54
legacy_seresnet152,1014.16,252.414,256,224,11.33,22.08,66.82
repvgg_b3g4,1009.72,253.523,256,224,17.89,15.1,83.83
jx_nest_tiny,1009.0,253.705,256,224,5.83,25.48,17.06
crossvit_18_240,995.7,257.092,256,240,9.05,26.26,43.27
vit_base_patch16_224,987.74,259.165,256,224,17.58,23.9,86.57
vit_base_patch16_sam_224,983.41,260.307,256,224,17.58,23.9,86.57
tresnet_xl,983.24,260.352,256,224,15.17,15.34,78.44
deit_base_patch16_224,981.95,260.694,256,224,17.58,23.9,86.57
regnety_120,981.03,260.937,256,224,12.14,21.38,51.82
deit_base_distilled_patch16_224,972.1,263.335,256,224,17.68,24.05,87.34
crossvit_18_dagger_240,968.8,264.234,256,240,9.5,27.03,44.27
efficientnet_el_pruned,927.76,275.921,256,300,8.0,30.7,10.59
efficientnet_el,927.59,275.971,256,300,8.0,30.7,10.59
tf_efficientnet_el,908.45,281.783,256,300,8.0,30.7,10.59
beit_base_patch16_224,907.03,282.225,256,224,17.58,23.9,86.53
dm_nfnet_f0,906.74,282.318,256,256,12.62,18.05,71.49
beit_base_patch16_224_in22k,904.43,283.033,256,224,17.6,23.92,102.56
twins_pcpvt_base,903.59,283.303,256,224,6.68,25.25,43.83
dla102x2,892.39,286.858,256,224,9.34,29.91,41.28
twins_svt_base,884.1,289.548,256,224,8.59,26.33,56.07
wide_resnet101_2,879.83,290.95,256,224,22.8,21.23,126.89
tf_efficientnetv2_s_in21ft1k,867.65,295.037,256,384,8.44,35.77,21.46
tf_efficientnetv2_s,866.61,295.392,256,384,8.44,35.77,21.46
cait_xxs36_224,864.05,296.268,256,224,3.77,30.34,17.3
resnetrs101,850.72,300.905,256,288,13.56,28.53,63.62
repvgg_b3,845.91,302.619,256,224,29.16,15.1,123.09
efficientnetv2_rw_s,844.69,303.059,256,384,8.72,38.03,23.94
dpn98,840.2,304.675,256,224,11.73,25.2,61.57
pit_b_distilled_224,838.63,305.249,256,224,12.5,33.07,74.79
pit_b_224,837.02,305.836,256,224,12.42,32.94,73.76
ens_adv_inception_resnet_v2,834.35,306.814,256,299,13.18,25.06,55.84
regnetx_160,833.02,307.301,256,224,15.99,25.52,54.28
inception_resnet_v2,832.69,307.427,256,299,13.18,25.06,55.84
xcit_small_24_p16_224_dist,830.32,308.297,256,224,9.1,23.64,47.67
xcit_small_24_p16_224,828.74,308.886,256,224,9.1,23.64,47.67
swin_small_patch4_window7_224,809.9,316.074,256,224,8.77,27.47,49.61
gluon_resnext101_64x4d,803.65,318.536,256,224,15.52,31.21,83.46
xcit_tiny_12_p8_224_dist,791.87,323.27,256,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,791.64,323.364,256,224,4.81,23.6,6.71
gluon_xception65,784.11,326.474,256,299,13.96,52.48,39.92
xception65,777.91,329.074,256,299,13.96,52.48,39.92
resnet101d,776.37,329.723,256,320,16.48,34.77,44.57
ig_resnext101_32x8d,775.21,330.222,256,224,16.48,31.21,88.79
swsl_resnext101_32x8d,773.83,330.808,256,224,16.48,31.21,88.79
resnext101_32x8d,773.08,331.13,256,224,16.48,31.21,88.79
ssl_resnext101_32x8d,769.28,332.765,256,224,16.48,31.21,88.79
tf_efficientnet_lite4,764.13,335.01,256,380,4.04,45.66,13.01
hrnet_w40,760.18,336.752,256,224,12.75,25.29,57.56
gluon_seresnext101_64x4d,756.43,338.418,256,224,15.53,31.25,88.23
resnest101e,755.07,339.024,256,256,13.38,28.66,48.28
hrnet_w48,716.4,357.329,256,224,17.34,28.56,77.47
cait_s24_224,714.86,358.098,256,224,9.35,40.58,46.92
tresnet_m_448,713.55,358.758,256,448,22.94,29.21,31.39
coat_tiny,707.04,362.062,256,224,4.35,27.2,5.5
regnetz_d,704.94,363.138,256,320,9.33,37.08,27.58
hrnet_w44,702.53,364.385,256,224,14.94,26.92,67.06
vit_large_r50_s32_224,680.11,376.394,256,224,19.58,24.41,328.99
jx_nest_small,676.97,378.142,256,224,10.35,40.04,38.35
twins_svt_large,673.22,380.252,256,224,15.15,35.1,99.27
crossvit_base_240,671.16,381.416,256,240,21.22,36.33,105.03
efficientnet_b4,667.18,383.692,256,384,4.51,50.04,19.34
twins_pcpvt_large,657.69,389.226,256,224,9.84,35.82,60.99
tf_efficientnet_b4,637.24,401.719,256,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,636.68,402.068,256,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,636.62,402.109,256,380,4.49,49.49,19.34
convit_base,621.76,411.725,256,224,17.52,31.77,86.54
dpn131,619.15,413.454,256,224,16.09,32.97,79.25
swin_base_patch4_window7_224,617.4,414.625,256,224,15.47,36.63,87.77
swin_base_patch4_window7_224_in22k,616.51,415.228,256,224,15.49,36.65,109.13
xcit_medium_24_p16_224_dist,613.3,417.401,256,224,16.13,31.71,84.4
xcit_medium_24_p16_224,612.89,417.675,256,224,16.13,31.71,84.4
vit_small_patch16_384,594.17,430.838,256,384,15.52,50.78,22.2
coat_mini,592.46,432.084,256,224,6.82,33.68,10.34
xception71,590.14,433.78,256,299,18.09,69.92,42.34
vit_small_r26_s32_384,574.75,445.396,256,384,10.43,29.85,36.47
hrnet_w64,570.22,448.937,256,224,28.97,35.09,128.06
dpn107,566.19,452.128,256,224,18.38,33.46,86.92
eca_nfnet_l1,559.71,457.367,256,320,14.92,34.42,41.41
gluon_senet154,559.51,457.529,256,224,20.77,38.69,115.09
legacy_senet154,558.72,458.178,256,224,20.77,38.69,115.09
xcit_tiny_24_p16_384_dist,556.09,460.342,256,384,6.87,34.29,12.12
xcit_small_12_p16_384_dist,546.87,468.099,256,384,14.14,36.51,26.25
resnet152d,546.29,468.597,256,320,24.08,47.67,60.21
regnety_320,522.26,490.163,256,224,32.34,30.26,145.05
jx_nest_base,511.39,500.584,256,224,17.96,53.39,67.72
regnety_160,509.43,502.501,256,288,26.37,38.07,83.59
tnt_s_patch16_224,497.77,514.282,256,224,5.24,24.37,23.76
seresnet152d,489.63,522.829,256,320,24.09,47.72,66.84
resnetrs152,483.98,528.928,256,320,24.34,48.14,86.62
regnetx_320,460.34,556.1,256,224,31.81,36.3,107.81
vit_large_patch32_384,457.67,559.348,256,384,45.31,43.86,306.63
mixer_l16_224,429.82,595.582,256,224,44.6,41.69,208.2
xcit_tiny_24_p8_224,417.75,612.797,256,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,417.56,613.068,256,224,9.21,45.39,12.11
xcit_small_12_p8_224,416.17,615.111,256,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,415.93,615.473,256,224,18.69,47.21,26.21
efficientnetv2_rw_m,404.25,633.263,256,416,21.49,79.62,53.24
resnet200d,391.43,653.991,256,320,31.25,67.33,64.69
resnetv2_50x1_bitm,388.67,658.636,256,448,16.62,44.46,25.55
xcit_nano_12_p8_384_dist,387.79,660.138,256,384,6.34,46.08,3.05
swin_large_patch4_window7_224,385.11,664.722,256,224,34.53,54.94,196.53
swin_large_patch4_window7_224_in22k,384.77,665.318,256,224,34.56,54.96,228.57
xcit_large_24_p16_224,374.96,682.722,256,224,35.86,47.27,189.1
xcit_large_24_p16_224_dist,374.65,683.281,256,224,35.86,47.27,189.1
ssl_resnext101_32x16d,366.28,698.897,256,224,36.27,51.18,194.03
ig_resnext101_32x16d,365.74,699.945,256,224,36.27,51.18,194.03
swsl_resnext101_32x16d,365.46,700.478,256,224,36.27,51.18,194.03
tresnet_l_448,350.13,731.146,256,448,43.5,47.56,55.99
resnetrs200,346.06,739.735,256,320,31.51,67.81,93.21
tf_efficientnetv2_m,339.58,753.868,256,480,24.76,89.84,54.14
tf_efficientnetv2_m_in21ft1k,339.57,753.879,256,480,24.76,89.84,54.14
vit_large_patch16_224,332.37,770.202,256,224,61.6,63.52,304.33
dm_nfnet_f1,329.67,776.526,256,320,35.97,46.77,132.63
tf_efficientnet_b5,319.74,800.623,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,319.51,801.207,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ns,319.48,801.287,256,456,10.46,98.86,30.39
crossvit_15_dagger_408,308.22,830.557,256,408,21.45,95.05,28.5
beit_large_patch16_224,304.41,840.964,256,224,61.6,63.52,304.43
beit_large_patch16_224_in22k,303.89,842.402,256,224,61.62,63.54,325.79
xcit_small_24_p16_384_dist,291.33,878.717,256,384,26.72,68.58,47.67
convmixer_768_32,284.41,900.105,256,224,19.55,25.95,21.11
eca_nfnet_l2,277.7,921.837,256,384,30.05,68.28,56.72
resnetv2_152x2_bit_teacher,272.08,940.888,256,224,46.95,45.11,236.34
xcit_tiny_12_p8_384_dist,271.29,943.618,256,384,14.13,69.14,6.71
tresnet_xl_448,265.72,963.421,256,448,60.65,61.31,78.44
vit_base_patch16_384,260.82,981.51,256,384,55.54,101.56,86.86
deit_base_patch16_384,260.27,983.563,256,384,55.54,101.56,86.86
resnest200e,257.06,995.856,256,320,35.69,82.78,70.2
deit_base_distilled_patch16_384,256.78,996.936,256,384,55.65,101.82,87.63
resnetv2_101x1_bitm,243.19,1052.661,256,448,31.65,64.93,44.54
cait_xxs24_384,238.44,1073.626,256,384,9.63,122.66,12.03
ecaresnet269d,229.27,1116.568,256,352,50.25,101.25,102.09
crossvit_18_dagger_408,229.17,558.535,128,408,32.47,124.87,44.61
vit_large_r50_s32_384,229.1,1117.419,256,384,57.43,76.52,329.09
nasnetalarge,226.47,1130.365,256,331,23.89,90.56,88.75
pnasnet5large,225.65,1134.478,256,331,25.04,92.89,86.06
beit_base_patch16_384,223.91,1143.322,256,384,55.54,101.56,86.74
resnetrs270,222.05,1152.857,256,352,51.13,105.48,129.86
xcit_small_24_p8_224,218.32,1172.571,256,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,218.2,1173.193,256,224,35.81,90.78,47.63
xcit_medium_24_p16_384_dist,212.67,1203.725,256,384,47.39,91.64,84.4
resmlp_big_24_224,199.99,1280.033,256,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,199.55,1282.862,256,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,199.5,1283.208,256,224,100.23,87.31,129.14
tf_efficientnetv2_l,196.16,1305.054,256,480,56.4,157.99,118.52
tf_efficientnetv2_l_in21ft1k,195.97,1306.333,256,480,56.4,157.99,118.52
dm_nfnet_f2,187.32,1366.603,256,352,63.22,79.06,193.78
tf_efficientnet_b6_ns,184.64,693.239,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ap,184.63,693.254,128,528,19.4,167.39,43.04
tf_efficientnet_b6,184.56,693.543,128,528,19.4,167.39,43.04
swin_base_patch4_window12_384,172.29,742.9,128,384,47.19,134.78,87.9
swin_base_patch4_window12_384_in22k,172.09,743.782,128,384,47.21,134.8,109.27
cait_xs24_384,168.76,1516.911,256,384,19.28,183.98,26.67
vit_base_r50_s16_384,164.61,1555.149,256,384,67.43,135.03,98.95
convmixer_1536_20,163.58,1564.936,256,224,48.68,33.03,51.63
cait_xxs36_384,159.38,1606.206,256,384,14.35,183.7,17.37
xcit_medium_24_p8_224,158.47,1615.434,256,224,63.53,121.23,84.32
xcit_medium_24_p8_224_dist,158.47,1615.404,256,224,63.53,121.23,84.32
resnetrs350,147.45,1736.143,256,384,77.59,154.74,163.96
ig_resnext101_32x32d,144.94,1766.221,256,224,87.29,91.12,468.53
xcit_tiny_24_p8_384_dist,140.02,1828.249,256,384,27.05,132.95,12.11
xcit_small_12_p8_384_dist,138.32,1850.769,256,384,54.92,138.29,26.21
cait_s24_384,130.37,1963.692,256,384,32.17,245.31,47.06
xcit_large_24_p16_384_dist,128.67,1989.551,256,384,105.35,137.17,189.1
tf_efficientnetv2_xl_in21ft1k,125.28,2043.358,256,512,93.85,247.32,208.12
resnest269e,119.95,2134.143,256,416,77.69,171.98,110.93
swin_large_patch4_window12_384,108.71,1177.454,128,384,104.08,202.16,196.74
swin_large_patch4_window12_384_in22k,108.63,1178.305,128,384,104.11,202.18,228.77
resnetrs420,107.0,2392.495,256,416,108.45,213.79,191.89
tf_efficientnet_b7_ns,106.07,603.375,64,600,38.33,289.94,66.35
tf_efficientnet_b7_ap,105.98,603.889,64,600,38.33,289.94,66.35
tf_efficientnet_b7,105.95,604.03,64,600,38.33,289.94,66.35
dm_nfnet_f3,102.63,2494.386,256,416,115.58,141.78,254.92
xcit_large_24_p8_224,95.99,2666.968,256,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,95.77,2673.153,256,224,141.23,181.56,188.93
resnetv2_152x2_bit_teacher_384,94.95,2696.08,256,384,136.16,132.56,236.34
resnetv2_50x3_bitm,93.71,1365.901,128,448,145.7,133.37,217.32
vit_large_patch16_384,89.71,2853.476,256,384,191.21,270.24,304.72
cait_s36_384,87.22,2935.166,256,384,47.99,367.4,68.37
ig_resnext101_32x48d,85.91,1489.933,128,224,153.57,131.06,828.41
beit_large_patch16_384,77.75,3292.734,256,384,191.21,270.24,305.0
xcit_small_24_p8_384_dist,72.45,3533.673,256,384,105.24,265.91,47.63
resnetv2_152x2_bitm,69.73,1835.714,128,448,184.99,180.43,236.34
tf_efficientnet_b8,66.92,956.333,64,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,66.91,956.434,64,672,63.48,442.89,87.41
dm_nfnet_f4,57.01,4490.519,256,512,216.26,262.26,316.07
resnetv2_101x3_bitm,56.17,2278.772,128,448,280.33,194.78,387.93
xcit_medium_24_p8_384_dist,53.81,4757.31,256,384,186.67,354.73,84.32
dm_nfnet_f5,41.46,6174.452,256,544,290.97,349.71,377.21
tf_efficientnet_l2_ns_475,38.52,1661.585,64,475,172.11,609.89,480.31
xcit_large_24_p8_384_dist,32.44,7891.525,256,384,415.0,531.82,188.93
beit_large_patch16_512,31.38,2039.285,64,512,362.24,656.39,305.67
cait_m36_384,30.16,8488.214,256,384,173.11,734.81,271.22
dm_nfnet_f6,30.03,8525.766,256,576,378.69,452.2,438.36
resnetv2_152x4_bitm,18.21,3515.177,64,480,844.84,414.26,936.53
tf_efficientnet_l2_ns,13.56,1770.059,24,800,479.12,1707.39,480.31
cait_m48_448,13.08,9784.533,128,448,329.41,1708.23,356.46
1 model infer_samples_per_sec infer_step_time infer_batch_size infer_img_size infer_gmacs infer_macts param_count
2 tf_mobilenetv3_small_minimal_100 23813.47 10.741 256 224 0.06 1.41 2.04
3 tf_mobilenetv3_small_075 20226.39 12.646 256 224 0.05 1.3 2.04
4 tf_mobilenetv3_small_100 18228.81 14.034 256 224 0.06 1.42 2.54
5 levit_128s 17210.0 14.865 256 224 0.31 1.88 7.78
6 regnetx_002 14906.56 17.164 256 224 0.2 2.16 2.68
7 regnety_002 13464.5 19.003 256 224 0.2 2.17 3.16
8 levit_128 11979.62 21.36 256 224 0.41 2.71 9.21
9 levit_192 10405.48 24.593 256 224 0.66 3.2 10.95
10 gernet_s 10172.35 25.156 256 224 0.75 2.65 8.17
11 vit_small_patch32_224 9285.97 27.555 256 224 1.15 2.5 22.88
12 regnetx_004 9188.53 27.851 256 224 0.4 3.14 5.16
13 tf_mobilenetv3_large_minimal_100 9164.35 27.924 256 224 0.22 4.4 3.92
14 tf_mobilenetv3_large_075 8667.81 29.525 256 224 0.16 4.0 3.99
15 mobilenetv3_rw 8631.08 29.65 256 224 0.23 4.41 5.48
16 vit_tiny_r_s16_p8_224 8546.38 29.942 256 224 0.44 2.06 6.34
17 mobilenetv3_large_100_miil 8526.13 30.015 256 224 0.23 4.41 5.48
18 mobilenetv3_large_100 8496.93 30.118 256 224 0.23 4.41 5.48
19 gluon_resnet18_v1b 8461.63 30.244 256 224 1.82 2.48 11.69
20 ssl_resnet18 8460.21 30.249 256 224 1.82 2.48 11.69
21 resnet18 8386.36 30.514 256 224 1.82 2.48 11.69
22 swsl_resnet18 8382.52 30.528 256 224 1.82 2.48 11.69
23 ghostnet_100 8276.88 30.92 256 224 0.15 3.55 5.18
24 levit_256 7773.58 32.922 256 224 1.13 4.23 18.89
25 legacy_seresnet18 7701.78 33.229 256 224 1.82 2.49 11.78
26 tf_mobilenetv3_large_100 7680.05 33.323 256 224 0.23 4.41 5.48
27 regnetx_006 7603.45 33.658 256 224 0.61 3.98 6.2
28 mobilenetv2_100 7541.65 33.934 256 224 0.31 6.68 3.5
29 regnety_004 7368.29 34.733 256 224 0.41 3.89 4.34
30 hardcorenas_a 7264.93 35.227 256 224 0.23 4.38 5.26
31 hardcorenas_b 7208.11 35.505 256 224 0.26 5.09 5.18
32 mnasnet_100 7142.85 35.829 256 224 0.33 5.46 4.38
33 resnet18d 7065.39 36.221 256 224 2.06 3.29 11.71
34 semnasnet_100 6753.19 37.897 256 224 0.32 6.23 3.89
35 hardcorenas_c 6746.84 37.933 256 224 0.28 5.01 5.52
36 spnasnet_100 6739.75 37.973 256 224 0.35 6.03 4.42
37 regnety_006 6693.6 38.235 256 224 0.61 4.33 6.06
38 hardcorenas_d 6572.55 38.939 256 224 0.3 4.93 7.5
39 tf_efficientnetv2_b0 6314.13 40.533 256 224 0.73 4.77 7.14
40 regnetx_008 6079.04 42.101 256 224 0.81 5.15 7.26
41 efficientnet_lite0 5804.98 44.09 256 224 0.4 6.74 4.65
42 dla46_c 5780.94 44.273 256 224 0.58 4.5 1.3
43 mobilenetv2_110d 5723.57 44.717 256 224 0.45 8.71 4.52
44 rexnet_100 5717.93 44.761 256 224 0.41 7.44 4.8
45 hardcorenas_f 5617.23 45.564 256 224 0.35 5.57 8.2
46 regnety_008 5508.59 46.462 256 224 0.81 5.25 6.26
47 hardcorenas_e 5410.0 47.31 256 224 0.35 5.65 8.07
48 fbnetc_100 5329.99 48.02 256 224 0.4 6.51 5.57
49 skresnet18 5316.12 48.145 256 224 1.82 3.24 11.96
50 tf_efficientnet_lite0 5240.32 48.842 256 224 0.4 6.74 4.65
51 mobilenetv2_140 5070.12 50.481 256 224 0.6 9.57 6.11
52 efficientnet_b0 5059.78 50.585 256 224 0.4 6.75 5.29
53 ese_vovnet19b_dw 5050.16 50.68 256 224 1.34 8.25 6.54
54 gluon_resnet34_v1b 4958.52 51.618 256 224 3.67 3.74 21.8
55 efficientnet_b1_pruned 4954.57 51.658 256 240 0.4 6.21 6.33
56 tv_resnet34 4937.29 51.84 256 224 3.67 3.74 21.8
57 resnet34 4928.32 51.933 256 224 3.67 3.74 21.8
58 hrnet_w18_small 4869.58 52.561 256 224 1.61 5.72 13.19
59 levit_384 4654.29 54.993 256 224 2.36 6.26 39.13
60 tf_efficientnet_b0_ap 4650.22 55.039 256 224 0.4 6.75 5.29
61 tf_efficientnet_b0_ns 4646.91 55.076 256 224 0.4 6.75 5.29
62 tf_efficientnet_b0 4644.25 55.108 256 224 0.4 6.75 5.29
63 dla46x_c 4605.94 55.57 256 224 0.54 5.66 1.07
64 selecsls42b 4570.17 56.005 256 224 2.98 4.62 32.46
65 deit_tiny_patch16_224 4543.82 56.329 256 224 1.26 5.97 5.72
66 vit_tiny_patch16_224 4538.07 56.399 256 224 1.26 5.97 5.72
67 gernet_m 4516.03 56.676 256 224 3.02 5.24 21.14
68 deit_tiny_distilled_patch16_224 4481.69 57.11 256 224 1.27 6.01 5.91
69 legacy_seresnet34 4474.42 57.204 256 224 3.67 3.74 21.96
70 resnet34d 4448.37 57.538 256 224 3.91 4.54 21.82
71 pit_ti_distilled_224 4332.05 59.084 256 224 0.71 6.23 5.1
72 pit_ti_224 4322.46 59.215 256 224 0.7 6.19 4.85
73 dla60x_c 4302.12 59.495 256 224 0.59 6.01 1.32
74 mixnet_s 4297.51 59.559 256 224 0.25 6.25 4.13
75 rexnet_130 4236.61 60.415 256 224 0.68 9.71 7.56
76 tf_efficientnetv2_b1 4231.93 60.481 256 240 1.21 7.34 8.14
77 xcit_nano_12_p16_224_dist 4191.2 61.067 256 224 0.56 4.17 3.05
78 xcit_nano_12_p16_224 4188.12 61.112 256 224 0.56 4.17 3.05
79 resmlp_12_distilled_224 4137.96 61.855 256 224 3.01 5.5 15.35
80 resmlp_12_224 4137.2 61.867 256 224 3.01 5.5 15.35
81 resnet26 4135.23 61.895 256 224 2.36 7.35 16.0
82 vit_base_patch32_sam_224 4111.04 62.261 256 224 4.41 5.01 88.22
83 mobilenetv2_120d 4108.55 62.299 256 224 0.69 11.97 5.83
84 vit_base_patch32_224 4102.94 62.384 256 224 4.41 5.01 88.22
85 tf_mixnet_s 4033.2 63.462 256 224 0.25 6.25 4.13
86 repvgg_b0 4020.07 63.669 256 224 3.41 6.15 15.82
87 selecsls60b 3957.1 64.683 256 224 3.63 5.52 32.77
88 selecsls60 3955.58 64.708 256 224 3.59 5.52 30.67
89 resnet26d 3771.71 67.862 256 224 2.6 8.15 16.01
90 dla34 3751.8 68.222 256 224 3.07 5.02 15.74
91 rexnet_150 3693.73 69.295 256 224 0.9 11.21 9.73
92 ecaresnet50d_pruned 3635.83 70.4 256 224 2.53 6.43 19.94
93 tf_efficientnet_lite1 3541.96 72.266 256 240 0.62 10.14 5.42
94 pit_xs_224 3506.77 72.991 256 224 1.4 7.71 10.62
95 regnetx_016 3495.21 73.233 256 224 1.62 7.93 9.19
96 pit_xs_distilled_224 3481.48 73.522 256 224 1.41 7.76 11.0
97 efficientnet_es_pruned 3365.67 76.052 256 224 1.81 8.73 5.44
98 efficientnet_es 3358.26 76.219 256 224 1.81 8.73 5.44
99 efficientnet_b2_pruned 3344.4 76.535 256 260 0.73 9.13 8.31
100 tf_efficientnet_es 3248.35 78.797 256 224 1.81 8.73 5.44
101 tf_efficientnetv2_b2 3221.93 79.444 256 260 1.72 9.84 10.1
102 resnest14d 3200.62 79.966 256 224 2.76 7.33 10.61
103 gernet_l 3173.09 80.669 256 256 4.57 8.0 31.08
104 regnety_016 3082.19 83.046 256 224 1.63 8.04 11.2
105 tf_efficientnet_cc_b0_8e 3079.83 83.109 256 224 0.42 9.42 24.01
106 tf_efficientnet_cc_b0_4e 3072.34 83.31 256 224 0.41 9.42 13.31
107 mixnet_m 3041.78 84.15 256 224 0.36 8.19 5.01
108 skresnet34 3025.8 84.595 256 224 3.67 5.13 22.28
109 resnext26ts 2999.56 85.335 256 256 2.43 10.52 10.3
110 repvgg_a2 2997.36 85.397 256 224 5.7 6.26 28.21
111 legacy_seresnext26_32x4d 2982.2 85.832 256 224 2.49 9.39 16.79
112 vit_tiny_r_s16_p8_384 2981.27 85.856 256 384 1.34 6.49 6.36
113 vit_small_patch32_384 2975.01 86.035 256 384 3.45 8.25 22.92
114 xcit_tiny_12_p16_224_dist 2962.26 86.406 256 224 1.24 6.29 6.72
115 xcit_tiny_12_p16_224 2958.87 86.506 256 224 1.24 6.29 6.72
116 resnet26t 2949.15 86.793 256 256 3.35 10.52 16.01
117 seresnext26ts 2930.19 87.355 256 256 2.43 10.52 10.39
118 eca_resnext26ts 2927.39 87.439 256 256 2.43 10.52 10.3
119 tf_mixnet_m 2918.13 87.716 256 224 0.36 8.19 5.01
120 tf_efficientnet_b1_ap 2909.55 87.973 256 240 0.71 10.88 7.79
121 tf_efficientnet_b1_ns 2907.96 88.021 256 240 0.71 10.88 7.79
122 tf_efficientnet_b1 2906.46 88.066 256 240 0.71 10.88 7.79
123 gcresnext26ts 2860.35 89.489 256 256 2.43 10.53 10.48
124 ecaresnet101d_pruned 2832.39 90.373 256 224 3.48 7.69 24.88
125 efficientnet_b1 2816.51 90.881 256 256 0.77 12.22 7.79
126 seresnext26t_32x4d 2803.22 91.313 256 224 2.7 10.09 16.81
127 seresnext26d_32x4d 2791.34 91.701 256 224 2.73 10.19 16.81
128 ecaresnetlight 2748.62 93.127 256 224 4.11 8.42 30.16
129 tf_efficientnet_lite2 2734.03 93.624 256 260 0.89 12.9 6.09
130 nf_regnet_b1 2722.26 94.028 256 288 1.02 9.2 10.22
131 crossvit_tiny_240 2719.71 94.117 256 240 1.57 9.08 7.01
132 rexnet_200 2697.36 94.896 256 224 1.56 14.91 16.37
133 resnetv2_50 2678.82 95.552 256 224 4.11 11.11 25.55
134 crossvit_9_240 2675.71 95.665 256 240 1.85 9.52 8.55
135 eca_botnext26ts_256 2660.24 96.221 256 256 2.46 11.6 10.59
136 tresnet_m 2657.69 96.314 256 224 5.74 7.31 31.39
137 botnet26t_256 2648.39 96.646 256 256 3.32 11.98 12.49
138 halonet26t 2611.82 98.005 256 256 3.19 11.69 12.48
139 vgg11 2609.78 98.082 256 224 7.61 7.44 132.86
140 eca_halonext26ts 2603.27 98.327 256 256 2.44 11.46 10.76
141 gluon_resnet50_v1b 2595.8 98.61 256 224 4.11 11.11 25.56
142 ssl_resnet50 2595.55 98.619 256 224 4.11 11.11 25.56
143 efficientnet_b3_pruned 2594.03 98.677 256 300 1.04 11.86 9.86
144 tv_resnet50 2591.78 98.763 256 224 4.11 11.11 25.56
145 crossvit_9_dagger_240 2589.88 98.836 256 240 1.99 9.97 8.78
146 resnet50 2584.36 99.039 256 224 4.11 11.11 25.56
147 swsl_resnet50 2581.65 99.146 256 224 4.11 11.11 25.56
148 convit_tiny 2544.18 100.61 256 224 1.26 7.94 5.71
149 hrnet_w18_small_v2 2509.11 102.017 256 224 2.62 9.65 15.6
150 resnet32ts 2503.04 102.263 256 256 4.63 11.58 17.96
151 bat_resnext26ts 2479.68 103.219 256 256 2.53 12.51 10.73
152 resnet33ts 2467.72 103.728 256 256 4.76 11.66 19.68
153 gluon_resnet50_v1c 2464.02 103.884 256 224 4.35 11.92 25.58
154 ese_vovnet39b 2457.44 104.162 256 224 7.09 6.74 24.57
155 cspresnet50 2453.31 104.338 256 256 4.54 11.5 21.62
156 cspresnext50 2451.69 104.407 256 224 3.1 12.14 20.57
157 gluon_resnet50_v1d 2445.69 104.663 256 224 4.35 11.92 25.58
158 resnet50d 2441.21 104.85 256 224 4.35 11.92 25.58
159 dpn68b 2432.24 105.242 256 224 2.35 10.47 12.61
160 legacy_seresnet50 2426.52 105.49 256 224 3.88 10.6 28.09
161 dpn68 2406.35 106.374 256 224 2.35 10.47 12.61
162 eca_resnet33ts 2405.01 106.434 256 256 4.76 11.66 19.68
163 seresnet33ts 2402.61 106.54 256 256 4.76 11.66 19.78
164 vgg11_bn 2383.08 107.413 256 224 7.62 7.44 132.87
165 mixnet_l 2358.59 108.528 256 224 0.58 10.84 7.33
166 lambda_resnet26t 2358.23 108.545 256 256 3.02 11.87 10.96
167 gcresnet33ts 2347.52 109.04 256 256 4.76 11.68 19.88
168 pit_s_224 2332.08 109.763 256 224 2.88 11.56 23.46
169 dla60 2324.78 110.106 256 224 4.26 10.16 22.04
170 seresnet50 2316.85 110.484 256 224 4.11 11.13 28.09
171 resnest26d 2313.67 110.634 256 224 3.64 9.97 17.07
172 pit_s_distilled_224 2311.97 110.718 256 224 2.9 11.64 24.04
173 deit_small_patch16_224 2297.25 111.426 256 224 4.61 11.95 22.05
174 vit_small_patch16_224 2293.15 111.622 256 224 4.61 11.95 22.05
175 deit_small_distilled_patch16_224 2268.28 112.85 256 224 4.63 12.02 22.44
176 tf_mixnet_l 2264.19 113.053 256 224 0.58 10.84 7.33
177 tf_efficientnet_b2_ns 2256.47 113.438 256 260 1.02 13.83 9.11
178 tf_efficientnet_b2_ap 2256.28 113.446 256 260 1.02 13.83 9.11
179 tf_efficientnet_b2 2253.15 113.605 256 260 1.02 13.83 9.11
180 tv_densenet121 2246.15 113.963 256 224 2.87 6.9 7.98
181 densenet121 2241.22 114.212 256 224 2.87 6.9 7.98
182 res2net50_48w_2s 2234.19 114.57 256 224 4.18 11.72 25.29
183 ecaresnet50d 2188.72 116.953 256 224 4.35 11.93 25.58
184 resnetblur50 2181.37 117.342 256 224 5.16 12.02 25.56
185 haloregnetz_b 2181.01 117.365 256 224 1.97 11.94 11.68
186 resnetrs50 2148.74 119.126 256 224 4.48 12.14 35.69
187 gluon_resnet50_v1s 2124.85 120.469 256 224 5.47 13.52 25.68
188 visformer_small 2123.42 120.549 256 224 4.88 11.43 40.22
189 gluon_inception_v3 2118.5 120.829 256 299 5.73 8.97 23.83
190 regnetx_032 2117.36 120.894 256 224 3.2 11.37 15.3
191 resmlp_24_distilled_224 2109.62 121.337 256 224 5.96 10.91 30.02
192 efficientnet_b2 2109.39 121.351 256 288 1.12 16.2 9.11
193 inception_v3 2109.39 121.351 256 299 5.73 8.97 23.83
194 resmlp_24_224 2108.77 121.386 256 224 5.96 10.91 30.02
195 tf_inception_v3 2108.49 121.403 256 299 5.73 8.97 23.83
196 adv_inception_v3 2107.2 121.474 256 299 5.73 8.97 23.83
197 efficientnet_em 2078.91 123.131 256 240 3.04 14.34 6.9
198 densenetblur121d 2047.01 125.05 256 224 3.11 7.9 8.0
199 tf_efficientnet_em 2016.96 126.911 256 240 3.04 14.34 6.9
200 ssl_resnext50_32x4d 2014.12 127.092 256 224 4.26 14.4 25.03
201 tv_resnext50_32x4d 2011.71 127.243 256 224 4.26 14.4 25.03
202 gluon_resnext50_32x4d 2008.79 127.428 256 224 4.26 14.4 25.03
203 swsl_resnext50_32x4d 2003.89 127.736 256 224 4.26 14.4 25.03
204 resnext50_32x4d 2003.33 127.776 256 224 4.26 14.4 25.03
205 tf_efficientnet_cc_b1_8e 1964.78 130.28 256 240 0.75 15.44 39.72
206 dla60x 1943.55 131.706 256 224 3.54 13.8 17.35
207 regnetx_040 1936.18 132.208 256 224 3.99 12.2 22.12
208 resnext50d_32x4d 1913.78 133.755 256 224 4.5 15.2 25.05
209 res2net50_26w_4s 1909.57 134.046 256 224 4.28 12.61 25.7
210 tf_efficientnetv2_b3 1906.95 134.234 256 300 3.04 15.74 14.36
211 regnety_040 1904.74 134.39 256 224 4.0 12.29 20.65
212 gcresnet50t 1896.24 134.993 256 256 5.42 14.67 25.9
213 efficientnetv2_rw_t 1892.3 135.274 256 288 3.19 16.42 13.65
214 sehalonet33ts 1872.56 136.7 256 256 3.55 14.7 13.69
215 lambda_resnet26rpt_256 1852.61 138.171 256 256 3.16 11.87 10.99
216 seresnext50_32x4d 1837.91 139.277 256 224 4.26 14.42 27.56
217 gcresnext50ts 1836.72 139.369 256 256 3.75 15.46 15.67
218 dla60_res2net 1835.75 139.44 256 224 4.15 12.34 20.85
219 gluon_seresnext50_32x4d 1834.84 139.511 256 224 4.26 14.42 27.56
220 legacy_seresnext50_32x4d 1831.03 139.801 256 224 4.26 14.42 27.56
221 resnest50d_1s4x24d 1825.82 140.197 256 224 4.43 13.57 25.68
222 repvgg_b1g4 1816.81 140.887 256 224 8.15 10.64 39.97
223 densenet169 1814.19 141.099 256 224 3.4 7.3 14.15
224 gc_efficientnetv2_rw_t 1807.69 141.606 256 288 3.2 16.45 13.68
225 coat_lite_tiny 1802.74 141.995 256 224 1.6 11.65 5.72
226 res2net50_14w_8s 1801.06 142.125 256 224 4.21 13.28 25.06
227 res2next50 1791.13 142.906 256 224 4.2 13.71 24.67
228 cspdarknet53 1752.54 146.063 256 256 6.57 16.81 27.64
229 ecaresnet26t 1748.79 146.375 256 320 5.24 16.44 16.01
230 dla60_res2next 1747.0 146.526 256 224 3.49 13.17 17.03
231 regnetz_b 1724.41 148.443 256 288 2.39 16.43 9.72
232 coat_lite_mini 1717.23 149.066 256 224 2.0 12.25 11.01
233 vgg13 1708.54 149.824 256 224 11.31 12.25 133.05
234 mixnet_xl 1689.17 151.542 256 224 0.93 14.57 11.9
235 vit_small_r26_s32_224 1664.97 153.742 256 224 3.56 9.85 36.43
236 resnetv2_101 1630.91 156.956 256 224 7.83 16.23 44.54
237 tf_efficientnet_lite3 1624.63 157.563 256 300 1.65 21.85 8.2
238 gluon_resnet101_v1b 1601.1 159.88 256 224 7.83 16.23 44.55
239 tv_resnet101 1599.29 160.06 256 224 7.83 16.23 44.55
240 repvgg_b1 1595.1 160.478 256 224 13.16 10.64 57.42
241 xcit_tiny_24_p16_224_dist 1586.68 161.331 256 224 2.34 11.82 12.12
242 xcit_tiny_24_p16_224 1585.43 161.458 256 224 2.34 11.82 12.12
243 xcit_small_12_p16_224_dist 1555.24 164.59 256 224 4.82 12.58 26.25
244 lambda_resnet50ts 1554.98 164.616 256 256 5.07 17.48 21.54
245 xcit_small_12_p16_224 1551.41 164.997 256 224 4.82 12.58 26.25
246 gluon_resnet101_v1c 1549.68 165.185 256 224 8.08 17.04 44.57
247 resnest50d 1546.83 165.485 256 224 5.4 14.36 27.48
248 vgg13_bn 1545.98 165.579 256 224 11.33 12.25 133.05
249 gluon_resnet101_v1d 1542.35 165.971 256 224 8.08 17.04 44.57
250 dla102 1540.91 166.124 256 224 7.19 14.18 33.27
251 twins_svt_small 1527.64 167.568 256 224 2.94 13.75 24.06
252 wide_resnet50_2 1520.69 168.321 256 224 11.43 14.4 68.88
253 gmixer_24_224 1499.34 170.73 256 224 5.28 14.45 24.72
254 resnetv2_50x1_bit_distilled 1491.85 171.586 256 224 4.23 11.11 25.55
255 regnetx_080 1490.15 171.784 256 224 8.02 14.06 39.57
256 xcit_nano_12_p16_384_dist 1485.24 172.347 256 384 1.64 12.15 3.05
257 crossvit_small_240 1471.27 173.987 256 240 5.63 18.17 26.86
258 legacy_seresnet101 1468.28 174.342 256 224 7.61 15.74 49.33
259 halonet50ts 1455.95 175.819 256 256 5.3 19.2 22.73
260 res2net50_26w_6s 1449.56 176.589 256 224 6.33 15.28 37.05
261 regnetx_064 1419.83 180.292 256 224 6.49 16.37 26.21
262 densenet201 1416.48 180.718 256 224 4.34 7.85 20.01
263 resmlp_36_224 1415.51 180.84 256 224 8.91 16.33 44.69
264 resmlp_36_distilled_224 1415.35 180.86 256 224 8.91 16.33 44.69
265 gluon_resnet101_v1s 1409.69 181.589 256 224 9.19 18.64 44.67
266 lamhalobotnet50ts_256 1390.79 184.054 256 256 5.02 18.44 22.57
267 nf_resnet50 1387.87 184.443 256 288 6.88 18.37 25.56
268 ecaresnet101d 1376.05 186.03 256 224 8.08 17.07 44.57
269 vgg16 1375.43 186.112 256 224 15.47 13.56 138.36
270 hrnet_w18 1354.53 188.983 256 224 4.32 16.31 21.3
271 regnety_032 1350.9 189.492 256 288 5.29 18.61 19.44
272 crossvit_15_240 1346.42 190.123 256 240 5.81 19.77 27.53
273 tresnet_l 1344.95 190.33 256 224 10.88 11.9 55.99
274 gmlp_s16_224 1341.95 190.757 256 224 4.42 15.1 19.42
275 tf_efficientnet_b3_ap 1337.77 191.35 256 300 1.87 23.83 12.23
276 tf_efficientnet_b3_ns 1337.21 191.429 256 300 1.87 23.83 12.23
277 tf_efficientnet_b3 1336.96 191.464 256 300 1.87 23.83 12.23
278 resnet51q 1335.36 191.695 256 288 8.07 20.94 35.7
279 vit_base_patch32_384 1326.76 192.939 256 384 13.06 16.5 88.3
280 xception 1314.05 194.804 256 299 8.4 35.83 22.86
281 crossvit_15_dagger_240 1305.29 196.113 256 240 6.13 20.43 28.21
282 dla102x 1302.51 196.531 256 224 5.89 19.42 26.31
283 efficientnet_b3 1297.94 197.224 256 320 2.01 26.52 12.23
284 mixer_b16_224 1285.59 199.119 256 224 12.62 14.53 59.88
285 mixer_b16_224_miil 1283.81 199.396 256 224 12.62 14.53 59.88
286 cait_xxs24_224 1283.41 199.457 256 224 2.53 20.29 11.96
287 skresnext50_32x4d 1277.72 200.346 256 224 4.5 17.18 27.48
288 regnety_064 1270.37 201.504 256 224 6.39 16.41 30.58
289 ssl_resnext101_32x4d 1267.29 201.994 256 224 8.01 21.23 44.18
290 gluon_resnext101_32x4d 1262.61 202.744 256 224 8.01 21.23 44.18
291 swsl_resnext101_32x4d 1261.41 202.934 256 224 8.01 21.23 44.18
292 vgg16_bn 1259.82 203.192 256 224 15.5 13.56 138.37
293 repvgg_b2g4 1250.78 204.659 256 224 12.63 12.9 61.76
294 halo2botnet50ts_256 1247.07 205.268 256 256 5.02 21.78 22.64
295 swin_tiny_patch4_window7_224 1247.03 205.273 256 224 4.51 17.06 28.29
296 twins_pcpvt_small 1237.18 206.911 256 224 3.83 18.08 24.11
297 regnety_080 1231.05 207.941 256 224 8.0 17.97 39.18
298 resnest50d_4s2x40d 1214.34 210.797 256 224 4.4 17.94 30.42
299 resnet61q 1207.53 211.987 256 288 9.87 21.52 36.85
300 eca_nfnet_l0 1191.71 214.805 256 288 7.12 17.29 24.14
301 nfnet_l0 1190.26 215.067 256 288 7.13 17.29 35.07
302 res2net101_26w_4s 1183.89 216.22 256 224 8.1 18.45 45.21
303 dpn92 1178.74 217.169 256 224 6.54 18.21 37.67
304 res2net50_26w_8s 1178.7 217.174 256 224 8.37 17.95 48.4
305 vit_tiny_patch16_384 1172.78 218.271 256 384 4.7 25.39 5.79
306 convit_small 1169.52 218.881 256 224 5.76 17.87 27.78
307 vgg19 1152.13 222.185 256 224 19.63 14.86 143.67
308 gluon_seresnext101_32x4d 1150.11 222.576 256 224 8.02 21.26 48.96
309 legacy_seresnext101_32x4d 1149.76 222.644 256 224 8.02 21.26 48.96
310 hrnet_w32 1142.24 224.109 256 224 8.97 22.02 41.23
311 xcit_nano_12_p8_224 1127.98 226.94 256 224 2.16 15.71 3.05
312 xcit_nano_12_p8_224_dist 1127.81 226.974 256 224 2.16 15.71 3.05
313 hrnet_w30 1123.84 227.778 256 224 8.15 21.21 37.71
314 tv_resnet152 1116.24 229.331 256 224 11.56 22.56 60.19
315 gluon_resnet152_v1b 1115.71 229.44 256 224 11.56 22.56 60.19
316 ecaresnet50t 1110.54 230.506 256 320 8.82 24.13 25.57
317 regnetz_c 1099.38 232.841 256 320 3.92 25.88 13.46
318 gluon_resnet152_v1c 1090.66 234.708 256 224 11.8 23.36 60.21
319 gluon_resnet152_v1d 1087.27 235.441 256 224 11.8 23.36 60.21
320 repvgg_b2 1087.24 235.444 256 224 20.45 12.9 89.02
321 xception41 1068.11 239.655 256 299 9.28 39.86 26.97
322 densenet161 1067.45 239.812 256 224 7.79 11.06 28.68
323 vgg19_bn 1061.8 241.089 256 224 19.66 14.86 143.68
324 inception_v4 1057.29 242.117 256 299 12.28 15.09 42.68
325 convmixer_1024_20_ks9_p14 1048.55 244.135 256 224 5.55 5.51 24.38
326 dla169 1043.9 245.223 256 224 11.6 20.2 53.39
327 xcit_tiny_12_p16_384_dist 1041.14 245.869 256 384 3.64 18.26 6.72
328 regnetx_120 1028.54 248.883 256 224 12.13 21.37 46.11
329 gluon_resnet152_v1s 1019.74 251.033 256 224 12.92 24.96 60.32
330 coat_lite_small 1019.01 251.211 256 224 3.96 22.09 19.84
331 vit_base_patch16_224_miil 1015.29 252.134 256 224 17.58 23.9 86.54
332 legacy_seresnet152 1014.16 252.414 256 224 11.33 22.08 66.82
333 repvgg_b3g4 1009.72 253.523 256 224 17.89 15.1 83.83
334 jx_nest_tiny 1009.0 253.705 256 224 5.83 25.48 17.06
335 crossvit_18_240 995.7 257.092 256 240 9.05 26.26 43.27
336 vit_base_patch16_224 987.74 259.165 256 224 17.58 23.9 86.57
337 vit_base_patch16_sam_224 983.41 260.307 256 224 17.58 23.9 86.57
338 tresnet_xl 983.24 260.352 256 224 15.17 15.34 78.44
339 deit_base_patch16_224 981.95 260.694 256 224 17.58 23.9 86.57
340 regnety_120 981.03 260.937 256 224 12.14 21.38 51.82
341 deit_base_distilled_patch16_224 972.1 263.335 256 224 17.68 24.05 87.34
342 crossvit_18_dagger_240 968.8 264.234 256 240 9.5 27.03 44.27
343 efficientnet_el_pruned 927.76 275.921 256 300 8.0 30.7 10.59
344 efficientnet_el 927.59 275.971 256 300 8.0 30.7 10.59
345 tf_efficientnet_el 908.45 281.783 256 300 8.0 30.7 10.59
346 beit_base_patch16_224 907.03 282.225 256 224 17.58 23.9 86.53
347 dm_nfnet_f0 906.74 282.318 256 256 12.62 18.05 71.49
348 beit_base_patch16_224_in22k 904.43 283.033 256 224 17.6 23.92 102.56
349 twins_pcpvt_base 903.59 283.303 256 224 6.68 25.25 43.83
350 dla102x2 892.39 286.858 256 224 9.34 29.91 41.28
351 twins_svt_base 884.1 289.548 256 224 8.59 26.33 56.07
352 wide_resnet101_2 879.83 290.95 256 224 22.8 21.23 126.89
353 tf_efficientnetv2_s_in21ft1k 867.65 295.037 256 384 8.44 35.77 21.46
354 tf_efficientnetv2_s 866.61 295.392 256 384 8.44 35.77 21.46
355 cait_xxs36_224 864.05 296.268 256 224 3.77 30.34 17.3
356 resnetrs101 850.72 300.905 256 288 13.56 28.53 63.62
357 repvgg_b3 845.91 302.619 256 224 29.16 15.1 123.09
358 efficientnetv2_rw_s 844.69 303.059 256 384 8.72 38.03 23.94
359 dpn98 840.2 304.675 256 224 11.73 25.2 61.57
360 pit_b_distilled_224 838.63 305.249 256 224 12.5 33.07 74.79
361 pit_b_224 837.02 305.836 256 224 12.42 32.94 73.76
362 ens_adv_inception_resnet_v2 834.35 306.814 256 299 13.18 25.06 55.84
363 regnetx_160 833.02 307.301 256 224 15.99 25.52 54.28
364 inception_resnet_v2 832.69 307.427 256 299 13.18 25.06 55.84
365 xcit_small_24_p16_224_dist 830.32 308.297 256 224 9.1 23.64 47.67
366 xcit_small_24_p16_224 828.74 308.886 256 224 9.1 23.64 47.67
367 swin_small_patch4_window7_224 809.9 316.074 256 224 8.77 27.47 49.61
368 gluon_resnext101_64x4d 803.65 318.536 256 224 15.52 31.21 83.46
369 xcit_tiny_12_p8_224_dist 791.87 323.27 256 224 4.81 23.6 6.71
370 xcit_tiny_12_p8_224 791.64 323.364 256 224 4.81 23.6 6.71
371 gluon_xception65 784.11 326.474 256 299 13.96 52.48 39.92
372 xception65 777.91 329.074 256 299 13.96 52.48 39.92
373 resnet101d 776.37 329.723 256 320 16.48 34.77 44.57
374 ig_resnext101_32x8d 775.21 330.222 256 224 16.48 31.21 88.79
375 swsl_resnext101_32x8d 773.83 330.808 256 224 16.48 31.21 88.79
376 resnext101_32x8d 773.08 331.13 256 224 16.48 31.21 88.79
377 ssl_resnext101_32x8d 769.28 332.765 256 224 16.48 31.21 88.79
378 tf_efficientnet_lite4 764.13 335.01 256 380 4.04 45.66 13.01
379 hrnet_w40 760.18 336.752 256 224 12.75 25.29 57.56
380 gluon_seresnext101_64x4d 756.43 338.418 256 224 15.53 31.25 88.23
381 resnest101e 755.07 339.024 256 256 13.38 28.66 48.28
382 hrnet_w48 716.4 357.329 256 224 17.34 28.56 77.47
383 cait_s24_224 714.86 358.098 256 224 9.35 40.58 46.92
384 tresnet_m_448 713.55 358.758 256 448 22.94 29.21 31.39
385 coat_tiny 707.04 362.062 256 224 4.35 27.2 5.5
386 regnetz_d 704.94 363.138 256 320 9.33 37.08 27.58
387 hrnet_w44 702.53 364.385 256 224 14.94 26.92 67.06
388 vit_large_r50_s32_224 680.11 376.394 256 224 19.58 24.41 328.99
389 jx_nest_small 676.97 378.142 256 224 10.35 40.04 38.35
390 twins_svt_large 673.22 380.252 256 224 15.15 35.1 99.27
391 crossvit_base_240 671.16 381.416 256 240 21.22 36.33 105.03
392 efficientnet_b4 667.18 383.692 256 384 4.51 50.04 19.34
393 twins_pcpvt_large 657.69 389.226 256 224 9.84 35.82 60.99
394 tf_efficientnet_b4 637.24 401.719 256 380 4.49 49.49 19.34
395 tf_efficientnet_b4_ap 636.68 402.068 256 380 4.49 49.49 19.34
396 tf_efficientnet_b4_ns 636.62 402.109 256 380 4.49 49.49 19.34
397 convit_base 621.76 411.725 256 224 17.52 31.77 86.54
398 dpn131 619.15 413.454 256 224 16.09 32.97 79.25
399 swin_base_patch4_window7_224 617.4 414.625 256 224 15.47 36.63 87.77
400 swin_base_patch4_window7_224_in22k 616.51 415.228 256 224 15.49 36.65 109.13
401 xcit_medium_24_p16_224_dist 613.3 417.401 256 224 16.13 31.71 84.4
402 xcit_medium_24_p16_224 612.89 417.675 256 224 16.13 31.71 84.4
403 vit_small_patch16_384 594.17 430.838 256 384 15.52 50.78 22.2
404 coat_mini 592.46 432.084 256 224 6.82 33.68 10.34
405 xception71 590.14 433.78 256 299 18.09 69.92 42.34
406 vit_small_r26_s32_384 574.75 445.396 256 384 10.43 29.85 36.47
407 hrnet_w64 570.22 448.937 256 224 28.97 35.09 128.06
408 dpn107 566.19 452.128 256 224 18.38 33.46 86.92
409 eca_nfnet_l1 559.71 457.367 256 320 14.92 34.42 41.41
410 gluon_senet154 559.51 457.529 256 224 20.77 38.69 115.09
411 legacy_senet154 558.72 458.178 256 224 20.77 38.69 115.09
412 xcit_tiny_24_p16_384_dist 556.09 460.342 256 384 6.87 34.29 12.12
413 xcit_small_12_p16_384_dist 546.87 468.099 256 384 14.14 36.51 26.25
414 resnet152d 546.29 468.597 256 320 24.08 47.67 60.21
415 regnety_320 522.26 490.163 256 224 32.34 30.26 145.05
416 jx_nest_base 511.39 500.584 256 224 17.96 53.39 67.72
417 regnety_160 509.43 502.501 256 288 26.37 38.07 83.59
418 tnt_s_patch16_224 497.77 514.282 256 224 5.24 24.37 23.76
419 seresnet152d 489.63 522.829 256 320 24.09 47.72 66.84
420 resnetrs152 483.98 528.928 256 320 24.34 48.14 86.62
421 regnetx_320 460.34 556.1 256 224 31.81 36.3 107.81
422 vit_large_patch32_384 457.67 559.348 256 384 45.31 43.86 306.63
423 mixer_l16_224 429.82 595.582 256 224 44.6 41.69 208.2
424 xcit_tiny_24_p8_224 417.75 612.797 256 224 9.21 45.39 12.11
425 xcit_tiny_24_p8_224_dist 417.56 613.068 256 224 9.21 45.39 12.11
426 xcit_small_12_p8_224 416.17 615.111 256 224 18.69 47.21 26.21
427 xcit_small_12_p8_224_dist 415.93 615.473 256 224 18.69 47.21 26.21
428 efficientnetv2_rw_m 404.25 633.263 256 416 21.49 79.62 53.24
429 resnet200d 391.43 653.991 256 320 31.25 67.33 64.69
430 resnetv2_50x1_bitm 388.67 658.636 256 448 16.62 44.46 25.55
431 xcit_nano_12_p8_384_dist 387.79 660.138 256 384 6.34 46.08 3.05
432 swin_large_patch4_window7_224 385.11 664.722 256 224 34.53 54.94 196.53
433 swin_large_patch4_window7_224_in22k 384.77 665.318 256 224 34.56 54.96 228.57
434 xcit_large_24_p16_224 374.96 682.722 256 224 35.86 47.27 189.1
435 xcit_large_24_p16_224_dist 374.65 683.281 256 224 35.86 47.27 189.1
436 ssl_resnext101_32x16d 366.28 698.897 256 224 36.27 51.18 194.03
437 ig_resnext101_32x16d 365.74 699.945 256 224 36.27 51.18 194.03
438 swsl_resnext101_32x16d 365.46 700.478 256 224 36.27 51.18 194.03
439 tresnet_l_448 350.13 731.146 256 448 43.5 47.56 55.99
440 resnetrs200 346.06 739.735 256 320 31.51 67.81 93.21
441 tf_efficientnetv2_m 339.58 753.868 256 480 24.76 89.84 54.14
442 tf_efficientnetv2_m_in21ft1k 339.57 753.879 256 480 24.76 89.84 54.14
443 vit_large_patch16_224 332.37 770.202 256 224 61.6 63.52 304.33
444 dm_nfnet_f1 329.67 776.526 256 320 35.97 46.77 132.63
445 tf_efficientnet_b5 319.74 800.623 256 456 10.46 98.86 30.39
446 tf_efficientnet_b5_ap 319.51 801.207 256 456 10.46 98.86 30.39
447 tf_efficientnet_b5_ns 319.48 801.287 256 456 10.46 98.86 30.39
448 crossvit_15_dagger_408 308.22 830.557 256 408 21.45 95.05 28.5
449 beit_large_patch16_224 304.41 840.964 256 224 61.6 63.52 304.43
450 beit_large_patch16_224_in22k 303.89 842.402 256 224 61.62 63.54 325.79
451 xcit_small_24_p16_384_dist 291.33 878.717 256 384 26.72 68.58 47.67
452 convmixer_768_32 284.41 900.105 256 224 19.55 25.95 21.11
453 eca_nfnet_l2 277.7 921.837 256 384 30.05 68.28 56.72
454 resnetv2_152x2_bit_teacher 272.08 940.888 256 224 46.95 45.11 236.34
455 xcit_tiny_12_p8_384_dist 271.29 943.618 256 384 14.13 69.14 6.71
456 tresnet_xl_448 265.72 963.421 256 448 60.65 61.31 78.44
457 vit_base_patch16_384 260.82 981.51 256 384 55.54 101.56 86.86
458 deit_base_patch16_384 260.27 983.563 256 384 55.54 101.56 86.86
459 resnest200e 257.06 995.856 256 320 35.69 82.78 70.2
460 deit_base_distilled_patch16_384 256.78 996.936 256 384 55.65 101.82 87.63
461 resnetv2_101x1_bitm 243.19 1052.661 256 448 31.65 64.93 44.54
462 cait_xxs24_384 238.44 1073.626 256 384 9.63 122.66 12.03
463 ecaresnet269d 229.27 1116.568 256 352 50.25 101.25 102.09
464 crossvit_18_dagger_408 229.17 558.535 128 408 32.47 124.87 44.61
465 vit_large_r50_s32_384 229.1 1117.419 256 384 57.43 76.52 329.09
466 nasnetalarge 226.47 1130.365 256 331 23.89 90.56 88.75
467 pnasnet5large 225.65 1134.478 256 331 25.04 92.89 86.06
468 beit_base_patch16_384 223.91 1143.322 256 384 55.54 101.56 86.74
469 resnetrs270 222.05 1152.857 256 352 51.13 105.48 129.86
470 xcit_small_24_p8_224 218.32 1172.571 256 224 35.81 90.78 47.63
471 xcit_small_24_p8_224_dist 218.2 1173.193 256 224 35.81 90.78 47.63
472 xcit_medium_24_p16_384_dist 212.67 1203.725 256 384 47.39 91.64 84.4
473 resmlp_big_24_224 199.99 1280.033 256 224 100.23 87.31 129.14
474 resmlp_big_24_224_in22ft1k 199.55 1282.862 256 224 100.23 87.31 129.14
475 resmlp_big_24_distilled_224 199.5 1283.208 256 224 100.23 87.31 129.14
476 tf_efficientnetv2_l 196.16 1305.054 256 480 56.4 157.99 118.52
477 tf_efficientnetv2_l_in21ft1k 195.97 1306.333 256 480 56.4 157.99 118.52
478 dm_nfnet_f2 187.32 1366.603 256 352 63.22 79.06 193.78
479 tf_efficientnet_b6_ns 184.64 693.239 128 528 19.4 167.39 43.04
480 tf_efficientnet_b6_ap 184.63 693.254 128 528 19.4 167.39 43.04
481 tf_efficientnet_b6 184.56 693.543 128 528 19.4 167.39 43.04
482 swin_base_patch4_window12_384 172.29 742.9 128 384 47.19 134.78 87.9
483 swin_base_patch4_window12_384_in22k 172.09 743.782 128 384 47.21 134.8 109.27
484 cait_xs24_384 168.76 1516.911 256 384 19.28 183.98 26.67
485 vit_base_r50_s16_384 164.61 1555.149 256 384 67.43 135.03 98.95
486 convmixer_1536_20 163.58 1564.936 256 224 48.68 33.03 51.63
487 cait_xxs36_384 159.38 1606.206 256 384 14.35 183.7 17.37
488 xcit_medium_24_p8_224 158.47 1615.434 256 224 63.53 121.23 84.32
489 xcit_medium_24_p8_224_dist 158.47 1615.404 256 224 63.53 121.23 84.32
490 resnetrs350 147.45 1736.143 256 384 77.59 154.74 163.96
491 ig_resnext101_32x32d 144.94 1766.221 256 224 87.29 91.12 468.53
492 xcit_tiny_24_p8_384_dist 140.02 1828.249 256 384 27.05 132.95 12.11
493 xcit_small_12_p8_384_dist 138.32 1850.769 256 384 54.92 138.29 26.21
494 cait_s24_384 130.37 1963.692 256 384 32.17 245.31 47.06
495 xcit_large_24_p16_384_dist 128.67 1989.551 256 384 105.35 137.17 189.1
496 tf_efficientnetv2_xl_in21ft1k 125.28 2043.358 256 512 93.85 247.32 208.12
497 resnest269e 119.95 2134.143 256 416 77.69 171.98 110.93
498 swin_large_patch4_window12_384 108.71 1177.454 128 384 104.08 202.16 196.74
499 swin_large_patch4_window12_384_in22k 108.63 1178.305 128 384 104.11 202.18 228.77
500 resnetrs420 107.0 2392.495 256 416 108.45 213.79 191.89
501 tf_efficientnet_b7_ns 106.07 603.375 64 600 38.33 289.94 66.35
502 tf_efficientnet_b7_ap 105.98 603.889 64 600 38.33 289.94 66.35
503 tf_efficientnet_b7 105.95 604.03 64 600 38.33 289.94 66.35
504 dm_nfnet_f3 102.63 2494.386 256 416 115.58 141.78 254.92
505 xcit_large_24_p8_224 95.99 2666.968 256 224 141.23 181.56 188.93
506 xcit_large_24_p8_224_dist 95.77 2673.153 256 224 141.23 181.56 188.93
507 resnetv2_152x2_bit_teacher_384 94.95 2696.08 256 384 136.16 132.56 236.34
508 resnetv2_50x3_bitm 93.71 1365.901 128 448 145.7 133.37 217.32
509 vit_large_patch16_384 89.71 2853.476 256 384 191.21 270.24 304.72
510 cait_s36_384 87.22 2935.166 256 384 47.99 367.4 68.37
511 ig_resnext101_32x48d 85.91 1489.933 128 224 153.57 131.06 828.41
512 beit_large_patch16_384 77.75 3292.734 256 384 191.21 270.24 305.0
513 xcit_small_24_p8_384_dist 72.45 3533.673 256 384 105.24 265.91 47.63
514 resnetv2_152x2_bitm 69.73 1835.714 128 448 184.99 180.43 236.34
515 tf_efficientnet_b8 66.92 956.333 64 672 63.48 442.89 87.41
516 tf_efficientnet_b8_ap 66.91 956.434 64 672 63.48 442.89 87.41
517 dm_nfnet_f4 57.01 4490.519 256 512 216.26 262.26 316.07
518 resnetv2_101x3_bitm 56.17 2278.772 128 448 280.33 194.78 387.93
519 xcit_medium_24_p8_384_dist 53.81 4757.31 256 384 186.67 354.73 84.32
520 dm_nfnet_f5 41.46 6174.452 256 544 290.97 349.71 377.21
521 tf_efficientnet_l2_ns_475 38.52 1661.585 64 475 172.11 609.89 480.31
522 xcit_large_24_p8_384_dist 32.44 7891.525 256 384 415.0 531.82 188.93
523 beit_large_patch16_512 31.38 2039.285 64 512 362.24 656.39 305.67
524 cait_m36_384 30.16 8488.214 256 384 173.11 734.81 271.22
525 dm_nfnet_f6 30.03 8525.766 256 576 378.69 452.2 438.36
526 resnetv2_152x4_bitm 18.21 3515.177 64 480 844.84 414.26 936.53
527 tf_efficientnet_l2_ns 13.56 1770.059 24 800 479.12 1707.39 480.31
528 cait_m48_448 13.08 9784.533 128 448 329.41 1708.23 356.46

@ -452,10 +452,10 @@ visformer_small,in1k
vit_base_patch16_224,in21k
vit_base_patch16_224_miil,in21k
vit_base_patch16_384,in21k
vit_base_patch16_sam_224,in1k
vit_base_patch16_224_sam,in1k
vit_base_patch32_224,in21k
vit_base_patch32_384,in21k
vit_base_patch32_sam_224,in1k
vit_base_patch32_224_sam,in1k
vit_base_r50_s16_384,in21k
vit_large_patch16_224,in21k
vit_large_patch16_384,in21k
1 model pretrain
452 vit_base_patch16_224 in21k
453 vit_base_patch16_224_miil in21k
454 vit_base_patch16_384 in21k
455 vit_base_patch16_sam_224 vit_base_patch16_224_sam in1k
456 vit_base_patch32_224 in21k
457 vit_base_patch32_384 in21k
458 vit_base_patch32_sam_224 vit_base_patch32_224_sam in1k
459 vit_base_r50_s16_384 in21k
460 vit_large_patch16_224 in21k
461 vit_large_patch16_384 in21k

@ -1,522 +1,553 @@
model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation
beit_large_patch16_512,98.560,1.440,99.840,0.160,305.67,512,1.000,bicubic
tf_efficientnet_l2_ns,98.540,1.460,99.820,0.180,480.31,800,0.960,bicubic
beit_large_patch16_384,98.500,1.500,99.820,0.180,305.00,384,1.000,bicubic
tf_efficientnet_l2_ns_475,98.490,1.510,99.830,0.170,480.31,475,0.936,bicubic
tf_efficientnet_l2_ns,98.550,1.450,99.820,0.180,480.31,800,0.960,bicubic
beit_large_patch16_384,98.520,1.480,99.820,0.180,305.00,384,1.000,bicubic
tf_efficientnet_l2_ns_475,98.500,1.500,99.830,0.170,480.31,475,0.936,bicubic
convnext_xlarge_384_in22ft1k,98.350,1.650,99.800,0.200,350.20,384,1.000,bicubic
convnext_large_384_in22ft1k,98.220,1.780,99.730,0.270,197.77,384,1.000,bicubic
vit_large_patch16_384,98.210,1.790,99.800,0.200,304.72,384,1.000,bicubic
beit_large_patch16_224,98.170,1.830,99.760,0.240,304.43,224,0.900,bicubic
swin_large_patch4_window12_384,98.020,1.980,99.690,0.310,196.74,384,1.000,bicubic
tf_efficientnet_b7_ns,97.880,2.120,99.720,0.280,66.35,600,0.949,bicubic
swin_base_patch4_window12_384,97.870,2.130,99.710,0.290,87.90,384,1.000,bicubic
beit_large_patch16_224,98.180,1.820,99.760,0.240,304.43,224,0.900,bicubic
swin_large_patch4_window12_384,98.040,1.960,99.690,0.310,196.74,384,1.000,bicubic
convnext_base_384_in22ft1k,97.950,2.050,99.650,0.350,88.59,384,1.000,bicubic
convnext_xlarge_in22ft1k,97.920,2.080,99.680,0.320,350.20,224,0.875,bicubic
tf_efficientnet_b7_ns,97.910,2.090,99.720,0.280,66.35,600,0.949,bicubic
swin_base_patch4_window12_384,97.890,2.110,99.710,0.290,87.90,384,1.000,bicubic
vit_large_r50_s32_384,97.860,2.140,99.670,0.330,329.09,384,1.000,bicubic
beit_base_patch16_384,97.830,2.170,99.700,0.300,86.74,384,1.000,bicubic
vit_base_patch16_384,97.830,2.170,99.670,0.330,86.86,384,1.000,bicubic
tf_efficientnetv2_l_in21ft1k,97.680,2.320,99.670,0.330,118.52,480,1.000,bicubic
tf_efficientnetv2_xl_in21ft1k,97.670,2.330,99.490,0.510,208.12,512,1.000,bicubic
swin_large_patch4_window7_224,97.660,2.340,99.580,0.420,196.53,224,0.900,bicubic
vit_base_patch16_384,97.840,2.160,99.670,0.330,86.86,384,1.000,bicubic
convnext_large_in22ft1k,97.830,2.170,99.690,0.310,197.77,224,0.875,bicubic
beit_base_patch16_384,97.810,2.190,99.700,0.300,86.74,384,1.000,bicubic
tf_efficientnetv2_l_in21ft1k,97.700,2.300,99.670,0.330,118.52,480,1.000,bicubic
swin_large_patch4_window7_224,97.650,2.350,99.580,0.420,196.53,224,0.900,bicubic
tf_efficientnetv2_xl_in21ft1k,97.650,2.350,99.490,0.510,208.12,512,1.000,bicubic
vit_large_patch16_224,97.640,2.360,99.590,0.410,304.33,224,0.900,bicubic
ig_resnext101_32x48d,97.640,2.360,99.710,0.290,828.41,224,0.875,bilinear
tf_efficientnet_b6_ns,97.630,2.370,99.580,0.420,43.04,528,0.942,bicubic
dm_nfnet_f6,97.610,2.390,99.550,0.450,438.36,576,0.956,bicubic
dm_nfnet_f4,97.570,2.430,99.510,0.490,316.07,512,0.951,bicubic
ig_resnext101_32x48d,97.620,2.380,99.710,0.290,828.41,224,0.875,bilinear
dm_nfnet_f6,97.600,2.400,99.550,0.450,438.36,576,0.956,bicubic
dm_nfnet_f4,97.580,2.420,99.510,0.490,316.07,512,0.951,bicubic
vit_base_patch8_224,97.580,2.420,99.670,0.330,86.58,224,0.900,bicubic
dm_nfnet_f5,97.540,2.460,99.570,0.430,377.21,544,0.954,bicubic
xcit_large_24_p8_384_dist,97.530,2.470,99.540,0.460,188.93,384,1.000,bicubic
xcit_large_24_p16_384_dist,97.530,2.470,99.480,0.520,189.10,384,1.000,bicubic
resnetv2_152x4_bitm,97.490,2.510,99.620,0.380,936.53,480,1.000,bilinear
tf_efficientnet_b5_ns,97.490,2.510,99.630,0.370,30.39,456,0.934,bicubic
xcit_large_24_p8_384_dist,97.520,2.480,99.540,0.460,188.93,384,1.000,bicubic
xcit_large_24_p16_384_dist,97.520,2.480,99.480,0.520,189.10,384,1.000,bicubic
tf_efficientnet_b5_ns,97.500,2.500,99.630,0.370,30.39,456,0.934,bicubic
resnetv2_152x4_bitm,97.490,2.510,99.610,0.390,936.53,480,1.000,bilinear
cait_m48_448,97.480,2.520,99.550,0.450,356.46,448,1.000,bicubic
tf_efficientnetv2_m_in21ft1k,97.480,2.520,99.530,0.470,54.14,480,1.000,bicubic
convnext_base_in22ft1k,97.470,2.530,99.610,0.390,88.59,224,0.875,bicubic
cait_m36_384,97.400,2.600,99.510,0.490,271.22,384,1.000,bicubic
ig_resnext101_32x32d,97.360,2.640,99.680,0.320,468.53,224,0.875,bilinear
ig_resnext101_32x32d,97.370,2.630,99.680,0.320,468.53,224,0.875,bilinear
dm_nfnet_f3,97.350,2.650,99.560,0.440,254.92,416,0.940,bicubic
cait_s36_384,97.340,2.660,99.530,0.470,68.37,384,1.000,bicubic
xcit_medium_24_p8_384_dist,97.300,2.700,99.510,0.490,84.32,384,1.000,bicubic
cait_s36_384,97.330,2.670,99.530,0.470,68.37,384,1.000,bicubic
xcit_medium_24_p8_384_dist,97.290,2.710,99.510,0.490,84.32,384,1.000,bicubic
tf_efficientnetv2_l,97.280,2.720,99.550,0.450,118.52,480,1.000,bicubic
xcit_medium_24_p16_384_dist,97.280,2.720,99.460,0.540,84.40,384,1.000,bicubic
tf_efficientnetv2_l,97.270,2.730,99.550,0.450,118.52,480,1.000,bicubic
swin_base_patch4_window7_224,97.260,2.740,99.530,0.470,87.77,224,0.900,bicubic
xcit_small_24_p8_384_dist,97.250,2.750,99.610,0.390,47.63,384,1.000,bicubic
swin_base_patch4_window7_224,97.250,2.750,99.530,0.470,87.77,224,0.900,bicubic
xcit_small_24_p8_384_dist,97.240,2.760,99.610,0.390,47.63,384,1.000,bicubic
xcit_small_12_p8_384_dist,97.230,2.770,99.480,0.520,26.21,384,1.000,bicubic
tf_efficientnet_b8,97.210,2.790,99.500,0.500,87.41,672,0.954,bicubic
swsl_resnext101_32x8d,97.200,2.800,99.570,0.430,88.79,224,0.875,bilinear
tf_efficientnet_b7_ap,97.200,2.800,99.540,0.460,66.35,600,0.949,bicubic
tf_efficientnet_b8,97.200,2.800,99.500,0.500,87.41,672,0.954,bicubic
regnetz_e8,97.200,2.800,99.500,0.500,57.70,320,1.000,bicubic
vit_base_r50_s16_384,97.180,2.820,99.560,0.440,98.95,384,1.000,bicubic
tf_efficientnetv2_m,97.140,2.860,99.410,0.590,54.14,480,1.000,bicubic
xcit_small_24_p16_384_dist,97.130,2.870,99.450,0.550,47.67,384,1.000,bicubic
xcit_small_24_p16_384_dist,97.120,2.880,99.460,0.540,47.67,384,1.000,bicubic
tf_efficientnet_b8_ap,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic
eca_nfnet_l2,97.090,2.910,99.510,0.490,56.72,384,1.000,bicubic
ecaresnet269d,97.090,2.910,99.470,0.530,102.09,352,1.000,bicubic
cait_s24_384,97.080,2.920,99.430,0.570,47.06,384,1.000,bicubic
beit_base_patch16_224,97.090,2.910,99.610,0.390,86.53,224,0.900,bicubic
ecaresnet269d,97.080,2.920,99.470,0.530,102.09,352,1.000,bicubic
tf_efficientnet_b6_ap,97.080,2.920,99.620,0.380,43.04,528,0.942,bicubic
beit_base_patch16_224,97.080,2.920,99.610,0.390,86.53,224,0.900,bicubic
xcit_large_24_p8_224_dist,97.060,2.940,99.420,0.580,188.93,224,1.000,bicubic
resnetv2_152x2_bitm,97.030,2.970,99.590,0.410,236.34,448,1.000,bilinear
dm_nfnet_f2,97.030,2.970,99.440,0.560,193.78,352,0.920,bicubic
resnetv2_101x3_bitm,97.020,2.980,99.490,0.510,387.93,448,1.000,bilinear
cait_s24_384,97.070,2.930,99.430,0.570,47.06,384,1.000,bicubic
xcit_large_24_p8_224_dist,97.070,2.930,99.420,0.580,188.93,224,1.000,bicubic
dm_nfnet_f2,97.020,2.980,99.440,0.560,193.78,352,0.920,bicubic
resnetv2_152x2_bitm,97.010,2.990,99.590,0.410,236.34,448,1.000,bilinear
tf_efficientnet_b7,97.010,2.990,99.520,0.480,66.35,600,0.949,bicubic
efficientnetv2_rw_m,96.990,3.010,99.530,0.470,53.24,416,1.000,bicubic
deit_base_distilled_patch16_384,96.970,3.030,99.490,0.510,87.63,384,1.000,bicubic
xcit_small_12_p16_384_dist,96.940,3.060,99.400,0.600,26.25,384,1.000,bicubic
tf_efficientnet_b4_ns,96.930,3.070,99.580,0.420,19.34,380,0.922,bicubic
xcit_medium_24_p8_224_dist,96.930,3.070,99.390,0.610,84.32,224,1.000,bicubic
resnetrs420,96.910,3.090,99.460,0.540,191.89,416,1.000,bicubic
dm_nfnet_f1,96.890,3.110,99.410,0.590,132.63,320,0.910,bicubic
vit_base_patch16_224,96.870,3.130,99.530,0.470,86.57,224,0.900,bicubic
resnetv2_101x3_bitm,96.990,3.010,99.490,0.510,387.93,448,1.000,bilinear
efficientnetv2_rw_m,96.980,3.020,99.530,0.470,53.24,416,1.000,bicubic
deit_base_distilled_patch16_384,96.960,3.040,99.480,0.520,87.63,384,1.000,bicubic
tf_efficientnet_b4_ns,96.950,3.050,99.580,0.420,19.34,380,0.922,bicubic
xcit_small_12_p16_384_dist,96.930,3.070,99.400,0.600,26.25,384,1.000,bicubic
resnetrs420,96.920,3.080,99.460,0.540,191.89,416,1.000,bicubic
xcit_medium_24_p8_224_dist,96.920,3.080,99.390,0.610,84.32,224,1.000,bicubic
dm_nfnet_f1,96.910,3.090,99.410,0.590,132.63,320,0.910,bicubic
vit_base_patch16_224,96.880,3.120,99.530,0.470,86.57,224,0.900,bicubic
xcit_small_24_p8_224_dist,96.870,3.130,99.480,0.520,47.63,224,1.000,bicubic
ig_resnext101_32x16d,96.820,3.180,99.600,0.400,194.03,224,0.875,bilinear
resnetv2_152x2_bit_teacher_384,96.810,3.190,99.450,0.550,236.34,384,1.000,bicubic
resnetv2_152x2_bit_teacher_384,96.830,3.170,99.450,0.550,236.34,384,1.000,bicubic
ig_resnext101_32x16d,96.810,3.190,99.600,0.400,194.03,224,0.875,bilinear
xcit_large_24_p16_224_dist,96.800,3.200,99.350,0.650,189.10,224,1.000,bicubic
vit_large_r50_s32_224,96.790,3.210,99.350,0.650,328.99,224,0.900,bicubic
convnext_large,96.770,3.230,99.310,0.690,197.77,224,0.875,bicubic
seresnet152d,96.770,3.230,99.450,0.550,66.84,320,1.000,bicubic
resnetrs350,96.760,3.240,99.360,0.640,163.96,384,1.000,bicubic
resnet200d,96.740,3.260,99.340,0.660,64.69,320,1.000,bicubic
tf_efficientnetv2_s_in21ft1k,96.740,3.260,99.420,0.580,21.46,384,1.000,bicubic
resnetv2_50x3_bitm,96.730,3.270,99.540,0.460,217.32,448,1.000,bilinear
resnetrs350,96.760,3.240,99.370,0.630,163.96,384,1.000,bicubic
resnet200d,96.720,3.280,99.330,0.670,64.69,320,1.000,bicubic
tf_efficientnetv2_s_in21ft1k,96.720,3.280,99.420,0.580,21.46,384,1.000,bicubic
resnetv2_50x3_bitm,96.710,3.290,99.550,0.450,217.32,448,1.000,bilinear
eca_nfnet_l1,96.700,3.300,99.290,0.710,41.41,320,1.000,bicubic
xcit_small_12_p8_224_dist,96.700,3.300,99.390,0.610,26.21,224,1.000,bicubic
resnetrs270,96.690,3.310,99.350,0.650,129.86,352,1.000,bicubic
vit_small_patch16_384,96.690,3.310,99.480,0.520,22.20,384,1.000,bicubic
tf_efficientnet_b5_ap,96.680,3.320,99.460,0.540,30.39,456,0.934,bicubic
vit_small_r26_s32_384,96.680,3.320,99.570,0.430,36.47,384,1.000,bicubic
pit_b_distilled_224,96.670,3.330,99.350,0.650,74.79,224,0.900,bicubic
tf_efficientnet_b6,96.670,3.330,99.370,0.630,43.04,528,0.942,bicubic
resnest200e,96.620,3.380,99.350,0.650,70.20,320,0.909,bicubic
tf_efficientnet_b5_ap,96.670,3.330,99.460,0.540,30.39,456,0.934,bicubic
pit_b_distilled_224,96.670,3.330,99.350,0.650,74.79,224,0.900,bicubic
regnetz_d8,96.620,3.380,99.450,0.550,23.37,320,1.000,bicubic
resmlp_big_24_224_in22ft1k,96.620,3.380,99.510,0.490,129.14,224,0.875,bicubic
xcit_medium_24_p16_224_dist,96.600,3.400,99.270,0.730,84.40,224,1.000,bicubic
resnest200e,96.610,3.390,99.350,0.650,70.20,320,0.909,bicubic
regnetz_d32,96.600,3.400,99.380,0.620,27.58,320,0.950,bicubic
swsl_resnext101_32x16d,96.600,3.400,99.530,0.470,194.03,224,0.875,bilinear
regnetz_d,96.590,3.410,99.380,0.620,27.58,320,0.950,bicubic
resnetrs152,96.570,3.430,99.240,0.760,86.62,320,1.000,bicubic
cait_xs24_384,96.560,3.440,99.420,0.580,26.67,384,1.000,bicubic
xcit_tiny_24_p8_384_dist,96.550,3.450,99.320,0.680,12.11,384,1.000,bicubic
xcit_medium_24_p16_224_dist,96.590,3.410,99.270,0.730,84.40,224,1.000,bicubic
resnetrs152,96.580,3.420,99.240,0.760,86.62,320,1.000,bicubic
cait_xs24_384,96.550,3.450,99.420,0.580,26.67,384,1.000,bicubic
efficientnetv2_rw_s,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic
xcit_tiny_24_p8_384_dist,96.540,3.460,99.320,0.680,12.11,384,1.000,bicubic
crossvit_18_dagger_408,96.530,3.470,99.260,0.740,44.61,408,1.000,bicubic
resnetrs200,96.530,3.470,99.350,0.650,93.21,320,1.000,bicubic
resnest269e,96.520,3.480,99.350,0.650,110.93,416,0.928,bicubic
resnetrs200,96.520,3.480,99.350,0.650,93.21,320,1.000,bicubic
vit_base_patch32_384,96.500,3.500,99.410,0.590,88.30,384,1.000,bicubic
resmlp_big_24_distilled_224,96.470,3.530,99.310,0.690,129.14,224,0.875,bicubic
vit_base_patch16_224_miil,96.440,3.560,99.310,0.690,86.54,224,0.875,bilinear
swsl_resnext101_32x4d,96.420,3.580,99.470,0.530,44.18,224,0.875,bilinear
xcit_small_24_p8_224,96.400,3.600,99.140,0.860,47.63,224,1.000,bicubic
xcit_large_24_p8_224,96.400,3.600,98.980,1.020,188.93,224,1.000,bicubic
cait_s24_224,96.390,3.610,99.150,0.850,46.92,224,1.000,bicubic
vit_base_patch32_384,96.490,3.510,99.410,0.590,88.30,384,1.000,bicubic
resmlp_big_24_distilled_224,96.450,3.550,99.310,0.690,129.14,224,0.875,bicubic
vit_base_patch16_224_miil,96.440,3.560,99.300,0.700,86.54,224,0.875,bilinear
convnext_base,96.440,3.560,99.230,0.770,88.59,224,0.875,bicubic
swsl_resnext101_32x4d,96.440,3.560,99.470,0.530,44.18,224,0.875,bilinear
xcit_large_24_p8_224,96.410,3.590,98.980,1.020,188.93,224,1.000,bicubic
xcit_small_24_p8_224,96.400,3.600,99.150,0.850,47.63,224,1.000,bicubic
crossvit_15_dagger_408,96.390,3.610,99.160,0.840,28.50,408,1.000,bicubic
tf_efficientnet_b3_ns,96.370,3.630,99.350,0.650,12.23,300,0.904,bicubic
resnet152d,96.350,3.650,99.390,0.610,60.21,320,1.000,bicubic
regnety_160,96.340,3.660,99.330,0.670,83.59,288,1.000,bicubic
tf_efficientnet_b5,96.340,3.660,99.310,0.690,30.39,456,0.934,bicubic
tf_efficientnet_b3_ns,96.390,3.610,99.350,0.650,12.23,300,0.904,bicubic
cait_s24_224,96.380,3.620,99.150,0.850,46.92,224,1.000,bicubic
resnet152d,96.360,3.640,99.390,0.610,60.21,320,1.000,bicubic
regnety_160,96.350,3.650,99.330,0.670,83.59,288,1.000,bicubic
tf_efficientnet_b5,96.350,3.650,99.310,0.690,30.39,456,0.934,bicubic
tf_efficientnetv2_s,96.340,3.660,99.200,0.800,21.46,384,1.000,bicubic
ig_resnext101_32x8d,96.310,3.690,99.430,0.570,88.79,224,0.875,bilinear
resnet101d,96.300,3.700,99.230,0.770,44.57,320,1.000,bicubic
twins_svt_large,96.250,3.750,99.170,0.830,99.27,224,0.900,bicubic
twins_svt_large,96.270,3.730,99.170,0.830,99.27,224,0.900,bicubic
jx_nest_base,96.240,3.760,99.210,0.790,67.72,224,0.875,bicubic
xcit_small_24_p16_224_dist,96.210,3.790,99.210,0.790,47.67,224,1.000,bicubic
tf_efficientnet_b4_ap,96.170,3.830,99.280,0.720,19.34,380,0.922,bicubic
efficientnet_b4,96.160,3.840,99.200,0.800,19.34,384,1.000,bicubic
convnext_small,96.170,3.830,99.110,0.890,50.22,224,0.875,bicubic
twins_svt_base,96.160,3.840,99.060,0.940,56.07,224,0.900,bicubic
twins_pcpvt_large,96.150,3.850,99.190,0.810,60.99,224,0.900,bicubic
deit_base_patch16_384,96.150,3.850,99.140,0.860,86.86,384,1.000,bicubic
dm_nfnet_f0,96.140,3.860,99.250,0.750,71.49,256,0.900,bicubic
nfnet_l0,96.130,3.870,99.240,0.760,35.07,288,1.000,bicubic
resnetv2_50x1_bit_distilled,96.110,3.890,99.280,0.720,25.55,224,0.875,bicubic
dm_nfnet_f0,96.150,3.850,99.250,0.750,71.49,256,0.900,bicubic
efficientnet_b4,96.150,3.850,99.190,0.810,19.34,384,1.000,bicubic
twins_pcpvt_large,96.150,3.850,99.180,0.820,60.99,224,0.900,bicubic
nfnet_l0,96.120,3.880,99.240,0.760,35.07,288,1.000,bicubic
resnetv2_50x1_bit_distilled,96.120,3.880,99.280,0.720,25.55,224,0.875,bicubic
xcit_medium_24_p8_224,96.110,3.890,98.890,1.110,84.32,224,1.000,bicubic
xcit_small_12_p8_224,96.110,3.890,99.160,0.840,26.21,224,1.000,bicubic
deit_base_distilled_patch16_224,96.100,3.900,99.190,0.810,87.34,224,0.900,bicubic
resnetv2_101x1_bitm,96.100,3.900,99.280,0.720,44.54,448,1.000,bilinear
resnetv2_152x2_bit_teacher,96.090,3.910,99.270,0.730,236.34,224,0.875,bicubic
xcit_tiny_12_p8_384_dist,96.060,3.940,99.140,0.860,6.71,384,1.000,bicubic
xcit_small_12_p16_224_dist,96.030,3.970,99.130,0.870,26.25,224,1.000,bicubic
regnety_032,95.980,4.020,99.190,0.810,19.44,288,1.000,bicubic
jx_nest_small,95.970,4.030,99.040,0.960,38.35,224,0.875,bicubic
tresnet_xl_448,95.970,4.030,99.120,0.880,78.44,448,0.875,bilinear
eca_nfnet_l0,95.960,4.040,99.210,0.790,24.14,288,1.000,bicubic
xcit_tiny_24_p16_384_dist,95.920,4.080,99.220,0.780,12.12,384,1.000,bicubic
tf_efficientnet_b4,95.890,4.110,99.170,0.830,19.34,380,0.922,bicubic
resnet51q,95.880,4.120,99.120,0.880,35.70,288,1.000,bilinear
swsl_resnext50_32x4d,95.880,4.120,99.250,0.750,25.03,224,0.875,bilinear
swin_small_patch4_window7_224,95.880,4.120,99.020,0.980,49.61,224,0.900,bicubic
tresnet_l_448,95.870,4.130,99.120,0.880,55.99,448,0.875,bilinear
cait_xxs36_384,95.860,4.140,99.090,0.910,17.37,384,1.000,bicubic
resnetv2_152x2_bit_teacher,96.100,3.900,99.270,0.730,236.34,224,0.875,bicubic
deit_base_distilled_patch16_224,96.090,3.910,99.190,0.810,87.34,224,0.900,bicubic
xcit_tiny_12_p8_384_dist,96.080,3.920,99.140,0.860,6.71,384,1.000,bicubic
xcit_small_12_p16_224_dist,96.030,3.970,99.140,0.860,26.25,224,1.000,bicubic
tresnet_xl_448,95.970,4.030,99.130,0.870,78.44,448,0.875,bilinear
regnety_032,95.970,4.030,99.190,0.810,19.44,288,1.000,bicubic
jx_nest_small,95.970,4.030,99.030,0.970,38.35,224,0.875,bicubic
eca_nfnet_l0,95.950,4.050,99.210,0.790,24.14,288,1.000,bicubic
xcit_tiny_24_p16_384_dist,95.930,4.070,99.220,0.780,12.12,384,1.000,bicubic
swin_small_patch4_window7_224,95.910,4.090,99.020,0.980,49.61,224,0.900,bicubic
resnet152,95.900,4.100,99.080,0.920,60.19,224,0.950,bicubic
tf_efficientnet_b4,95.900,4.100,99.170,0.830,19.34,380,0.922,bicubic
resnet51q,95.870,4.130,99.120,0.880,35.70,288,1.000,bilinear
tresnet_l_448,95.860,4.140,99.120,0.880,55.99,448,0.875,bilinear
swsl_resnext50_32x4d,95.860,4.140,99.250,0.750,25.03,224,0.875,bilinear
resnest101e,95.860,4.140,99.210,0.790,48.28,256,0.875,bilinear
cait_xxs36_384,95.850,4.150,99.090,0.910,17.37,384,1.000,bicubic
vit_large_patch32_384,95.830,4.170,99.150,0.850,306.63,384,1.000,bicubic
xcit_tiny_24_p8_224_dist,95.820,4.180,99.210,0.790,12.11,224,1.000,bicubic
regnetz_c,95.800,4.200,99.100,0.900,13.46,320,0.940,bicubic
ssl_resnext101_32x16d,95.800,4.200,99.180,0.820,194.03,224,0.875,bilinear
xcit_tiny_24_p8_224_dist,95.810,4.190,99.210,0.790,12.11,224,1.000,bicubic
regnetz_c16,95.800,4.200,99.100,0.900,13.46,320,0.940,bicubic
ssl_resnext101_32x16d,95.790,4.210,99.180,0.820,194.03,224,0.875,bilinear
twins_pcpvt_base,95.790,4.210,99.130,0.870,43.83,224,0.900,bicubic
resnet61q,95.790,4.210,98.990,1.010,36.85,288,1.000,bicubic
tf_efficientnet_b2_ns,95.750,4.250,99.120,0.880,9.11,260,0.890,bicubic
resnet61q,95.780,4.220,98.990,1.010,36.85,288,1.000,bicubic
tf_efficientnet_b2_ns,95.770,4.230,99.120,0.880,9.11,260,0.890,bicubic
gc_efficientnetv2_rw_t,95.740,4.260,99.020,0.980,13.68,288,1.000,bicubic
tresnet_m,95.730,4.270,99.030,0.970,31.39,224,0.875,bilinear
pnasnet5large,95.720,4.280,98.920,1.080,86.06,331,0.911,bicubic
efficientnet_b3,95.720,4.280,99.040,0.960,12.23,320,1.000,bicubic
tresnet_m,95.720,4.280,99.030,0.970,31.39,224,0.875,bilinear
efficientnet_b3,95.710,4.290,99.040,0.960,12.23,320,1.000,bicubic
pnasnet5large,95.710,4.290,98.920,1.080,86.06,331,0.911,bicubic
crossvit_15_dagger_240,95.690,4.310,98.830,1.170,28.21,240,0.875,bicubic
nasnetalarge,95.680,4.320,98.930,1.070,88.75,331,0.911,bicubic
xcit_tiny_24_p8_224,95.670,4.330,99.050,0.950,12.11,224,1.000,bicubic
crossvit_15_dagger_240,95.670,4.330,98.820,1.180,28.21,240,0.875,bicubic
vit_small_r26_s32_224,95.640,4.360,99.190,0.810,36.43,224,0.900,bicubic
pit_b_224,95.630,4.370,98.670,1.330,73.76,224,0.900,bicubic
pit_b_224,95.640,4.360,98.670,1.330,73.76,224,0.900,bicubic
resnetv2_101,95.630,4.370,98.990,1.010,44.54,224,0.950,bicubic
efficientnetv2_rw_t,95.610,4.390,99.070,0.930,13.65,288,1.000,bicubic
crossvit_18_dagger_240,95.560,4.440,99.060,0.940,44.27,240,0.875,bicubic
convit_base,95.550,4.450,98.890,1.110,86.54,224,0.875,bicubic
ecaresnet101d,95.540,4.460,99.130,0.870,44.57,224,0.875,bicubic
coat_lite_small,95.530,4.470,98.860,1.140,19.84,224,0.900,bicubic
crossvit_18_dagger_240,95.570,4.430,99.060,0.940,44.27,240,0.875,bicubic
convit_base,95.550,4.450,98.870,1.130,86.54,224,0.875,bicubic
convnext_tiny,95.550,4.450,99.020,0.980,28.59,224,0.875,bicubic
coat_lite_small,95.540,4.460,98.860,1.140,19.84,224,0.900,bicubic
xcit_medium_24_p16_224,95.530,4.470,98.740,1.260,84.40,224,1.000,bicubic
xcit_small_24_p16_224,95.530,4.470,98.760,1.240,47.67,224,1.000,bicubic
ecaresnet101d,95.530,4.470,99.130,0.870,44.57,224,0.875,bicubic
levit_384,95.530,4.470,99.050,0.950,39.13,224,0.900,bicubic
xcit_small_24_p16_224,95.530,4.470,98.770,1.230,47.67,224,1.000,bicubic
xcit_medium_24_p16_224,95.520,4.480,98.770,1.230,84.40,224,1.000,bicubic
crossvit_base_240,95.520,4.480,98.820,1.180,105.03,240,0.875,bicubic
visformer_small,95.500,4.500,98.900,1.100,40.22,224,0.900,bicubic
ecaresnet50t,95.500,4.500,99.120,0.880,25.57,320,0.950,bicubic
ssl_resnext101_32x8d,95.470,4.530,99.120,0.880,88.79,224,0.875,bilinear
crossvit_18_240,95.450,4.550,98.790,1.210,43.27,240,0.875,bicubic
ecaresnet50t,95.510,4.490,99.120,0.880,25.57,320,0.950,bicubic
fbnetv3_g,95.510,4.490,98.990,1.010,16.62,288,0.950,bilinear
ssl_resnext101_32x8d,95.490,4.510,99.120,0.880,88.79,224,0.875,bilinear
visformer_small,95.480,4.520,98.900,1.100,40.22,224,0.900,bicubic
crossvit_18_240,95.440,4.560,98.790,1.210,43.27,240,0.875,bicubic
deit_base_patch16_224,95.440,4.560,98.840,1.160,86.57,224,0.900,bicubic
tresnet_xl,95.440,4.560,99.060,0.940,78.44,224,0.875,bilinear
ssl_resnext101_32x4d,95.430,4.570,99.130,0.870,44.18,224,0.875,bilinear
resnetrs101,95.420,4.580,99.030,0.970,63.62,288,0.940,bicubic
xcit_large_24_p16_224,95.410,4.590,98.630,1.370,189.10,224,1.000,bicubic
ssl_resnext101_32x4d,95.440,4.560,99.130,0.870,44.18,224,0.875,bilinear
tresnet_xl,95.440,4.560,99.050,0.950,78.44,224,0.875,bilinear
resnetrs101,95.430,4.570,99.030,0.970,63.62,288,0.940,bicubic
halo2botnet50ts_256,95.420,4.580,99.010,0.990,22.64,256,0.950,bicubic
xcit_large_24_p16_224,95.420,4.580,98.620,1.380,189.10,224,1.000,bicubic
xcit_small_12_p16_224,95.420,4.580,98.840,1.160,26.25,224,1.000,bicubic
swsl_resnet50,95.400,4.600,99.290,0.710,25.56,224,0.875,bilinear
xcit_small_12_p16_224,95.400,4.600,98.830,1.170,26.25,224,1.000,bicubic
vit_small_patch16_224,95.370,4.630,99.150,0.850,22.05,224,0.900,bicubic
resnet101,95.360,4.640,98.860,1.140,44.55,224,0.950,bicubic
tf_efficientnet_b3_ap,95.320,4.680,98.900,1.100,12.23,300,0.904,bicubic
mixer_b16_224_miil,95.310,4.690,98.890,1.110,59.88,224,0.875,bilinear
mixer_b16_224_miil,95.300,4.700,98.880,1.120,59.88,224,0.875,bilinear
tresnet_l,95.290,4.710,99.010,0.990,55.99,224,0.875,bilinear
cait_xxs24_384,95.270,4.730,98.960,1.040,12.03,384,1.000,bicubic
cait_xxs24_384,95.280,4.720,98.960,1.040,12.03,384,1.000,bicubic
jx_nest_tiny,95.250,4.750,98.980,1.020,17.06,224,0.875,bicubic
pit_s_distilled_224,95.240,4.760,99.050,0.950,24.04,224,0.900,bicubic
twins_pcpvt_small,95.230,4.770,98.880,1.120,24.11,224,0.900,bicubic
twins_svt_small,95.210,4.790,98.890,1.110,24.06,224,0.900,bicubic
convit_small,95.180,4.820,98.920,1.080,27.78,224,0.875,bicubic
twins_pcpvt_small,95.210,4.790,98.880,1.120,24.11,224,0.900,bicubic
convit_small,95.200,4.800,98.900,1.100,27.78,224,0.875,bicubic
twins_svt_small,95.200,4.800,98.880,1.120,24.06,224,0.900,bicubic
tf_efficientnet_b1_ns,95.170,4.830,99.120,0.880,7.79,240,0.882,bicubic
tf_efficientnetv2_b3,95.170,4.830,98.820,1.180,14.36,300,0.904,bicubic
halonet50ts,95.160,4.840,98.770,1.230,22.73,256,0.940,bicubic
tf_efficientnetv2_b3,95.160,4.840,98.820,1.180,14.36,300,0.904,bicubic
lamhalobotnet50ts_256,95.150,4.850,98.880,1.120,22.57,256,0.950,bicubic
crossvit_15_240,95.140,4.860,98.930,1.070,27.53,240,0.875,bicubic
xcit_tiny_12_p16_384_dist,95.130,4.870,99.020,0.980,6.72,384,1.000,bicubic
swin_tiny_patch4_window7_224,95.130,4.870,98.850,1.150,28.29,224,0.900,bicubic
crossvit_15_240,95.120,4.880,98.930,1.070,27.53,240,0.875,bicubic
efficientnet_el,95.120,4.880,98.980,1.020,10.59,300,0.904,bicubic
gernet_l,95.110,4.890,98.900,1.100,31.08,256,0.875,bilinear
xcit_tiny_12_p16_384_dist,95.110,4.890,99.020,0.980,6.72,384,1.000,bicubic
convmixer_1536_20,95.080,4.920,99.030,0.970,51.63,224,0.960,bicubic
xcit_tiny_12_p8_224_dist,95.080,4.920,98.910,1.090,6.71,224,1.000,bicubic
ecaresnet101d_pruned,95.070,4.930,98.980,1.020,24.88,224,0.875,bicubic
vit_small_patch32_384,95.060,4.940,98.990,1.010,22.92,384,1.000,bicubic
legacy_senet154,95.060,4.940,98.830,1.170,115.09,224,0.875,bilinear
regnetz_b,95.060,4.940,99.050,0.950,9.72,288,0.940,bicubic
gluon_resnet152_v1s,95.050,4.950,98.930,1.070,60.32,224,0.875,bicubic
halonet50ts,95.050,4.950,98.590,1.410,22.73,256,0.940,bicubic
wide_resnet50_2,95.050,4.950,98.970,1.030,68.88,224,0.875,bicubic
gernet_l,95.100,4.900,98.900,1.100,31.08,256,0.875,bilinear
xcit_tiny_12_p8_224_dist,95.090,4.910,98.910,1.090,6.71,224,1.000,bicubic
ecaresnet101d_pruned,95.080,4.920,98.980,1.020,24.88,224,0.875,bicubic
legacy_senet154,95.070,4.930,98.830,1.170,115.09,224,0.875,bilinear
regnetz_b16,95.070,4.930,99.050,0.950,9.72,288,0.940,bicubic
wide_resnet50_2,95.070,4.930,98.970,1.030,68.88,224,0.875,bicubic
convmixer_1536_20,95.060,4.940,99.030,0.970,51.63,224,0.960,bicubic
vit_small_patch32_384,95.050,4.950,98.990,1.010,22.92,384,1.000,bicubic
gluon_resnet152_v1s,95.040,4.960,98.930,1.070,60.32,224,0.875,bicubic
tnt_s_patch16_224,95.040,4.960,98.840,1.160,23.76,224,0.900,bicubic
seresnext50_32x4d,95.030,4.970,98.890,1.110,27.56,224,0.875,bicubic
levit_256,95.030,4.970,98.890,1.110,18.89,224,0.900,bicubic
seresnext50_32x4d,95.030,4.970,98.880,1.120,27.56,224,0.875,bicubic
levit_256,95.020,4.980,98.890,1.110,18.89,224,0.900,bicubic
tf_efficientnet_b3,95.020,4.980,98.910,1.090,12.23,300,0.904,bicubic
resnetv2_50x1_bitm,95.020,4.980,99.050,0.950,25.55,448,1.000,bilinear
vit_base_patch32_224,95.010,4.990,99.020,0.980,88.22,224,0.900,bicubic
coat_mini,94.990,5.010,98.780,1.220,10.34,224,0.900,bicubic
resnetv2_50x1_bitm,95.010,4.990,99.060,0.940,25.55,448,1.000,bilinear
vit_base_patch32_224,95.000,5.000,99.030,0.970,88.22,224,0.900,bicubic
tresnet_m_448,94.990,5.010,98.980,1.020,31.39,448,0.875,bilinear
resnest50d_4s2x40d,94.950,5.050,99.070,0.930,30.42,224,0.875,bicubic
rexnet_200,94.940,5.060,99.000,1.000,16.37,224,0.875,bicubic
gluon_seresnext101_64x4d,94.940,5.060,98.820,1.180,88.23,224,0.875,bicubic
gluon_senet154,94.930,5.070,98.770,1.230,115.09,224,0.875,bicubic
coat_mini,94.970,5.030,98.780,1.220,10.34,224,0.900,bicubic
resnest50d_4s2x40d,94.960,5.040,99.070,0.930,30.42,224,0.875,bicubic
rexnet_200,94.950,5.050,99.010,0.990,16.37,224,0.875,bicubic
gluon_senet154,94.920,5.080,98.760,1.240,115.09,224,0.875,bicubic
gluon_seresnext101_32x4d,94.920,5.080,98.810,1.190,48.96,224,0.875,bicubic
seresnet33ts,94.870,5.130,98.790,1.210,19.78,256,0.900,bicubic
resmlp_36_distilled_224,94.870,5.130,98.860,1.140,44.69,224,0.875,bicubic
gluon_seresnext101_64x4d,94.920,5.080,98.830,1.170,88.23,224,0.875,bicubic
tf_efficientnet_lite4,94.890,5.110,99.020,0.980,13.01,380,0.920,bilinear
resmlp_36_distilled_224,94.880,5.120,98.840,1.160,44.69,224,0.875,bicubic
ssl_resnext50_32x4d,94.870,5.130,98.890,1.110,25.03,224,0.875,bilinear
gcresnet50t,94.860,5.140,98.800,1.200,25.90,256,0.900,bicubic
ssl_resnext50_32x4d,94.860,5.140,98.870,1.130,25.03,224,0.875,bilinear
tf_efficientnet_lite4,94.860,5.140,99.020,0.980,13.01,380,0.920,bilinear
resnest50d,94.850,5.150,98.880,1.120,27.48,224,0.875,bilinear
seresnet33ts,94.850,5.150,98.790,1.210,19.78,256,0.900,bicubic
crossvit_small_240,94.830,5.170,99.020,0.980,26.86,240,0.875,bicubic
resnest50d,94.830,5.170,98.880,1.120,27.48,224,0.875,bilinear
lamhalobotnet50ts_256,94.800,5.200,98.550,1.450,22.57,256,0.950,bicubic
sehalonet33ts,94.780,5.220,98.570,1.430,13.69,256,0.940,bicubic
resnest50d_1s4x24d,94.770,5.230,98.980,1.020,25.68,224,0.875,bicubic
lambda_resnet50ts,94.790,5.210,98.460,1.540,21.54,256,0.950,bicubic
ecaresnetlight,94.770,5.230,98.800,1.200,30.16,224,0.875,bicubic
lambda_resnet50ts,94.770,5.230,98.470,1.530,21.54,256,0.950,bicubic
halo2botnet50ts_256,94.760,5.240,98.660,1.340,22.64,256,0.950,bicubic
gluon_resnet152_v1d,94.750,5.250,98.740,1.260,60.21,224,0.875,bicubic
xcit_tiny_12_p8_224,94.710,5.290,98.830,1.170,6.71,224,1.000,bicubic
sehalonet33ts,94.770,5.230,98.570,1.430,13.69,256,0.940,bicubic
resnest50d_1s4x24d,94.750,5.250,98.980,1.020,25.68,224,0.875,bicubic
gluon_resnet152_v1d,94.740,5.260,98.740,1.260,60.21,224,0.875,bicubic
gluon_resnet101_v1s,94.720,5.280,98.820,1.180,44.67,224,0.875,bicubic
deit_small_distilled_patch16_224,94.710,5.290,99.030,0.970,22.44,224,0.900,bicubic
haloregnetz_b,94.710,5.290,98.660,1.340,11.68,224,0.940,bicubic
gluon_resnet101_v1s,94.700,5.300,98.820,1.180,44.67,224,0.875,bicubic
cspdarknet53,94.670,5.330,98.810,1.190,27.64,256,0.887,bilinear
resmlp_big_24_224,94.650,5.350,98.490,1.510,129.14,224,0.875,bicubic
gluon_resnext101_64x4d,94.640,5.360,98.670,1.330,83.46,224,0.875,bicubic
efficientnet_b2,94.630,5.370,98.710,1.290,9.11,288,1.000,bicubic
ecaresnet50d,94.620,5.380,98.890,1.110,25.58,224,0.875,bicubic
efficientnet_b3_pruned,94.620,5.380,98.770,1.230,9.86,300,0.904,bicubic
haloregnetz_b,94.700,5.300,98.660,1.340,11.68,224,0.940,bicubic
xcit_tiny_12_p8_224,94.690,5.310,98.830,1.170,6.71,224,1.000,bicubic
resmlp_big_24_224,94.670,5.330,98.480,1.520,129.14,224,0.875,bicubic
cspdarknet53,94.660,5.340,98.800,1.200,27.64,256,0.887,bilinear
gluon_resnext101_64x4d,94.660,5.340,98.650,1.350,83.46,224,0.875,bicubic
efficientnet_b3_pruned,94.630,5.370,98.760,1.240,9.86,300,0.904,bicubic
ecaresnet50d,94.630,5.370,98.890,1.110,25.58,224,0.875,bicubic
gernet_m,94.620,5.380,98.860,1.140,21.14,224,0.875,bilinear
pit_s_224,94.580,5.420,98.720,1.280,23.46,224,0.900,bicubic
repvgg_b3,94.570,5.430,98.790,1.210,123.09,224,0.875,bilinear
nf_resnet50,94.560,5.440,98.790,1.210,25.56,288,0.940,bicubic
seresnet50,94.560,5.440,98.750,1.250,28.09,224,0.875,bicubic
regnety_320,94.550,5.450,98.850,1.150,145.05,224,0.875,bicubic
inception_resnet_v2,94.540,5.460,98.790,1.210,55.84,299,0.897,bicubic
efficientnet_b2,94.610,5.390,98.710,1.290,9.11,288,1.000,bicubic
pit_s_224,94.590,5.410,98.700,1.300,23.46,224,0.900,bicubic
repvgg_b3,94.570,5.430,98.780,1.220,123.09,224,0.875,bilinear
sebotnet33ts_256,94.570,5.430,98.500,1.500,13.70,256,0.940,bicubic
nf_resnet50,94.550,5.450,98.790,1.210,25.56,288,0.940,bicubic
seresnet50,94.550,5.450,98.750,1.250,28.09,224,0.875,bicubic
regnety_320,94.540,5.460,98.850,1.150,145.05,224,0.875,bicubic
resnext50_32x4d,94.540,5.460,98.610,1.390,25.03,224,0.950,bicubic
gluon_resnext101_32x4d,94.540,5.460,98.630,1.370,44.18,224,0.875,bicubic
repvgg_b3g4,94.530,5.470,98.960,1.040,83.83,224,0.875,bilinear
xcit_tiny_24_p16_224_dist,94.520,5.480,98.790,1.210,12.12,224,1.000,bicubic
convmixer_768_32,94.490,5.510,98.850,1.150,21.11,224,0.960,bicubic
inception_resnet_v2,94.540,5.460,98.790,1.210,55.84,299,0.897,bicubic
xcit_tiny_24_p16_224_dist,94.530,5.470,98.780,1.220,12.12,224,1.000,bicubic
repvgg_b3g4,94.520,5.480,98.970,1.030,83.83,224,0.875,bilinear
convmixer_768_32,94.500,5.500,98.850,1.150,21.11,224,0.960,bicubic
gcresnext50ts,94.490,5.510,98.670,1.330,15.67,256,0.900,bicubic
tf_efficientnet_b2_ap,94.490,5.510,98.620,1.380,9.11,260,0.890,bicubic
regnety_120,94.480,5.520,98.810,1.190,51.82,224,0.875,bicubic
gcresnext50ts,94.480,5.520,98.670,1.330,15.67,256,0.900,bicubic
gcresnet33ts,94.480,5.520,98.780,1.220,19.88,256,0.900,bicubic
rexnet_150,94.480,5.520,98.790,1.210,9.73,224,0.875,bicubic
cspresnext50,94.470,5.530,98.680,1.320,20.57,224,0.875,bilinear
ssl_resnet50,94.470,5.530,98.920,1.080,25.56,224,0.875,bilinear
rexnet_150,94.470,5.530,98.790,1.210,9.73,224,0.875,bicubic
resmlp_24_distilled_224,94.450,5.550,98.770,1.230,30.02,224,0.875,bicubic
resnetv2_50,94.440,5.560,98.730,1.270,25.55,224,0.950,bicubic
regnetx_320,94.440,5.560,98.730,1.270,107.81,224,0.875,bicubic
tf_efficientnetv2_b2,94.410,5.590,98.570,1.430,10.10,260,0.890,bicubic
deit_small_patch16_224,94.400,5.600,98.690,1.310,22.05,224,0.900,bicubic
gcresnet33ts,94.470,5.530,98.770,1.230,19.88,256,0.900,bicubic
regnetx_320,94.460,5.540,98.740,1.260,107.81,224,0.875,bicubic
resmlp_24_distilled_224,94.460,5.540,98.770,1.230,30.02,224,0.875,bicubic
ssl_resnet50,94.450,5.550,98.920,1.080,25.56,224,0.875,bilinear
resnetv2_50,94.440,5.560,98.740,1.260,25.55,224,0.950,bicubic
tf_efficientnetv2_b2,94.420,5.580,98.570,1.430,10.10,260,0.890,bicubic
efficientnet_el_pruned,94.400,5.600,98.740,1.260,10.59,300,0.904,bicubic
tf_efficientnet_el,94.400,5.600,98.710,1.290,10.59,300,0.904,bicubic
efficientnet_el_pruned,94.390,5.610,98.750,1.250,10.59,300,0.904,bicubic
inception_v4,94.370,5.630,98.580,1.420,42.68,299,0.875,bicubic
tf_efficientnet_b2,94.370,5.630,98.610,1.390,9.11,260,0.890,bicubic
legacy_seresnext101_32x4d,94.350,5.650,98.630,1.370,48.96,224,0.875,bilinear
deit_small_patch16_224,94.390,5.610,98.690,1.310,22.05,224,0.900,bicubic
inception_v4,94.380,5.620,98.580,1.420,42.68,299,0.875,bicubic
legacy_seresnext101_32x4d,94.360,5.640,98.650,1.350,48.96,224,0.875,bilinear
resnet50_gn,94.360,5.640,98.710,1.290,25.56,224,0.940,bicubic
tf_efficientnet_b2,94.360,5.640,98.610,1.390,9.11,260,0.890,bicubic
gluon_seresnext50_32x4d,94.330,5.670,98.610,1.390,27.56,224,0.875,bicubic
resnet50,94.330,5.670,98.440,1.560,25.56,224,0.950,bicubic
gluon_seresnext50_32x4d,94.330,5.670,98.620,1.380,27.56,224,0.875,bicubic
ecaresnet26t,94.300,5.700,98.710,1.290,16.01,320,0.950,bicubic
ecaresnet26t,94.310,5.690,98.720,1.280,16.01,320,0.950,bicubic
dpn107,94.300,5.700,98.470,1.530,86.92,224,0.875,bicubic
xception71,94.290,5.710,98.640,1.360,42.34,299,0.903,bicubic
resnetrs50,94.290,5.710,98.640,1.360,35.69,224,0.910,bicubic
resnet50d,94.270,5.730,98.720,1.280,25.58,224,0.875,bicubic
resnetrs50,94.300,5.700,98.640,1.360,35.69,224,0.910,bicubic
xception71,94.280,5.720,98.640,1.360,42.34,299,0.903,bicubic
cait_xxs36_224,94.260,5.740,98.720,1.280,17.30,224,1.000,bicubic
gluon_xception65,94.260,5.740,98.570,1.430,39.92,299,0.903,bicubic
cait_xxs36_224,94.260,5.740,98.710,1.290,17.30,224,1.000,bicubic
skresnext50_32x4d,94.260,5.740,98.470,1.530,27.48,224,0.875,bicubic
regnetx_120,94.240,5.760,98.670,1.330,46.11,224,0.875,bicubic
dpn92,94.220,5.780,98.730,1.270,37.67,224,0.875,bicubic
ecaresnet50d_pruned,94.210,5.790,98.730,1.270,19.94,224,0.875,bicubic
mixnet_xl,94.200,5.800,98.340,1.660,11.90,224,0.875,bicubic
eca_resnet33ts,94.200,5.800,98.770,1.230,19.68,256,0.900,bicubic
gluon_resnet101_v1d,94.200,5.800,98.570,1.430,44.57,224,0.875,bicubic
resmlp_36_224,94.190,5.810,98.660,1.340,44.69,224,0.875,bicubic
resnext50d_32x4d,94.190,5.810,98.570,1.430,25.05,224,0.875,bicubic
tf_efficientnet_lite3,94.190,5.810,98.640,1.360,8.20,300,0.904,bilinear
levit_192,94.180,5.820,98.560,1.440,10.95,224,0.900,bicubic
regnety_080,94.180,5.820,98.680,1.320,39.18,224,0.875,bicubic
ens_adv_inception_resnet_v2,94.160,5.840,98.610,1.390,55.84,299,0.897,bicubic
resnet50d,94.260,5.740,98.720,1.280,25.58,224,0.875,bicubic
skresnext50_32x4d,94.260,5.740,98.460,1.540,27.48,224,0.875,bicubic
regnetx_120,94.240,5.760,98.650,1.350,46.11,224,0.875,bicubic
gluon_resnet101_v1d,94.240,5.760,98.560,1.440,44.57,224,0.875,bicubic
dpn92,94.230,5.770,98.730,1.270,37.67,224,0.875,bicubic
ecaresnet50d_pruned,94.220,5.780,98.730,1.270,19.94,224,0.875,bicubic
resmlp_36_224,94.200,5.800,98.660,1.340,44.69,224,0.875,bicubic
tf_efficientnet_lite3,94.200,5.800,98.640,1.360,8.20,300,0.904,bilinear
eca_resnet33ts,94.190,5.810,98.770,1.230,19.68,256,0.900,bicubic
mixnet_xl,94.190,5.810,98.340,1.660,11.90,224,0.875,bicubic
resnext50d_32x4d,94.180,5.820,98.570,1.430,25.05,224,0.875,bicubic
regnety_080,94.170,5.830,98.680,1.320,39.18,224,0.875,bicubic
levit_192,94.170,5.830,98.550,1.450,10.95,224,0.900,bicubic
ens_adv_inception_resnet_v2,94.160,5.840,98.600,1.400,55.84,299,0.897,bicubic
gluon_resnet152_v1c,94.160,5.840,98.640,1.360,60.21,224,0.875,bicubic
gmlp_s16_224,94.150,5.850,98.500,1.500,19.42,224,0.875,bicubic
regnety_064,94.150,5.850,98.740,1.260,30.58,224,0.875,bicubic
vit_base_patch16_sam_224,94.150,5.850,98.670,1.330,86.57,224,0.900,bicubic
efficientnet_b2_pruned,94.140,5.860,98.520,1.480,8.31,260,0.890,bicubic
dpn98,94.120,5.880,98.580,1.420,61.57,224,0.875,bicubic
efficientnet_b2_pruned,94.140,5.860,98.530,1.470,8.31,260,0.890,bicubic
regnety_064,94.140,5.860,98.730,1.270,30.58,224,0.875,bicubic
vit_base_patch16_224_sam,94.140,5.860,98.670,1.330,86.57,224,0.900,bicubic
regnetx_160,94.120,5.880,98.740,1.260,54.28,224,0.875,bicubic
nf_regnet_b1,94.120,5.880,98.620,1.380,10.22,288,0.900,bicubic
resnext50_32x4d,94.110,5.890,98.350,1.650,25.03,224,0.875,bicubic
nf_regnet_b1,94.120,5.880,98.630,1.370,10.22,288,0.900,bicubic
dpn98,94.110,5.890,98.580,1.420,61.57,224,0.875,bicubic
ese_vovnet39b,94.090,5.910,98.660,1.340,24.57,224,0.875,bicubic
gluon_resnet152_v1b,94.080,5.920,98.460,1.540,60.19,224,0.875,bicubic
xcit_tiny_24_p16_224,94.080,5.920,98.510,1.490,12.12,224,1.000,bicubic
coat_lite_mini,94.050,5.950,98.540,1.460,11.01,224,0.900,bicubic
xcit_tiny_24_p16_224,94.070,5.930,98.510,1.490,12.12,224,1.000,bicubic
coat_lite_mini,94.060,5.940,98.550,1.450,11.01,224,0.900,bicubic
eca_halonext26ts,94.050,5.950,98.500,1.500,10.76,256,0.940,bicubic
halonet26t,94.020,5.980,98.500,1.500,12.48,256,0.950,bicubic
resmlp_24_224,94.020,5.980,98.330,1.670,30.02,224,0.875,bicubic
hrnet_w64,94.010,5.990,98.620,1.380,128.06,224,0.875,bilinear
dpn131,93.990,6.010,98.720,1.280,79.25,224,0.875,bicubic
hrnet_w64,93.990,6.010,98.620,1.380,128.06,224,0.875,bilinear
halonet26t,93.980,6.020,98.490,1.510,12.48,256,0.950,bicubic
dla102x2,93.960,6.040,98.480,1.520,41.28,224,0.875,bilinear
hrnet_w48,93.940,6.060,98.610,1.390,77.47,224,0.875,bilinear
fbnetv3_b,93.960,6.040,98.630,1.370,8.60,256,0.950,bilinear
resnetblur50,93.950,6.050,98.580,1.420,25.56,224,0.875,bicubic
tf_efficientnetv2_b1,93.940,6.060,98.620,1.380,8.14,240,0.882,bicubic
resnetblur50,93.930,6.070,98.580,1.420,25.56,224,0.875,bicubic
tf_efficientnet_cc_b1_8e,93.920,6.080,98.250,1.750,39.72,240,0.882,bicubic
fbnetv3_d,93.930,6.070,98.740,1.260,10.31,256,0.950,bilinear
hrnet_w48,93.920,6.080,98.610,1.390,77.47,224,0.875,bilinear
tf_efficientnet_cc_b1_8e,93.910,6.090,98.260,1.740,39.72,240,0.882,bicubic
rexnet_130,93.900,6.100,98.400,1.600,7.56,224,0.875,bicubic
regnetx_064,93.890,6.110,98.630,1.370,26.21,224,0.875,bicubic
regnetx_080,93.870,6.130,98.520,1.480,39.57,224,0.875,bicubic
regnety_040,93.860,6.140,98.640,1.360,20.65,224,0.875,bicubic
repvgg_b2g4,93.840,6.160,98.600,1.400,61.76,224,0.875,bilinear
efficientnet_em,93.840,6.160,98.810,1.190,6.90,240,0.882,bicubic
gluon_resnext50_32x4d,93.820,6.180,98.410,1.590,25.03,224,0.875,bicubic
lambda_resnet26t,93.820,6.180,98.650,1.350,10.96,256,0.940,bicubic
regnety_040,93.860,6.140,98.650,1.350,20.65,224,0.875,bicubic
repvgg_b2g4,93.840,6.160,98.590,1.410,61.76,224,0.875,bilinear
efficientnet_em,93.830,6.170,98.810,1.190,6.90,240,0.882,bicubic
lambda_resnet26t,93.830,6.170,98.650,1.350,10.96,256,0.940,bicubic
pit_xs_distilled_224,93.820,6.180,98.670,1.330,11.00,224,0.900,bicubic
eca_botnext26ts_256,93.790,6.210,98.500,1.500,10.59,256,0.950,bicubic
resnext101_32x8d,93.790,6.210,98.580,1.420,88.79,224,0.875,bilinear
gluon_resnet50_v1d,93.780,6.220,98.400,1.600,25.58,224,0.875,bicubic
xception65,93.770,6.230,98.360,1.640,39.92,299,0.903,bicubic
cspresnet50,93.750,6.250,98.630,1.370,21.62,256,0.887,bilinear
gluon_resnet101_v1b,93.730,6.270,98.400,1.600,44.55,224,0.875,bicubic
resnext101_32x8d,93.820,6.180,98.580,1.420,88.79,224,0.875,bilinear
gluon_resnext50_32x4d,93.810,6.190,98.410,1.590,25.03,224,0.875,bicubic
eca_botnext26ts_256,93.780,6.220,98.500,1.500,10.59,256,0.950,bicubic
gluon_resnet50_v1d,93.770,6.230,98.390,1.610,25.58,224,0.875,bicubic
xception65,93.760,6.240,98.370,1.630,39.92,299,0.903,bicubic
gluon_resnet101_v1b,93.750,6.250,98.380,1.620,44.55,224,0.875,bicubic
res2net101_26w_4s,93.750,6.250,98.310,1.690,45.21,224,0.875,bilinear
cspresnet50,93.740,6.260,98.640,1.360,21.62,256,0.887,bilinear
legacy_seresnext50_32x4d,93.730,6.270,98.580,1.420,27.56,224,0.875,bilinear
wide_resnet101_2,93.720,6.280,98.540,1.460,126.89,224,0.875,bilinear
res2net101_26w_4s,93.720,6.280,98.320,1.680,45.21,224,0.875,bilinear
legacy_seresnext50_32x4d,93.720,6.280,98.580,1.420,27.56,224,0.875,bilinear
lambda_resnet26rpt_256,93.720,6.280,98.500,1.500,10.99,256,0.940,bicubic
tf_efficientnet_b1_ap,93.710,6.290,98.360,1.640,7.79,240,0.882,bicubic
dpn68b,93.680,6.320,98.530,1.470,12.61,224,0.875,bicubic
gluon_resnet101_v1c,93.660,6.340,98.410,1.590,44.57,224,0.875,bicubic
lambda_resnet26rpt_256,93.710,6.290,98.510,1.490,10.99,256,0.940,bicubic
dpn68b,93.690,6.310,98.520,1.480,12.61,224,0.875,bicubic
tf_efficientnet_b1_ap,93.690,6.310,98.360,1.640,7.79,240,0.882,bicubic
gluon_resnet101_v1c,93.660,6.340,98.420,1.580,44.57,224,0.875,bicubic
vit_tiny_patch16_384,93.650,6.350,98.600,1.400,5.79,384,1.000,bicubic
gluon_resnet50_v1s,93.630,6.370,98.470,1.530,25.68,224,0.875,bicubic
tf_efficientnet_b0_ns,93.620,6.380,98.640,1.360,5.29,224,0.875,bicubic
resnet33ts,93.600,6.400,98.530,1.470,19.68,256,0.900,bicubic
cait_xxs24_224,93.590,6.410,98.440,1.560,11.96,224,1.000,bicubic
hrnet_w44,93.580,6.420,98.700,1.300,67.06,224,0.875,bilinear
coat_tiny,93.580,6.420,98.410,1.590,5.50,224,0.900,bicubic
regnetx_040,93.550,6.450,98.560,1.440,22.12,224,0.875,bicubic
hrnet_w32,93.520,6.480,98.440,1.560,41.23,224,0.875,bilinear
eca_halonext26ts,93.510,6.490,98.280,1.720,10.76,256,0.940,bicubic
tf_efficientnet_b1,93.510,6.490,98.360,1.640,7.79,240,0.882,bicubic
dla102x,93.510,6.490,98.500,1.500,26.31,224,0.875,bilinear
tf_efficientnet_b0_ns,93.630,6.370,98.640,1.360,5.29,224,0.875,bicubic
gluon_resnet50_v1s,93.620,6.380,98.460,1.540,25.68,224,0.875,bicubic
cait_xxs24_224,93.600,6.400,98.440,1.560,11.96,224,1.000,bicubic
resnet33ts,93.600,6.400,98.540,1.460,19.68,256,0.900,bicubic
coat_tiny,93.590,6.410,98.420,1.580,5.50,224,0.900,bicubic
hrnet_w44,93.550,6.450,98.700,1.300,67.06,224,0.875,bilinear
regnetx_040,93.550,6.450,98.550,1.450,22.12,224,0.875,bicubic
hrnet_w32,93.530,6.470,98.460,1.540,41.23,224,0.875,bilinear
dla102x,93.520,6.480,98.510,1.490,26.31,224,0.875,bilinear
xcit_nano_12_p8_384_dist,93.520,6.480,98.540,1.460,3.05,384,1.000,bicubic
tf_efficientnet_b1,93.500,6.500,98.360,1.640,7.79,240,0.882,bicubic
botnet26t_256,93.500,6.500,98.300,1.700,12.49,256,0.950,bicubic
repvgg_b2,93.490,6.510,98.730,1.270,89.02,224,0.875,bilinear
hrnet_w40,93.490,6.510,98.590,1.410,57.56,224,0.875,bilinear
xcit_nano_12_p8_384_dist,93.480,6.520,98.520,1.480,3.05,384,1.000,bicubic
xception,93.480,6.520,98.530,1.470,22.86,299,0.897,bicubic
hrnet_w40,93.490,6.510,98.580,1.420,57.56,224,0.875,bilinear
resnet32ts,93.470,6.530,98.490,1.510,17.96,256,0.900,bicubic
gluon_inception_v3,93.460,6.540,98.560,1.440,23.83,299,0.875,bicubic
res2net50_26w_8s,93.430,6.570,98.180,1.820,48.40,224,0.875,bilinear
mixnet_l,93.430,6.570,98.220,1.780,7.33,224,0.875,bicubic
legacy_seresnet152,93.420,6.580,98.340,1.660,66.82,224,0.875,bilinear
xception41,93.410,6.590,98.420,1.580,26.97,299,0.903,bicubic
xception,93.470,6.530,98.530,1.470,22.86,299,0.897,bicubic
gluon_inception_v3,93.460,6.540,98.570,1.430,23.83,299,0.875,bicubic
mixnet_l,93.450,6.550,98.220,1.780,7.33,224,0.875,bicubic
xception41,93.430,6.570,98.430,1.570,26.97,299,0.903,bicubic
res2net50_26w_8s,93.410,6.590,98.180,1.820,48.40,224,0.875,bilinear
res2net50_26w_6s,93.410,6.590,98.280,1.720,37.05,224,0.875,bilinear
legacy_seresnet152,93.400,6.600,98.340,1.660,66.82,224,0.875,bilinear
xcit_tiny_12_p16_224_dist,93.400,6.600,98.490,1.510,6.72,224,1.000,bicubic
res2net50_26w_6s,93.400,6.600,98.280,1.720,37.05,224,0.875,bilinear
resnest26d,93.360,6.640,98.640,1.360,17.07,224,0.875,bilinear
levit_128,93.340,6.660,98.380,1.620,9.21,224,0.900,bicubic
dla169,93.340,6.660,98.600,1.400,53.39,224,0.875,bilinear
dla169,93.340,6.660,98.590,1.410,53.39,224,0.875,bilinear
bat_resnext26ts,93.330,6.670,98.350,1.650,10.73,256,0.900,bicubic
levit_128,93.330,6.670,98.380,1.620,9.21,224,0.900,bicubic
repvgg_b1,93.330,6.670,98.510,1.490,57.42,224,0.875,bilinear
tf_inception_v3,93.330,6.670,98.040,1.960,23.83,299,0.875,bicubic
tv_resnet152,93.330,6.670,98.390,1.610,60.19,224,0.875,bilinear
tf_mixnet_l,93.310,6.690,98.030,1.970,7.33,224,0.875,bicubic
bat_resnext26ts,93.310,6.690,98.350,1.650,10.73,256,0.900,bicubic
legacy_seresnet101,93.300,6.700,98.500,1.500,49.33,224,0.875,bilinear
selecsls60b,93.290,6.710,98.280,1.720,32.77,224,0.875,bicubic
efficientnet_b1,93.240,6.760,98.290,1.710,7.79,256,1.000,bicubic
coat_lite_tiny,93.220,6.780,98.270,1.730,5.72,224,0.900,bicubic
efficientnet_es,93.200,6.800,98.400,1.600,5.44,224,0.875,bicubic
hrnet_w30,93.200,6.800,98.410,1.590,37.71,224,0.875,bilinear
tf_mixnet_l,93.320,6.680,98.030,1.970,7.33,224,0.875,bicubic
resnest26d,93.320,6.680,98.630,1.370,17.07,224,0.875,bilinear
tf_inception_v3,93.320,6.680,98.030,1.970,23.83,299,0.875,bicubic
tv_resnet152,93.310,6.690,98.390,1.610,60.19,224,0.875,bilinear
legacy_seresnet101,93.300,6.700,98.510,1.490,49.33,224,0.875,bilinear
selecsls60b,93.300,6.700,98.280,1.720,32.77,224,0.875,bicubic
efficientnet_b1,93.250,6.750,98.290,1.710,7.79,256,1.000,bicubic
coat_lite_tiny,93.230,6.770,98.260,1.740,5.72,224,0.900,bicubic
hrnet_w30,93.190,6.810,98.410,1.590,37.71,224,0.875,bilinear
dla60_res2net,93.180,6.820,98.420,1.580,20.85,224,0.875,bilinear
dla60_res2next,93.180,6.820,98.410,1.590,17.03,224,0.875,bilinear
dla60_res2net,93.160,6.840,98.410,1.590,20.85,224,0.875,bilinear
efficientnet_es,93.140,6.860,98.420,1.580,5.44,224,0.875,bicubic
dla60x,93.120,6.880,98.510,1.490,17.35,224,0.875,bilinear
regnetx_032,93.120,6.880,98.390,1.610,15.30,224,0.875,bicubic
pit_xs_224,93.120,6.880,98.320,1.680,10.62,224,0.900,bicubic
pit_xs_224,93.110,6.890,98.320,1.680,10.62,224,0.900,bicubic
tf_efficientnetv2_b0,93.110,6.890,98.390,1.610,7.14,224,0.875,bicubic
dla60x,93.090,6.910,98.490,1.510,17.35,224,0.875,bilinear
dla102,93.080,6.920,98.540,1.460,33.27,224,0.875,bilinear
gluon_resnet50_v1c,93.030,6.970,98.370,1.630,25.58,224,0.875,bicubic
regnety_016,93.030,6.970,98.360,1.640,11.20,224,0.875,bicubic
dla102,93.060,6.940,98.550,1.450,33.27,224,0.875,bilinear
rexnet_100,93.030,6.970,98.190,1.810,4.80,224,0.875,bicubic
regnety_016,93.030,6.970,98.360,1.640,11.20,224,0.875,bicubic
gluon_resnet50_v1c,93.030,6.970,98.390,1.610,25.58,224,0.875,bicubic
selecsls60,93.020,6.980,98.310,1.690,30.67,224,0.875,bicubic
repvgg_b1g4,93.000,7.000,98.430,1.570,39.97,224,0.875,bilinear
legacy_seresnet50,92.950,7.050,98.190,1.810,28.09,224,0.875,bilinear
repvgg_b1g4,92.980,7.020,98.430,1.570,39.97,224,0.875,bilinear
legacy_seresnet50,92.960,7.040,98.190,1.810,28.09,224,0.875,bilinear
hardcorenas_f,92.950,7.050,98.160,1.840,8.20,224,0.875,bilinear
tf_efficientnet_em,92.950,7.050,98.210,1.790,6.90,240,0.882,bicubic
crossvit_9_dagger_240,92.920,7.080,98.250,1.750,8.78,240,0.875,bicubic
adv_inception_v3,92.890,7.110,98.130,1.870,23.83,299,0.875,bicubic
res2next50,92.850,7.150,98.180,1.820,24.67,224,0.875,bilinear
gmixer_24_224,92.840,7.160,97.880,2.120,24.72,224,0.875,bicubic
tf_efficientnet_em,92.930,7.070,98.200,1.800,6.90,240,0.882,bicubic
adv_inception_v3,92.890,7.110,98.140,1.860,23.83,299,0.875,bicubic
crossvit_9_dagger_240,92.890,7.110,98.230,1.770,8.78,240,0.875,bicubic
res2next50,92.860,7.140,98.190,1.810,24.67,224,0.875,bilinear
gmixer_24_224,92.830,7.170,97.880,2.120,24.72,224,0.875,bicubic
resmlp_12_distilled_224,92.830,7.170,98.140,1.860,15.35,224,0.875,bicubic
tf_efficientnet_cc_b0_8e,92.820,7.180,98.180,1.820,24.01,224,0.875,bicubic
seresnext26t_32x4d,92.810,7.190,98.370,1.630,16.81,224,0.875,bicubic
tv_resnet101,92.810,7.190,98.230,1.770,44.55,224,0.875,bilinear
tf_efficientnet_cc_b0_8e,92.830,7.170,98.180,1.820,24.01,224,0.875,bicubic
seresnext26t_32x4d,92.820,7.180,98.370,1.630,16.81,224,0.875,bicubic
tv_resnet101,92.820,7.180,98.250,1.750,44.55,224,0.875,bilinear
gcresnext26ts,92.780,7.220,98.260,1.740,10.48,256,0.900,bicubic
efficientnet_b1_pruned,92.770,7.230,98.040,1.960,6.33,240,0.882,bicubic
gcresnext26ts,92.770,7.230,98.270,1.730,10.48,256,0.900,bicubic
tv_resnext50_32x4d,92.760,7.240,98.280,1.720,25.03,224,0.875,bilinear
densenet201,92.750,7.250,98.240,1.760,20.01,224,0.875,bicubic
resnet26t,92.750,7.250,98.240,1.760,16.01,256,0.940,bicubic
seresnext26d_32x4d,92.740,7.260,98.150,1.850,16.81,224,0.875,bicubic
inception_v3,92.720,7.280,97.960,2.040,23.83,299,0.875,bicubic
res2net50_14w_8s,92.720,7.280,98.190,1.810,25.06,224,0.875,bilinear
resnet34d,92.700,7.300,98.300,1.700,21.82,224,0.875,bicubic
seresnext26ts,92.680,7.320,98.300,1.700,10.39,256,0.900,bicubic
efficientnet_b0,92.670,7.330,98.080,1.920,5.29,224,0.875,bicubic
eca_resnext26ts,92.660,7.340,98.260,1.740,10.30,256,0.900,bicubic
tf_efficientnet_lite2,92.660,7.340,98.230,1.770,6.09,260,0.890,bicubic
legacy_seresnext26_32x4d,92.630,7.370,98.120,1.880,16.79,224,0.875,bicubic
tf_efficientnet_cc_b0_4e,92.620,7.380,98.080,1.920,13.31,224,0.875,bicubic
tf_efficientnet_lite1,92.620,7.380,98.080,1.920,5.42,240,0.882,bicubic
hardcorenas_e,92.580,7.420,98.110,1.890,8.07,224,0.875,bilinear
res2net50_48w_2s,92.550,7.450,98.080,1.920,25.29,224,0.875,bilinear
gluon_resnet50_v1b,92.540,7.460,98.190,1.810,25.56,224,0.875,bicubic
densenet161,92.500,7.500,98.290,1.710,28.68,224,0.875,bicubic
xcit_tiny_12_p16_224,92.490,7.510,98.250,1.750,6.72,224,1.000,bicubic
res2net50_26w_4s,92.480,7.520,98.070,1.930,25.70,224,0.875,bilinear
mixnet_m,92.440,7.560,97.870,2.130,5.01,224,0.875,bicubic
convmixer_1024_20_ks9_p14,92.430,7.570,98.270,1.730,24.38,224,0.960,bicubic
hardcorenas_d,92.420,7.580,98.070,1.930,7.50,224,0.875,bilinear
densenet201,92.750,7.250,98.230,1.770,20.01,224,0.875,bicubic
resnet26t,92.750,7.250,98.230,1.770,16.01,256,0.940,bicubic
tv_resnext50_32x4d,92.750,7.250,98.270,1.730,25.03,224,0.875,bilinear
res2net50_14w_8s,92.740,7.260,98.180,1.820,25.06,224,0.875,bilinear
inception_v3,92.720,7.280,97.970,2.030,23.83,299,0.875,bicubic
efficientnet_b0,92.690,7.310,98.070,1.930,5.29,224,0.875,bicubic
seresnext26d_32x4d,92.690,7.310,98.150,1.850,16.81,224,0.875,bicubic
seresnext26ts,92.690,7.310,98.290,1.710,10.39,256,0.900,bicubic
resnet34d,92.680,7.320,98.310,1.690,21.82,224,0.875,bicubic
tf_efficientnet_lite2,92.650,7.350,98.230,1.770,6.09,260,0.890,bicubic
legacy_seresnext26_32x4d,92.640,7.360,98.130,1.870,16.79,224,0.875,bicubic
tf_efficientnet_lite1,92.620,7.380,98.070,1.930,5.42,240,0.882,bicubic
eca_resnext26ts,92.610,7.390,98.260,1.740,10.30,256,0.900,bicubic
tf_efficientnet_cc_b0_4e,92.600,7.400,98.080,1.920,13.31,224,0.875,bicubic
hardcorenas_e,92.570,7.430,98.110,1.890,8.07,224,0.875,bilinear
gluon_resnet50_v1b,92.540,7.460,98.170,1.830,25.56,224,0.875,bicubic
res2net50_48w_2s,92.540,7.460,98.090,1.910,25.29,224,0.875,bilinear
xcit_tiny_12_p16_224,92.500,7.500,98.240,1.760,6.72,224,1.000,bicubic
densenet161,92.490,7.510,98.290,1.710,28.68,224,0.875,bicubic
res2net50_26w_4s,92.490,7.510,98.060,1.940,25.70,224,0.875,bilinear
tinynet_a,92.440,7.560,98.080,1.920,6.19,192,0.875,bicubic
mixnet_m,92.430,7.570,97.870,2.130,5.01,224,0.875,bicubic
convmixer_1024_20_ks9_p14,92.420,7.580,98.270,1.730,24.38,224,0.960,bicubic
hardcorenas_d,92.400,7.600,98.070,1.930,7.50,224,0.875,bilinear
mobilenetv2_120d,92.400,7.600,98.050,1.950,5.83,224,0.875,bicubic
skresnet34,92.380,7.620,98.140,1.860,22.28,224,0.875,bicubic
skresnet34,92.390,7.610,98.150,1.850,22.28,224,0.875,bicubic
tf_mixnet_m,92.330,7.670,97.890,2.110,5.01,224,0.875,bicubic
hrnet_w18,92.310,7.690,98.250,1.750,21.30,224,0.875,bilinear
selecsls42b,92.300,7.700,98.140,1.860,32.46,224,0.875,bicubic
mobilenetv3_large_100_miil,92.270,7.730,97.640,2.360,5.48,224,0.875,bilinear
ese_vovnet19b_dw,92.270,7.730,98.100,1.900,6.54,224,0.875,bicubic
tf_efficientnet_b0,92.230,7.770,98.000,2.000,5.29,224,0.875,bicubic
tf_efficientnet_b0_ap,92.220,7.780,98.020,1.980,5.29,224,0.875,bicubic
dla60,92.220,7.780,98.110,1.890,22.04,224,0.875,bilinear
resmlp_12_224,92.190,7.810,98.160,1.840,15.35,224,0.875,bicubic
hrnet_w18,92.320,7.680,98.250,1.750,21.30,224,0.875,bilinear
ese_vovnet19b_dw,92.280,7.720,98.090,1.910,6.54,224,0.875,bicubic
selecsls42b,92.280,7.720,98.140,1.860,32.46,224,0.875,bicubic
mobilenetv3_large_100_miil,92.260,7.740,97.640,2.360,5.48,224,0.875,bilinear
tf_efficientnet_b0,92.260,7.740,98.000,2.000,5.29,224,0.875,bicubic
dla60,92.230,7.770,98.110,1.890,22.04,224,0.875,bilinear
resmlp_12_224,92.210,7.790,98.160,1.840,15.35,224,0.875,bicubic
tf_efficientnet_b0_ap,92.200,7.800,98.020,1.980,5.29,224,0.875,bicubic
regnetx_016,92.160,7.840,98.210,1.790,9.19,224,0.875,bicubic
gernet_s,92.140,7.860,98.190,1.810,8.17,224,0.875,bilinear
xcit_nano_12_p8_224_dist,92.100,7.900,98.160,1.840,3.05,224,1.000,bicubic
resnet26d,92.050,7.950,97.960,2.040,16.01,224,0.875,bicubic
vit_small_patch32_224,92.030,7.970,98.230,1.770,22.88,224,0.900,bicubic
vit_tiny_r_s16_p8_384,92.030,7.970,98.290,1.710,6.36,384,1.000,bicubic
dpn68,92.020,7.980,98.040,1.960,12.61,224,0.875,bicubic
resnet26d,92.070,7.930,97.970,2.030,16.01,224,0.875,bicubic
vit_small_patch32_224,92.040,7.960,98.230,1.770,22.88,224,0.900,bicubic
vit_tiny_r_s16_p8_384,92.040,7.960,98.290,1.710,6.36,384,1.000,bicubic
dpn68,92.030,7.970,98.050,1.950,12.61,224,0.875,bicubic
hardcorenas_c,92.020,7.980,97.840,2.160,5.52,224,0.875,bilinear
tf_efficientnet_es,91.990,8.010,97.870,2.130,5.44,224,0.875,bicubic
levit_128s,91.950,8.050,98.060,1.940,7.78,224,0.900,bicubic
tf_efficientnet_es,91.980,8.020,97.870,2.130,5.44,224,0.875,bicubic
levit_128s,91.960,8.040,98.060,1.940,7.78,224,0.900,bicubic
repvgg_a2,91.940,8.060,98.150,1.850,28.21,224,0.875,bilinear
densenet169,91.910,8.090,98.100,1.900,14.15,224,0.875,bicubic
densenetblur121d,91.910,8.090,98.090,1.910,8.00,224,0.875,bicubic
tv_resnet50,91.890,8.110,98.040,1.960,25.56,224,0.875,bilinear
mixer_b16_224,91.860,8.140,97.230,2.770,59.88,224,0.875,bicubic
resnext26ts,91.860,8.140,97.930,2.070,10.30,256,0.900,bicubic
xcit_nano_12_p16_384_dist,91.830,8.170,98.010,1.990,3.05,384,1.000,bicubic
mobilenetv2_140,91.830,8.170,97.860,2.140,6.11,224,0.875,bicubic
mixnet_s,91.820,8.180,97.690,2.310,4.13,224,0.875,bicubic
densenet169,91.920,8.080,98.100,1.900,14.15,224,0.875,bicubic
densenetblur121d,91.910,8.090,98.070,1.930,8.00,224,0.875,bicubic
tv_resnet50,91.880,8.120,98.040,1.960,25.56,224,0.875,bilinear
resnext26ts,91.870,8.130,97.920,2.080,10.30,256,0.900,bicubic
mixer_b16_224,91.860,8.140,97.250,2.750,59.88,224,0.875,bicubic
mobilenetv2_140,91.840,8.160,97.850,2.150,6.11,224,0.875,bicubic
mixnet_s,91.830,8.170,97.690,2.310,4.13,224,0.875,bicubic
xcit_nano_12_p16_384_dist,91.830,8.170,98.020,1.980,3.05,384,1.000,bicubic
hardcorenas_b,91.780,8.220,97.780,2.220,5.18,224,0.875,bilinear
vit_tiny_patch16_224,91.760,8.240,98.040,1.960,5.72,224,0.900,bicubic
hardcorenas_b,91.740,8.260,97.780,2.220,5.18,224,0.875,bilinear
resnest14d,91.730,8.270,97.870,2.130,10.61,224,0.875,bilinear
regnety_008,91.710,8.290,98.180,1.820,6.26,224,0.875,bicubic
densenet121,91.570,8.430,98.030,1.970,7.98,224,0.875,bicubic
tf_mixnet_s,91.510,8.490,97.610,2.390,4.13,224,0.875,bicubic
repvgg_b0,91.450,8.550,97.980,2.020,15.82,224,0.875,bilinear
regnety_008,91.720,8.280,98.180,1.820,6.26,224,0.875,bicubic
resnest14d,91.720,8.280,97.870,2.130,10.61,224,0.875,bilinear
densenet121,91.580,8.420,98.030,1.970,7.98,224,0.875,bicubic
tf_mixnet_s,91.510,8.490,97.620,2.380,4.13,224,0.875,bicubic
repvgg_b0,91.420,8.580,97.990,2.010,15.82,224,0.875,bilinear
regnety_006,91.380,8.620,97.710,2.290,6.06,224,0.875,bicubic
mobilenetv3_large_100,91.340,8.660,97.710,2.290,5.48,224,0.875,bicubic
hardcorenas_a,91.340,8.660,97.860,2.140,5.26,224,0.875,bilinear
semnasnet_100,91.280,8.720,97.570,2.430,3.89,224,0.875,bicubic
tf_mobilenetv3_large_100,91.230,8.770,97.660,2.340,5.48,224,0.875,bilinear
hardcorenas_a,91.350,8.650,97.860,2.140,5.26,224,0.875,bilinear
mobilenetv3_large_100,91.330,8.670,97.710,2.290,5.48,224,0.875,bicubic
semnasnet_100,91.270,8.730,97.560,2.440,3.89,224,0.875,bicubic
tf_mobilenetv3_large_100,91.240,8.760,97.660,2.340,5.48,224,0.875,bilinear
mobilenetv3_rw,91.210,8.790,97.660,2.340,5.48,224,0.875,bicubic
efficientnet_es_pruned,91.190,8.810,97.740,2.260,5.44,224,0.875,bicubic
hrnet_w18_small_v2,91.190,8.810,97.900,2.100,15.60,224,0.875,bilinear
efficientnet_lite0,91.140,8.860,97.630,2.370,4.65,224,0.875,bicubic
resnet26,91.130,8.870,97.740,2.260,16.00,224,0.875,bicubic
resnet34,91.120,8.880,97.630,2.370,21.80,224,0.875,bilinear
efficientnet_es_pruned,91.180,8.820,97.750,2.250,5.44,224,0.875,bicubic
efficientnet_lite0,91.130,8.870,97.620,2.380,4.65,224,0.875,bicubic
resnet34,91.130,8.870,97.620,2.380,21.80,224,0.875,bilinear
resnet26,91.120,8.880,97.750,2.250,16.00,224,0.875,bicubic
regnetx_008,91.050,8.950,97.710,2.290,7.26,224,0.875,bicubic
tf_efficientnet_lite0,91.050,8.950,97.570,2.430,4.65,224,0.875,bicubic
xcit_nano_12_p8_224,90.990,9.010,97.800,2.200,3.05,224,1.000,bicubic
gluon_resnet34_v1b,90.990,9.010,97.650,2.350,21.80,224,0.875,bicubic
mobilenetv2_110d,90.970,9.030,97.560,2.440,4.52,224,0.875,bicubic
tf_efficientnet_lite0,91.050,8.950,97.590,2.410,4.65,224,0.875,bicubic
xcit_nano_12_p8_224,91.010,8.990,97.800,2.200,3.05,224,1.000,bicubic
gluon_resnet34_v1b,90.960,9.040,97.640,2.360,21.80,224,0.875,bicubic
mobilenetv2_110d,90.960,9.040,97.560,2.440,4.52,224,0.875,bicubic
tinynet_b,90.930,9.070,97.670,2.330,3.73,188,0.875,bicubic
legacy_seresnet34,90.900,9.100,97.580,2.420,21.96,224,0.875,bilinear
tv_densenet121,90.900,9.100,97.700,2.300,7.98,224,0.875,bicubic
pit_ti_distilled_224,90.880,9.120,97.720,2.280,5.10,224,0.900,bicubic
dla34,90.770,9.230,97.650,2.350,15.74,224,0.875,bilinear
deit_tiny_distilled_patch16_224,90.730,9.270,97.580,2.420,5.91,224,0.900,bicubic
fbnetc_100,90.720,9.280,97.210,2.790,5.57,224,0.875,bilinear
swsl_resnet18,90.680,9.320,97.710,2.290,11.69,224,0.875,bilinear
crossvit_9_240,90.660,9.340,97.740,2.260,8.55,240,0.875,bicubic
convit_tiny,90.610,9.390,97.730,2.270,5.71,224,0.875,bicubic
pit_ti_distilled_224,90.900,9.100,97.710,2.290,5.10,224,0.900,bicubic
tv_densenet121,90.890,9.110,97.710,2.290,7.98,224,0.875,bicubic
dla34,90.770,9.230,97.660,2.340,15.74,224,0.875,bilinear
deit_tiny_distilled_patch16_224,90.710,9.290,97.570,2.430,5.91,224,0.900,bicubic
fbnetc_100,90.710,9.290,97.210,2.790,5.57,224,0.875,bilinear
swsl_resnet18,90.690,9.310,97.700,2.300,11.69,224,0.875,bilinear
crossvit_9_240,90.640,9.360,97.740,2.260,8.55,240,0.875,bicubic
convit_tiny,90.640,9.360,97.740,2.260,5.71,224,0.875,bicubic
mnasnet_100,90.510,9.490,97.470,2.530,4.38,224,0.875,bicubic
regnety_004,90.480,9.520,97.560,2.440,4.34,224,0.875,bicubic
regnety_004,90.490,9.510,97.540,2.460,4.34,224,0.875,bicubic
regnetx_006,90.350,9.650,97.430,2.570,6.20,224,0.875,bicubic
spnasnet_100,90.350,9.650,97.190,2.810,4.42,224,0.875,bilinear
regnetx_006,90.320,9.680,97.430,2.570,6.20,224,0.875,bicubic
crossvit_tiny_240,90.250,9.750,97.610,2.390,7.01,240,0.875,bicubic
ssl_resnet18,90.230,9.770,97.560,2.440,11.69,224,0.875,bilinear
vgg19_bn,90.100,9.900,97.580,2.420,143.68,224,0.875,bilinear
crossvit_tiny_240,90.250,9.750,97.590,2.410,7.01,240,0.875,bicubic
ssl_resnet18,90.220,9.780,97.550,2.450,11.69,224,0.875,bilinear
vgg16_bn,90.090,9.910,97.370,2.630,138.37,224,0.875,bilinear
vgg19_bn,90.080,9.920,97.580,2.420,143.68,224,0.875,bilinear
semnasnet_075,90.080,9.920,97.430,2.570,2.91,224,0.875,bicubic
ghostnet_100,90.030,9.970,97.370,2.630,5.18,224,0.875,bilinear
pit_ti_224,89.930,10.070,97.440,2.560,4.85,224,0.900,bicubic
tv_resnet34,89.920,10.080,97.340,2.660,21.80,224,0.875,bilinear
vit_base_patch32_sam_224,89.750,10.250,97.000,3.000,88.22,224,0.900,bicubic
tf_mobilenetv3_large_075,89.710,10.290,97.220,2.780,3.99,224,0.875,bilinear
pit_ti_224,89.950,10.050,97.450,2.550,4.85,224,0.900,bicubic
tv_resnet34,89.930,10.070,97.340,2.660,21.80,224,0.875,bilinear
vit_base_patch32_224_sam,89.750,10.250,97.000,3.000,88.22,224,0.900,bicubic
tf_mobilenetv3_large_075,89.680,10.320,97.210,2.790,3.99,224,0.875,bilinear
xcit_nano_12_p16_224_dist,89.680,10.320,97.090,2.910,3.05,224,1.000,bicubic
deit_tiny_patch16_224,89.670,10.330,97.440,2.560,5.72,224,0.900,bicubic
skresnet18,89.660,10.340,97.220,2.780,11.96,224,0.875,bicubic
skresnet18,89.660,10.340,97.230,2.770,11.96,224,0.875,bicubic
mobilenetv2_100,89.610,10.390,97.150,2.850,3.50,224,0.875,bicubic
resnet18d,89.290,10.710,97.140,2.860,11.71,224,0.875,bicubic
vit_tiny_r_s16_p8_224,89.190,10.810,97.230,2.770,6.34,224,0.900,bicubic
vgg19,89.060,10.940,96.870,3.130,143.67,224,0.875,bilinear
hrnet_w18_small,89.060,10.940,97.100,2.900,13.19,224,0.875,bilinear
tf_mobilenetv3_large_minimal_100,88.950,11.050,96.870,3.130,3.92,224,0.875,bilinear
legacy_seresnet18,88.880,11.120,96.970,3.030,11.78,224,0.875,bicubic
regnetx_004,88.880,11.120,97.120,2.880,5.16,224,0.875,bicubic
vgg13_bn,88.780,11.220,96.970,3.030,133.05,224,0.875,bilinear
xcit_nano_12_p16_224,88.590,11.410,96.790,3.210,3.05,224,1.000,bicubic
resnet18d,89.270,10.730,97.140,2.860,11.71,224,0.875,bicubic
vit_tiny_r_s16_p8_224,89.170,10.830,97.230,2.770,6.34,224,0.900,bicubic
hrnet_w18_small,89.050,10.950,97.110,2.890,13.19,224,0.875,bilinear
vgg19,89.040,10.960,96.870,3.130,143.67,224,0.875,bilinear
tf_mobilenetv3_large_minimal_100,88.960,11.040,96.860,3.140,3.92,224,0.875,bilinear
regnetx_004,88.900,11.100,97.120,2.880,5.16,224,0.875,bicubic
legacy_seresnet18,88.880,11.120,96.980,3.020,11.78,224,0.875,bicubic
lcnet_100,88.790,11.210,96.730,3.270,2.95,224,0.875,bicubic
vgg13_bn,88.760,11.240,96.970,3.030,133.05,224,0.875,bilinear
xcit_nano_12_p16_224,88.610,11.390,96.790,3.210,3.05,224,1.000,bicubic
vgg16,88.550,11.450,96.790,3.210,138.36,224,0.875,bilinear
gluon_resnet18_v1b,88.380,11.620,96.700,3.300,11.69,224,0.875,bicubic
vgg11_bn,87.520,12.480,96.810,3.190,132.87,224,0.875,bilinear
resnet18,87.370,12.630,96.260,3.740,11.69,224,0.875,bilinear
regnety_002,87.360,12.640,96.570,3.430,3.16,224,0.875,bicubic
mixer_l16_224,87.160,12.840,93.530,6.470,208.20,224,0.875,bicubic
vgg13,87.030,12.970,96.310,3.690,133.05,224,0.875,bilinear
vgg11,86.580,13.420,96.290,3.710,132.86,224,0.875,bilinear
dla60x_c,86.280,13.720,96.160,3.840,1.32,224,0.875,bilinear
regnetx_002,86.200,13.800,95.970,4.030,2.68,224,0.875,bicubic
tf_mobilenetv3_small_100,85.210,14.790,95.780,4.220,2.54,224,0.875,bilinear
dla46x_c,84.250,15.750,95.280,4.720,1.07,224,0.875,bilinear
dla46_c,83.640,16.360,94.910,5.090,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075,83.480,16.520,94.800,5.200,2.04,224,0.875,bilinear
tf_mobilenetv3_small_minimal_100,81.390,18.610,93.670,6.330,2.04,224,0.875,bilinear
gluon_resnet18_v1b,88.400,11.600,96.680,3.320,11.69,224,0.875,bicubic
tinynet_c,87.770,12.230,96.370,3.630,2.46,184,0.875,bicubic
vgg11_bn,87.500,12.500,96.820,3.180,132.87,224,0.875,bilinear
resnet18,87.390,12.610,96.290,3.710,11.69,224,0.875,bilinear
regnety_002,87.380,12.620,96.590,3.410,3.16,224,0.875,bicubic
mixer_l16_224,87.150,12.850,93.510,6.490,208.20,224,0.875,bicubic
vgg13,87.050,12.950,96.320,3.680,133.05,224,0.875,bilinear
vgg11,86.550,13.450,96.280,3.720,132.86,224,0.875,bilinear
dla60x_c,86.290,13.710,96.160,3.840,1.32,224,0.875,bilinear
regnetx_002,86.190,13.810,95.980,4.020,2.68,224,0.875,bicubic
lcnet_075,85.990,14.010,95.680,4.320,2.36,224,0.875,bicubic
tf_mobilenetv3_small_100,85.210,14.790,95.770,4.230,2.54,224,0.875,bilinear
tinynet_d,84.750,15.250,95.180,4.820,2.34,152,0.875,bicubic
dla46x_c,84.250,15.750,95.270,4.730,1.07,224,0.875,bilinear
mnasnet_small,83.990,16.010,94.920,5.080,2.03,224,0.875,bicubic
mobilenetv2_050,83.890,16.110,94.710,5.290,1.97,224,0.875,bicubic
dla46_c,83.650,16.350,94.920,5.080,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075,83.510,16.490,94.800,5.200,2.04,224,0.875,bilinear
lcnet_050,81.780,18.220,93.710,6.290,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100,81.380,18.620,93.670,6.330,2.04,224,0.875,bilinear
tinynet_e,78.900,21.100,92.560,7.440,2.04,106,0.875,bicubic

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
2 beit_large_patch16_512 98.560 1.440 99.840 0.160 305.67 512 1.000 bicubic
3 tf_efficientnet_l2_ns 98.540 98.550 1.460 1.450 99.820 0.180 480.31 800 0.960 bicubic
4 beit_large_patch16_384 98.500 98.520 1.500 1.480 99.820 0.180 305.00 384 1.000 bicubic
5 tf_efficientnet_l2_ns_475 98.490 98.500 1.510 1.500 99.830 0.170 480.31 475 0.936 bicubic
6 convnext_xlarge_384_in22ft1k 98.350 1.650 99.800 0.200 350.20 384 1.000 bicubic
7 convnext_large_384_in22ft1k 98.220 1.780 99.730 0.270 197.77 384 1.000 bicubic
8 vit_large_patch16_384 98.210 1.790 99.800 0.200 304.72 384 1.000 bicubic
9 beit_large_patch16_224 98.170 98.180 1.830 1.820 99.760 0.240 304.43 224 0.900 bicubic
10 swin_large_patch4_window12_384 98.020 98.040 1.980 1.960 99.690 0.310 196.74 384 1.000 bicubic
11 tf_efficientnet_b7_ns convnext_base_384_in22ft1k 97.880 97.950 2.120 2.050 99.720 99.650 0.280 0.350 66.35 88.59 600 384 0.949 1.000 bicubic
12 swin_base_patch4_window12_384 convnext_xlarge_in22ft1k 97.870 97.920 2.130 2.080 99.710 99.680 0.290 0.320 87.90 350.20 384 224 1.000 0.875 bicubic
13 tf_efficientnet_b7_ns 97.910 2.090 99.720 0.280 66.35 600 0.949 bicubic
14 swin_base_patch4_window12_384 97.890 2.110 99.710 0.290 87.90 384 1.000 bicubic
15 vit_large_r50_s32_384 97.860 2.140 99.670 0.330 329.09 384 1.000 bicubic
16 beit_base_patch16_384 vit_base_patch16_384 97.830 97.840 2.170 2.160 99.700 99.670 0.300 0.330 86.74 86.86 384 1.000 bicubic
17 vit_base_patch16_384 convnext_large_in22ft1k 97.830 2.170 99.670 99.690 0.330 0.310 86.86 197.77 384 224 1.000 0.875 bicubic
18 tf_efficientnetv2_l_in21ft1k beit_base_patch16_384 97.680 97.810 2.320 2.190 99.670 99.700 0.330 0.300 118.52 86.74 480 384 1.000 bicubic
19 tf_efficientnetv2_xl_in21ft1k tf_efficientnetv2_l_in21ft1k 97.670 97.700 2.330 2.300 99.490 99.670 0.510 0.330 208.12 118.52 512 480 1.000 bicubic
20 swin_large_patch4_window7_224 97.660 97.650 2.340 2.350 99.580 0.420 196.53 224 0.900 bicubic
21 tf_efficientnetv2_xl_in21ft1k 97.650 2.350 99.490 0.510 208.12 512 1.000 bicubic
22 vit_large_patch16_224 97.640 2.360 99.590 0.410 304.33 224 0.900 bicubic
ig_resnext101_32x48d 97.640 2.360 99.710 0.290 828.41 224 0.875 bilinear
23 tf_efficientnet_b6_ns 97.630 2.370 99.580 0.420 43.04 528 0.942 bicubic
24 dm_nfnet_f6 ig_resnext101_32x48d 97.610 97.620 2.390 2.380 99.550 99.710 0.450 0.290 438.36 828.41 576 224 0.956 0.875 bicubic bilinear
25 dm_nfnet_f4 dm_nfnet_f6 97.570 97.600 2.430 2.400 99.510 99.550 0.490 0.450 316.07 438.36 512 576 0.951 0.956 bicubic
26 dm_nfnet_f4 97.580 2.420 99.510 0.490 316.07 512 0.951 bicubic
27 vit_base_patch8_224 97.580 2.420 99.670 0.330 86.58 224 0.900 bicubic
28 dm_nfnet_f5 97.540 2.460 99.570 0.430 377.21 544 0.954 bicubic
29 xcit_large_24_p8_384_dist 97.530 97.520 2.470 2.480 99.540 0.460 188.93 384 1.000 bicubic
30 xcit_large_24_p16_384_dist 97.530 97.520 2.470 2.480 99.480 0.520 189.10 384 1.000 bicubic
31 resnetv2_152x4_bitm tf_efficientnet_b5_ns 97.490 97.500 2.510 2.500 99.620 99.630 0.380 0.370 936.53 30.39 480 456 1.000 0.934 bilinear bicubic
32 tf_efficientnet_b5_ns resnetv2_152x4_bitm 97.490 2.510 99.630 99.610 0.370 0.390 30.39 936.53 456 480 0.934 1.000 bicubic bilinear
33 cait_m48_448 97.480 2.520 99.550 0.450 356.46 448 1.000 bicubic
34 tf_efficientnetv2_m_in21ft1k 97.480 2.520 99.530 0.470 54.14 480 1.000 bicubic
35 convnext_base_in22ft1k 97.470 2.530 99.610 0.390 88.59 224 0.875 bicubic
36 cait_m36_384 97.400 2.600 99.510 0.490 271.22 384 1.000 bicubic
37 ig_resnext101_32x32d 97.360 97.370 2.640 2.630 99.680 0.320 468.53 224 0.875 bilinear
38 dm_nfnet_f3 97.350 2.650 99.560 0.440 254.92 416 0.940 bicubic
39 cait_s36_384 97.340 97.330 2.660 2.670 99.530 0.470 68.37 384 1.000 bicubic
40 xcit_medium_24_p8_384_dist 97.300 97.290 2.700 2.710 99.510 0.490 84.32 384 1.000 bicubic
41 tf_efficientnetv2_l 97.280 2.720 99.550 0.450 118.52 480 1.000 bicubic
42 xcit_medium_24_p16_384_dist 97.280 2.720 99.460 0.540 84.40 384 1.000 bicubic
43 tf_efficientnetv2_l swin_base_patch4_window7_224 97.270 97.250 2.730 2.750 99.550 99.530 0.450 0.470 118.52 87.77 480 224 1.000 0.900 bicubic
44 swin_base_patch4_window7_224 xcit_small_24_p8_384_dist 97.260 97.240 2.740 2.760 99.530 99.610 0.470 0.390 87.77 47.63 224 384 0.900 1.000 bicubic
xcit_small_24_p8_384_dist 97.250 2.750 99.610 0.390 47.63 384 1.000 bicubic
45 xcit_small_12_p8_384_dist 97.230 2.770 99.480 0.520 26.21 384 1.000 bicubic
tf_efficientnet_b8 97.210 2.790 99.500 0.500 87.41 672 0.954 bicubic
46 swsl_resnext101_32x8d 97.200 2.800 99.570 0.430 88.79 224 0.875 bilinear
47 tf_efficientnet_b7_ap 97.200 2.800 99.540 0.460 66.35 600 0.949 bicubic
48 tf_efficientnet_b8 97.200 2.800 99.500 0.500 87.41 672 0.954 bicubic
49 regnetz_e8 97.200 2.800 99.500 0.500 57.70 320 1.000 bicubic
50 vit_base_r50_s16_384 97.180 2.820 99.560 0.440 98.95 384 1.000 bicubic
51 tf_efficientnetv2_m 97.140 2.860 99.410 0.590 54.14 480 1.000 bicubic
52 xcit_small_24_p16_384_dist 97.130 97.120 2.870 2.880 99.450 99.460 0.550 0.540 47.67 384 1.000 bicubic
53 tf_efficientnet_b8_ap 97.110 2.890 99.660 0.340 87.41 672 0.954 bicubic
54 eca_nfnet_l2 97.090 2.910 99.510 0.490 56.72 384 1.000 bicubic
55 ecaresnet269d beit_base_patch16_224 97.090 2.910 99.470 99.610 0.530 0.390 102.09 86.53 352 224 1.000 0.900 bicubic
56 cait_s24_384 ecaresnet269d 97.080 2.920 99.430 99.470 0.570 0.530 47.06 102.09 384 352 1.000 bicubic
57 tf_efficientnet_b6_ap 97.080 2.920 99.620 0.380 43.04 528 0.942 bicubic
58 beit_base_patch16_224 cait_s24_384 97.080 97.070 2.920 2.930 99.610 99.430 0.390 0.570 86.53 47.06 224 384 0.900 1.000 bicubic
59 xcit_large_24_p8_224_dist 97.060 97.070 2.940 2.930 99.420 0.580 188.93 224 1.000 bicubic
60 resnetv2_152x2_bitm dm_nfnet_f2 97.030 97.020 2.970 2.980 99.590 99.440 0.410 0.560 236.34 193.78 448 352 1.000 0.920 bilinear bicubic
61 dm_nfnet_f2 resnetv2_152x2_bitm 97.030 97.010 2.970 2.990 99.440 99.590 0.560 0.410 193.78 236.34 352 448 0.920 1.000 bicubic bilinear
resnetv2_101x3_bitm 97.020 2.980 99.490 0.510 387.93 448 1.000 bilinear
62 tf_efficientnet_b7 97.010 2.990 99.520 0.480 66.35 600 0.949 bicubic
63 efficientnetv2_rw_m resnetv2_101x3_bitm 96.990 3.010 99.530 99.490 0.470 0.510 53.24 387.93 416 448 1.000 bicubic bilinear
64 deit_base_distilled_patch16_384 efficientnetv2_rw_m 96.970 96.980 3.030 3.020 99.490 99.530 0.510 0.470 87.63 53.24 384 416 1.000 bicubic
65 xcit_small_12_p16_384_dist deit_base_distilled_patch16_384 96.940 96.960 3.060 3.040 99.400 99.480 0.600 0.520 26.25 87.63 384 1.000 bicubic
66 tf_efficientnet_b4_ns 96.930 96.950 3.070 3.050 99.580 0.420 19.34 380 0.922 bicubic
67 xcit_medium_24_p8_224_dist xcit_small_12_p16_384_dist 96.930 3.070 99.390 99.400 0.610 0.600 84.32 26.25 224 384 1.000 bicubic
68 resnetrs420 96.910 96.920 3.090 3.080 99.460 0.540 191.89 416 1.000 bicubic
69 dm_nfnet_f1 xcit_medium_24_p8_224_dist 96.890 96.920 3.110 3.080 99.410 99.390 0.590 0.610 132.63 84.32 320 224 0.910 1.000 bicubic
70 vit_base_patch16_224 dm_nfnet_f1 96.870 96.910 3.130 3.090 99.530 99.410 0.470 0.590 86.57 132.63 224 320 0.900 0.910 bicubic
71 vit_base_patch16_224 96.880 3.120 99.530 0.470 86.57 224 0.900 bicubic
72 xcit_small_24_p8_224_dist 96.870 3.130 99.480 0.520 47.63 224 1.000 bicubic
73 ig_resnext101_32x16d resnetv2_152x2_bit_teacher_384 96.820 96.830 3.180 3.170 99.600 99.450 0.400 0.550 194.03 236.34 224 384 0.875 1.000 bilinear bicubic
74 resnetv2_152x2_bit_teacher_384 ig_resnext101_32x16d 96.810 3.190 99.450 99.600 0.550 0.400 236.34 194.03 384 224 1.000 0.875 bicubic bilinear
75 xcit_large_24_p16_224_dist 96.800 3.200 99.350 0.650 189.10 224 1.000 bicubic
76 vit_large_r50_s32_224 96.790 3.210 99.350 0.650 328.99 224 0.900 bicubic
77 convnext_large 96.770 3.230 99.310 0.690 197.77 224 0.875 bicubic
78 seresnet152d 96.770 3.230 99.450 0.550 66.84 320 1.000 bicubic
79 resnetrs350 96.760 3.240 99.360 99.370 0.640 0.630 163.96 384 1.000 bicubic
80 resnet200d 96.740 96.720 3.260 3.280 99.340 99.330 0.660 0.670 64.69 320 1.000 bicubic
81 tf_efficientnetv2_s_in21ft1k 96.740 96.720 3.260 3.280 99.420 0.580 21.46 384 1.000 bicubic
82 resnetv2_50x3_bitm 96.730 96.710 3.270 3.290 99.540 99.550 0.460 0.450 217.32 448 1.000 bilinear
83 eca_nfnet_l1 96.700 3.300 99.290 0.710 41.41 320 1.000 bicubic
84 xcit_small_12_p8_224_dist 96.700 3.300 99.390 0.610 26.21 224 1.000 bicubic
85 resnetrs270 96.690 3.310 99.350 0.650 129.86 352 1.000 bicubic
86 vit_small_patch16_384 96.690 3.310 99.480 0.520 22.20 384 1.000 bicubic
tf_efficientnet_b5_ap 96.680 3.320 99.460 0.540 30.39 456 0.934 bicubic
87 vit_small_r26_s32_384 96.680 3.320 99.570 0.430 36.47 384 1.000 bicubic
pit_b_distilled_224 96.670 3.330 99.350 0.650 74.79 224 0.900 bicubic
88 tf_efficientnet_b6 96.670 3.330 99.370 0.630 43.04 528 0.942 bicubic
89 resnest200e tf_efficientnet_b5_ap 96.620 96.670 3.380 3.330 99.350 99.460 0.650 0.540 70.20 30.39 320 456 0.909 0.934 bicubic
90 pit_b_distilled_224 96.670 3.330 99.350 0.650 74.79 224 0.900 bicubic
91 regnetz_d8 96.620 3.380 99.450 0.550 23.37 320 1.000 bicubic
92 resmlp_big_24_224_in22ft1k 96.620 3.380 99.510 0.490 129.14 224 0.875 bicubic
93 xcit_medium_24_p16_224_dist resnest200e 96.600 96.610 3.400 3.390 99.270 99.350 0.730 0.650 84.40 70.20 224 320 1.000 0.909 bicubic
94 regnetz_d32 96.600 3.400 99.380 0.620 27.58 320 0.950 bicubic
95 swsl_resnext101_32x16d 96.600 3.400 99.530 0.470 194.03 224 0.875 bilinear
96 regnetz_d xcit_medium_24_p16_224_dist 96.590 3.410 99.380 99.270 0.620 0.730 27.58 84.40 320 224 0.950 1.000 bicubic
97 resnetrs152 96.570 96.580 3.430 3.420 99.240 0.760 86.62 320 1.000 bicubic
98 cait_xs24_384 96.560 96.550 3.440 3.450 99.420 0.580 26.67 384 1.000 bicubic
xcit_tiny_24_p8_384_dist 96.550 3.450 99.320 0.680 12.11 384 1.000 bicubic
99 efficientnetv2_rw_s 96.540 3.460 99.360 0.640 23.94 384 1.000 bicubic
100 xcit_tiny_24_p8_384_dist 96.540 3.460 99.320 0.680 12.11 384 1.000 bicubic
101 crossvit_18_dagger_408 96.530 3.470 99.260 0.740 44.61 408 1.000 bicubic
102 resnetrs200 96.530 3.470 99.350 0.650 93.21 320 1.000 bicubic
103 resnest269e 96.520 3.480 99.350 0.650 110.93 416 0.928 bicubic
104 resnetrs200 vit_base_patch32_384 96.520 96.490 3.480 3.510 99.350 99.410 0.650 0.590 93.21 88.30 320 384 1.000 bicubic
105 vit_base_patch32_384 resmlp_big_24_distilled_224 96.500 96.450 3.500 3.550 99.410 99.310 0.590 0.690 88.30 129.14 384 224 1.000 0.875 bicubic
106 resmlp_big_24_distilled_224 vit_base_patch16_224_miil 96.470 96.440 3.530 3.560 99.310 99.300 0.690 0.700 129.14 86.54 224 0.875 bicubic bilinear
107 vit_base_patch16_224_miil convnext_base 96.440 3.560 99.310 99.230 0.690 0.770 86.54 88.59 224 0.875 bilinear bicubic
108 swsl_resnext101_32x4d 96.420 96.440 3.580 3.560 99.470 0.530 44.18 224 0.875 bilinear
109 xcit_small_24_p8_224 xcit_large_24_p8_224 96.400 96.410 3.600 3.590 99.140 98.980 0.860 1.020 47.63 188.93 224 1.000 bicubic
110 xcit_large_24_p8_224 xcit_small_24_p8_224 96.400 3.600 98.980 99.150 1.020 0.850 188.93 47.63 224 1.000 bicubic
cait_s24_224 96.390 3.610 99.150 0.850 46.92 224 1.000 bicubic
111 crossvit_15_dagger_408 96.390 3.610 99.160 0.840 28.50 408 1.000 bicubic
112 tf_efficientnet_b3_ns 96.370 96.390 3.630 3.610 99.350 0.650 12.23 300 0.904 bicubic
113 resnet152d cait_s24_224 96.350 96.380 3.650 3.620 99.390 99.150 0.610 0.850 60.21 46.92 320 224 1.000 bicubic
114 regnety_160 resnet152d 96.340 96.360 3.660 3.640 99.330 99.390 0.670 0.610 83.59 60.21 288 320 1.000 bicubic
115 tf_efficientnet_b5 regnety_160 96.340 96.350 3.660 3.650 99.310 99.330 0.690 0.670 30.39 83.59 456 288 0.934 1.000 bicubic
116 tf_efficientnet_b5 96.350 3.650 99.310 0.690 30.39 456 0.934 bicubic
117 tf_efficientnetv2_s 96.340 3.660 99.200 0.800 21.46 384 1.000 bicubic
118 ig_resnext101_32x8d 96.310 3.690 99.430 0.570 88.79 224 0.875 bilinear
119 resnet101d 96.300 3.700 99.230 0.770 44.57 320 1.000 bicubic
120 twins_svt_large 96.250 96.270 3.750 3.730 99.170 0.830 99.27 224 0.900 bicubic
121 jx_nest_base 96.240 3.760 99.210 0.790 67.72 224 0.875 bicubic
122 xcit_small_24_p16_224_dist 96.210 3.790 99.210 0.790 47.67 224 1.000 bicubic
123 tf_efficientnet_b4_ap 96.170 3.830 99.280 0.720 19.34 380 0.922 bicubic
124 efficientnet_b4 convnext_small 96.160 96.170 3.840 3.830 99.200 99.110 0.800 0.890 19.34 50.22 384 224 1.000 0.875 bicubic
125 twins_svt_base 96.160 3.840 99.060 0.940 56.07 224 0.900 bicubic
twins_pcpvt_large 96.150 3.850 99.190 0.810 60.99 224 0.900 bicubic
126 deit_base_patch16_384 96.150 3.850 99.140 0.860 86.86 384 1.000 bicubic
127 dm_nfnet_f0 96.140 96.150 3.860 3.850 99.250 0.750 71.49 256 0.900 bicubic
128 nfnet_l0 efficientnet_b4 96.130 96.150 3.870 3.850 99.240 99.190 0.760 0.810 35.07 19.34 288 384 1.000 bicubic
129 resnetv2_50x1_bit_distilled twins_pcpvt_large 96.110 96.150 3.890 3.850 99.280 99.180 0.720 0.820 25.55 60.99 224 0.875 0.900 bicubic
130 nfnet_l0 96.120 3.880 99.240 0.760 35.07 288 1.000 bicubic
131 resnetv2_50x1_bit_distilled 96.120 3.880 99.280 0.720 25.55 224 0.875 bicubic
132 xcit_medium_24_p8_224 96.110 3.890 98.890 1.110 84.32 224 1.000 bicubic
133 xcit_small_12_p8_224 96.110 3.890 99.160 0.840 26.21 224 1.000 bicubic
deit_base_distilled_patch16_224 96.100 3.900 99.190 0.810 87.34 224 0.900 bicubic
134 resnetv2_101x1_bitm 96.100 3.900 99.280 0.720 44.54 448 1.000 bilinear
135 resnetv2_152x2_bit_teacher 96.090 96.100 3.910 3.900 99.270 0.730 236.34 224 0.875 bicubic
136 xcit_tiny_12_p8_384_dist deit_base_distilled_patch16_224 96.060 96.090 3.940 3.910 99.140 99.190 0.860 0.810 6.71 87.34 384 224 1.000 0.900 bicubic
137 xcit_small_12_p16_224_dist xcit_tiny_12_p8_384_dist 96.030 96.080 3.970 3.920 99.130 99.140 0.870 0.860 26.25 6.71 224 384 1.000 bicubic
138 regnety_032 xcit_small_12_p16_224_dist 95.980 96.030 4.020 3.970 99.190 99.140 0.810 0.860 19.44 26.25 288 224 1.000 bicubic
139 jx_nest_small tresnet_xl_448 95.970 4.030 99.040 99.130 0.960 0.870 38.35 78.44 224 448 0.875 bicubic bilinear
140 tresnet_xl_448 regnety_032 95.970 4.030 99.120 99.190 0.880 0.810 78.44 19.44 448 288 0.875 1.000 bilinear bicubic
141 eca_nfnet_l0 jx_nest_small 95.960 95.970 4.040 4.030 99.210 99.030 0.790 0.970 24.14 38.35 288 224 1.000 0.875 bicubic
142 xcit_tiny_24_p16_384_dist eca_nfnet_l0 95.920 95.950 4.080 4.050 99.220 99.210 0.780 0.790 12.12 24.14 384 288 1.000 bicubic
143 tf_efficientnet_b4 xcit_tiny_24_p16_384_dist 95.890 95.930 4.110 4.070 99.170 99.220 0.830 0.780 19.34 12.12 380 384 0.922 1.000 bicubic
144 resnet51q swin_small_patch4_window7_224 95.880 95.910 4.120 4.090 99.120 99.020 0.880 0.980 35.70 49.61 288 224 1.000 0.900 bilinear bicubic
145 swsl_resnext50_32x4d resnet152 95.880 95.900 4.120 4.100 99.250 99.080 0.750 0.920 25.03 60.19 224 0.875 0.950 bilinear bicubic
146 swin_small_patch4_window7_224 tf_efficientnet_b4 95.880 95.900 4.120 4.100 99.020 99.170 0.980 0.830 49.61 19.34 224 380 0.900 0.922 bicubic
147 tresnet_l_448 resnet51q 95.870 4.130 99.120 0.880 55.99 35.70 448 288 0.875 1.000 bilinear
148 cait_xxs36_384 tresnet_l_448 95.860 4.140 99.090 99.120 0.910 0.880 17.37 55.99 384 448 1.000 0.875 bicubic bilinear
149 swsl_resnext50_32x4d 95.860 4.140 99.250 0.750 25.03 224 0.875 bilinear
150 resnest101e 95.860 4.140 99.210 0.790 48.28 256 0.875 bilinear
151 cait_xxs36_384 95.850 4.150 99.090 0.910 17.37 384 1.000 bicubic
152 vit_large_patch32_384 95.830 4.170 99.150 0.850 306.63 384 1.000 bicubic
153 xcit_tiny_24_p8_224_dist 95.820 95.810 4.180 4.190 99.210 0.790 12.11 224 1.000 bicubic
154 regnetz_c regnetz_c16 95.800 4.200 99.100 0.900 13.46 320 0.940 bicubic
155 ssl_resnext101_32x16d 95.800 95.790 4.200 4.210 99.180 0.820 194.03 224 0.875 bilinear
156 twins_pcpvt_base 95.790 4.210 99.130 0.870 43.83 224 0.900 bicubic
157 resnet61q 95.790 95.780 4.210 4.220 98.990 1.010 36.85 288 1.000 bicubic
158 tf_efficientnet_b2_ns 95.750 95.770 4.250 4.230 99.120 0.880 9.11 260 0.890 bicubic
159 gc_efficientnetv2_rw_t 95.740 4.260 99.020 0.980 13.68 288 1.000 bicubic
160 tresnet_m 95.730 95.720 4.270 4.280 99.030 0.970 31.39 224 0.875 bilinear
161 pnasnet5large efficientnet_b3 95.720 95.710 4.280 4.290 98.920 99.040 1.080 0.960 86.06 12.23 331 320 0.911 1.000 bicubic
162 efficientnet_b3 pnasnet5large 95.720 95.710 4.280 4.290 99.040 98.920 0.960 1.080 12.23 86.06 320 331 1.000 0.911 bicubic
163 crossvit_15_dagger_240 95.690 4.310 98.830 1.170 28.21 240 0.875 bicubic
164 nasnetalarge 95.680 4.320 98.930 1.070 88.75 331 0.911 bicubic
165 xcit_tiny_24_p8_224 95.670 4.330 99.050 0.950 12.11 224 1.000 bicubic
crossvit_15_dagger_240 95.670 4.330 98.820 1.180 28.21 240 0.875 bicubic
166 vit_small_r26_s32_224 95.640 4.360 99.190 0.810 36.43 224 0.900 bicubic
167 pit_b_224 95.630 95.640 4.370 4.360 98.670 1.330 73.76 224 0.900 bicubic
168 resnetv2_101 95.630 4.370 98.990 1.010 44.54 224 0.950 bicubic
169 efficientnetv2_rw_t 95.610 4.390 99.070 0.930 13.65 288 1.000 bicubic
170 crossvit_18_dagger_240 95.560 95.570 4.440 4.430 99.060 0.940 44.27 240 0.875 bicubic
171 convit_base 95.550 4.450 98.890 98.870 1.110 1.130 86.54 224 0.875 bicubic
172 ecaresnet101d convnext_tiny 95.540 95.550 4.460 4.450 99.130 99.020 0.870 0.980 44.57 28.59 224 0.875 bicubic
173 coat_lite_small 95.530 95.540 4.470 4.460 98.860 1.140 19.84 224 0.900 bicubic
174 xcit_medium_24_p16_224 95.530 4.470 98.740 1.260 84.40 224 1.000 bicubic
175 xcit_small_24_p16_224 95.530 4.470 98.760 1.240 47.67 224 1.000 bicubic
176 ecaresnet101d 95.530 4.470 99.130 0.870 44.57 224 0.875 bicubic
177 levit_384 95.530 4.470 99.050 0.950 39.13 224 0.900 bicubic
xcit_small_24_p16_224 95.530 4.470 98.770 1.230 47.67 224 1.000 bicubic
xcit_medium_24_p16_224 95.520 4.480 98.770 1.230 84.40 224 1.000 bicubic
178 crossvit_base_240 95.520 4.480 98.820 1.180 105.03 240 0.875 bicubic
179 visformer_small ecaresnet50t 95.500 95.510 4.500 4.490 98.900 99.120 1.100 0.880 40.22 25.57 224 320 0.900 0.950 bicubic
180 ecaresnet50t fbnetv3_g 95.500 95.510 4.500 4.490 99.120 98.990 0.880 1.010 25.57 16.62 320 288 0.950 bicubic bilinear
181 ssl_resnext101_32x8d 95.470 95.490 4.530 4.510 99.120 0.880 88.79 224 0.875 bilinear
182 crossvit_18_240 visformer_small 95.450 95.480 4.550 4.520 98.790 98.900 1.210 1.100 43.27 40.22 240 224 0.875 0.900 bicubic
183 crossvit_18_240 95.440 4.560 98.790 1.210 43.27 240 0.875 bicubic
184 deit_base_patch16_224 95.440 4.560 98.840 1.160 86.57 224 0.900 bicubic
185 tresnet_xl ssl_resnext101_32x4d 95.440 4.560 99.060 99.130 0.940 0.870 78.44 44.18 224 0.875 bilinear
186 ssl_resnext101_32x4d tresnet_xl 95.430 95.440 4.570 4.560 99.130 99.050 0.870 0.950 44.18 78.44 224 0.875 bilinear
187 resnetrs101 95.420 95.430 4.580 4.570 99.030 0.970 63.62 288 0.940 bicubic
188 xcit_large_24_p16_224 halo2botnet50ts_256 95.410 95.420 4.590 4.580 98.630 99.010 1.370 0.990 189.10 22.64 224 256 1.000 0.950 bicubic
189 xcit_large_24_p16_224 95.420 4.580 98.620 1.380 189.10 224 1.000 bicubic
190 xcit_small_12_p16_224 95.420 4.580 98.840 1.160 26.25 224 1.000 bicubic
191 swsl_resnet50 95.400 4.600 99.290 0.710 25.56 224 0.875 bilinear
xcit_small_12_p16_224 95.400 4.600 98.830 1.170 26.25 224 1.000 bicubic
192 vit_small_patch16_224 95.370 4.630 99.150 0.850 22.05 224 0.900 bicubic
193 resnet101 95.360 4.640 98.860 1.140 44.55 224 0.950 bicubic
194 tf_efficientnet_b3_ap 95.320 4.680 98.900 1.100 12.23 300 0.904 bicubic
195 mixer_b16_224_miil 95.310 95.300 4.690 4.700 98.890 98.880 1.110 1.120 59.88 224 0.875 bilinear
196 tresnet_l 95.290 4.710 99.010 0.990 55.99 224 0.875 bilinear
197 cait_xxs24_384 95.270 95.280 4.730 4.720 98.960 1.040 12.03 384 1.000 bicubic
198 jx_nest_tiny 95.250 4.750 98.980 1.020 17.06 224 0.875 bicubic
199 pit_s_distilled_224 95.240 4.760 99.050 0.950 24.04 224 0.900 bicubic
200 twins_pcpvt_small 95.230 95.210 4.770 4.790 98.880 1.120 24.11 224 0.900 bicubic
201 twins_svt_small convit_small 95.210 95.200 4.790 4.800 98.890 98.900 1.110 1.100 24.06 27.78 224 0.900 0.875 bicubic
202 convit_small twins_svt_small 95.180 95.200 4.820 4.800 98.920 98.880 1.080 1.120 27.78 24.06 224 0.875 0.900 bicubic
203 tf_efficientnet_b1_ns 95.170 4.830 99.120 0.880 7.79 240 0.882 bicubic
204 tf_efficientnetv2_b3 halonet50ts 95.170 95.160 4.830 4.840 98.820 98.770 1.180 1.230 14.36 22.73 300 256 0.904 0.940 bicubic
205 tf_efficientnetv2_b3 95.160 4.840 98.820 1.180 14.36 300 0.904 bicubic
206 lamhalobotnet50ts_256 95.150 4.850 98.880 1.120 22.57 256 0.950 bicubic
207 crossvit_15_240 95.140 4.860 98.930 1.070 27.53 240 0.875 bicubic
208 xcit_tiny_12_p16_384_dist 95.130 4.870 99.020 0.980 6.72 384 1.000 bicubic
209 swin_tiny_patch4_window7_224 95.130 4.870 98.850 1.150 28.29 224 0.900 bicubic
crossvit_15_240 95.120 4.880 98.930 1.070 27.53 240 0.875 bicubic
210 efficientnet_el 95.120 4.880 98.980 1.020 10.59 300 0.904 bicubic
211 gernet_l 95.110 95.100 4.890 4.900 98.900 1.100 31.08 256 0.875 bilinear
212 xcit_tiny_12_p16_384_dist xcit_tiny_12_p8_224_dist 95.110 95.090 4.890 4.910 99.020 98.910 0.980 1.090 6.72 6.71 384 224 1.000 bicubic
213 convmixer_1536_20 ecaresnet101d_pruned 95.080 4.920 99.030 98.980 0.970 1.020 51.63 24.88 224 0.960 0.875 bicubic
214 xcit_tiny_12_p8_224_dist legacy_senet154 95.080 95.070 4.920 4.930 98.910 98.830 1.090 1.170 6.71 115.09 224 1.000 0.875 bicubic bilinear
215 ecaresnet101d_pruned regnetz_b16 95.070 4.930 98.980 99.050 1.020 0.950 24.88 9.72 224 288 0.875 0.940 bicubic
216 vit_small_patch32_384 wide_resnet50_2 95.060 95.070 4.940 4.930 98.990 98.970 1.010 1.030 22.92 68.88 384 224 1.000 0.875 bicubic
217 legacy_senet154 convmixer_1536_20 95.060 4.940 98.830 99.030 1.170 0.970 115.09 51.63 224 0.875 0.960 bilinear bicubic
218 regnetz_b vit_small_patch32_384 95.060 95.050 4.940 4.950 99.050 98.990 0.950 1.010 9.72 22.92 288 384 0.940 1.000 bicubic
219 gluon_resnet152_v1s 95.050 95.040 4.950 4.960 98.930 1.070 60.32 224 0.875 bicubic
halonet50ts 95.050 4.950 98.590 1.410 22.73 256 0.940 bicubic
wide_resnet50_2 95.050 4.950 98.970 1.030 68.88 224 0.875 bicubic
220 tnt_s_patch16_224 95.040 4.960 98.840 1.160 23.76 224 0.900 bicubic
221 seresnext50_32x4d 95.030 4.970 98.890 98.880 1.110 1.120 27.56 224 0.875 bicubic
222 levit_256 95.030 95.020 4.970 4.980 98.890 1.110 18.89 224 0.900 bicubic
223 tf_efficientnet_b3 95.020 4.980 98.910 1.090 12.23 300 0.904 bicubic
224 resnetv2_50x1_bitm 95.020 95.010 4.980 4.990 99.050 99.060 0.950 0.940 25.55 448 1.000 bilinear
225 vit_base_patch32_224 95.010 95.000 4.990 5.000 99.020 99.030 0.980 0.970 88.22 224 0.900 bicubic
coat_mini 94.990 5.010 98.780 1.220 10.34 224 0.900 bicubic
226 tresnet_m_448 94.990 5.010 98.980 1.020 31.39 448 0.875 bilinear
227 resnest50d_4s2x40d coat_mini 94.950 94.970 5.050 5.030 99.070 98.780 0.930 1.220 30.42 10.34 224 0.875 0.900 bicubic
228 rexnet_200 resnest50d_4s2x40d 94.940 94.960 5.060 5.040 99.000 99.070 1.000 0.930 16.37 30.42 224 0.875 bicubic
229 gluon_seresnext101_64x4d rexnet_200 94.940 94.950 5.060 5.050 98.820 99.010 1.180 0.990 88.23 16.37 224 0.875 bicubic
230 gluon_senet154 94.930 94.920 5.070 5.080 98.770 98.760 1.230 1.240 115.09 224 0.875 bicubic
231 gluon_seresnext101_32x4d 94.920 5.080 98.810 1.190 48.96 224 0.875 bicubic
232 seresnet33ts gluon_seresnext101_64x4d 94.870 94.920 5.130 5.080 98.790 98.830 1.210 1.170 19.78 88.23 256 224 0.900 0.875 bicubic
233 resmlp_36_distilled_224 tf_efficientnet_lite4 94.870 94.890 5.130 5.110 98.860 99.020 1.140 0.980 44.69 13.01 224 380 0.875 0.920 bicubic bilinear
234 resmlp_36_distilled_224 94.880 5.120 98.840 1.160 44.69 224 0.875 bicubic
235 ssl_resnext50_32x4d 94.870 5.130 98.890 1.110 25.03 224 0.875 bilinear
236 gcresnet50t 94.860 5.140 98.800 1.200 25.90 256 0.900 bicubic
237 ssl_resnext50_32x4d resnest50d 94.860 94.850 5.140 5.150 98.870 98.880 1.130 1.120 25.03 27.48 224 0.875 bilinear
238 tf_efficientnet_lite4 seresnet33ts 94.860 94.850 5.140 5.150 99.020 98.790 0.980 1.210 13.01 19.78 380 256 0.920 0.900 bilinear bicubic
239 crossvit_small_240 94.830 5.170 99.020 0.980 26.86 240 0.875 bicubic
240 resnest50d lambda_resnet50ts 94.830 94.790 5.170 5.210 98.880 98.460 1.120 1.540 27.48 21.54 224 256 0.875 0.950 bilinear bicubic
lamhalobotnet50ts_256 94.800 5.200 98.550 1.450 22.57 256 0.950 bicubic
sehalonet33ts 94.780 5.220 98.570 1.430 13.69 256 0.940 bicubic
resnest50d_1s4x24d 94.770 5.230 98.980 1.020 25.68 224 0.875 bicubic
241 ecaresnetlight 94.770 5.230 98.800 1.200 30.16 224 0.875 bicubic
242 lambda_resnet50ts sehalonet33ts 94.770 5.230 98.470 98.570 1.530 1.430 21.54 13.69 256 0.950 0.940 bicubic
243 halo2botnet50ts_256 resnest50d_1s4x24d 94.760 94.750 5.240 5.250 98.660 98.980 1.340 1.020 22.64 25.68 256 224 0.950 0.875 bicubic
244 gluon_resnet152_v1d 94.750 94.740 5.250 5.260 98.740 1.260 60.21 224 0.875 bicubic
245 xcit_tiny_12_p8_224 gluon_resnet101_v1s 94.710 94.720 5.290 5.280 98.830 98.820 1.170 1.180 6.71 44.67 224 1.000 0.875 bicubic
246 deit_small_distilled_patch16_224 94.710 5.290 99.030 0.970 22.44 224 0.900 bicubic
247 haloregnetz_b 94.710 94.700 5.290 5.300 98.660 1.340 11.68 224 0.940 bicubic
248 gluon_resnet101_v1s xcit_tiny_12_p8_224 94.700 94.690 5.300 5.310 98.820 98.830 1.180 1.170 44.67 6.71 224 0.875 1.000 bicubic
249 cspdarknet53 resmlp_big_24_224 94.670 5.330 98.810 98.480 1.190 1.520 27.64 129.14 256 224 0.887 0.875 bilinear bicubic
250 resmlp_big_24_224 cspdarknet53 94.650 94.660 5.350 5.340 98.490 98.800 1.510 1.200 129.14 27.64 224 256 0.875 0.887 bicubic bilinear
251 gluon_resnext101_64x4d 94.640 94.660 5.360 5.340 98.670 98.650 1.330 1.350 83.46 224 0.875 bicubic
252 efficientnet_b2 efficientnet_b3_pruned 94.630 5.370 98.710 98.760 1.290 1.240 9.11 9.86 288 300 1.000 0.904 bicubic
253 ecaresnet50d 94.620 94.630 5.380 5.370 98.890 1.110 25.58 224 0.875 bicubic
efficientnet_b3_pruned 94.620 5.380 98.770 1.230 9.86 300 0.904 bicubic
254 gernet_m 94.620 5.380 98.860 1.140 21.14 224 0.875 bilinear
255 pit_s_224 efficientnet_b2 94.580 94.610 5.420 5.390 98.720 98.710 1.280 1.290 23.46 9.11 224 288 0.900 1.000 bicubic
256 repvgg_b3 pit_s_224 94.570 94.590 5.430 5.410 98.790 98.700 1.210 1.300 123.09 23.46 224 0.875 0.900 bilinear bicubic
257 nf_resnet50 repvgg_b3 94.560 94.570 5.440 5.430 98.790 98.780 1.210 1.220 25.56 123.09 288 224 0.940 0.875 bicubic bilinear
258 seresnet50 sebotnet33ts_256 94.560 94.570 5.440 5.430 98.750 98.500 1.250 1.500 28.09 13.70 224 256 0.875 0.940 bicubic
259 regnety_320 nf_resnet50 94.550 5.450 98.850 98.790 1.150 1.210 145.05 25.56 224 288 0.875 0.940 bicubic
260 inception_resnet_v2 seresnet50 94.540 94.550 5.460 5.450 98.790 98.750 1.210 1.250 55.84 28.09 299 224 0.897 0.875 bicubic
261 regnety_320 94.540 5.460 98.850 1.150 145.05 224 0.875 bicubic
262 resnext50_32x4d 94.540 5.460 98.610 1.390 25.03 224 0.950 bicubic
263 gluon_resnext101_32x4d 94.540 5.460 98.630 1.370 44.18 224 0.875 bicubic
264 repvgg_b3g4 inception_resnet_v2 94.530 94.540 5.470 5.460 98.960 98.790 1.040 1.210 83.83 55.84 224 299 0.875 0.897 bilinear bicubic
265 xcit_tiny_24_p16_224_dist 94.520 94.530 5.480 5.470 98.790 98.780 1.210 1.220 12.12 224 1.000 bicubic
266 convmixer_768_32 repvgg_b3g4 94.490 94.520 5.510 5.480 98.850 98.970 1.150 1.030 21.11 83.83 224 0.960 0.875 bicubic bilinear
267 convmixer_768_32 94.500 5.500 98.850 1.150 21.11 224 0.960 bicubic
268 gcresnext50ts 94.490 5.510 98.670 1.330 15.67 256 0.900 bicubic
269 tf_efficientnet_b2_ap 94.490 5.510 98.620 1.380 9.11 260 0.890 bicubic
270 regnety_120 94.480 5.520 98.810 1.190 51.82 224 0.875 bicubic
271 gcresnext50ts rexnet_150 94.480 5.520 98.670 98.790 1.330 1.210 15.67 9.73 256 224 0.900 0.875 bicubic
gcresnet33ts 94.480 5.520 98.780 1.220 19.88 256 0.900 bicubic
272 cspresnext50 94.470 5.530 98.680 1.320 20.57 224 0.875 bilinear
273 ssl_resnet50 gcresnet33ts 94.470 5.530 98.920 98.770 1.080 1.230 25.56 19.88 224 256 0.875 0.900 bilinear bicubic
274 rexnet_150 regnetx_320 94.470 94.460 5.530 5.540 98.790 98.740 1.210 1.260 9.73 107.81 224 0.875 bicubic
275 resmlp_24_distilled_224 94.450 94.460 5.550 5.540 98.770 1.230 30.02 224 0.875 bicubic
276 resnetv2_50 ssl_resnet50 94.440 94.450 5.560 5.550 98.730 98.920 1.270 1.080 25.55 25.56 224 0.950 0.875 bicubic bilinear
277 regnetx_320 resnetv2_50 94.440 5.560 98.730 98.740 1.270 1.260 107.81 25.55 224 0.875 0.950 bicubic
278 tf_efficientnetv2_b2 94.410 94.420 5.590 5.580 98.570 1.430 10.10 260 0.890 bicubic
279 deit_small_patch16_224 efficientnet_el_pruned 94.400 5.600 98.690 98.740 1.310 1.260 22.05 10.59 224 300 0.900 0.904 bicubic
280 tf_efficientnet_el 94.400 5.600 98.710 1.290 10.59 300 0.904 bicubic
281 efficientnet_el_pruned deit_small_patch16_224 94.390 5.610 98.750 98.690 1.250 1.310 10.59 22.05 300 224 0.904 0.900 bicubic
282 inception_v4 94.370 94.380 5.630 5.620 98.580 1.420 42.68 299 0.875 bicubic
283 tf_efficientnet_b2 legacy_seresnext101_32x4d 94.370 94.360 5.630 5.640 98.610 98.650 1.390 1.350 9.11 48.96 260 224 0.890 0.875 bicubic bilinear
284 legacy_seresnext101_32x4d resnet50_gn 94.350 94.360 5.650 5.640 98.630 98.710 1.370 1.290 48.96 25.56 224 0.875 0.940 bilinear bicubic
285 tf_efficientnet_b2 94.360 5.640 98.610 1.390 9.11 260 0.890 bicubic
286 gluon_seresnext50_32x4d 94.330 5.670 98.610 1.390 27.56 224 0.875 bicubic
287 resnet50 94.330 5.670 98.440 1.560 25.56 224 0.950 bicubic
288 gluon_seresnext50_32x4d ecaresnet26t 94.330 94.310 5.670 5.690 98.620 98.720 1.380 1.280 27.56 16.01 224 320 0.875 0.950 bicubic
ecaresnet26t 94.300 5.700 98.710 1.290 16.01 320 0.950 bicubic
289 dpn107 94.300 5.700 98.470 1.530 86.92 224 0.875 bicubic
290 xception71 resnetrs50 94.290 94.300 5.710 5.700 98.640 1.360 42.34 35.69 299 224 0.903 0.910 bicubic
291 resnetrs50 xception71 94.290 94.280 5.710 5.720 98.640 1.360 35.69 42.34 224 299 0.910 0.903 bicubic
292 resnet50d cait_xxs36_224 94.270 94.260 5.730 5.740 98.720 1.280 25.58 17.30 224 0.875 1.000 bicubic
293 gluon_xception65 94.260 5.740 98.570 1.430 39.92 299 0.903 bicubic
294 cait_xxs36_224 resnet50d 94.260 5.740 98.710 98.720 1.290 1.280 17.30 25.58 224 1.000 0.875 bicubic
295 skresnext50_32x4d 94.260 5.740 98.470 98.460 1.530 1.540 27.48 224 0.875 bicubic
296 regnetx_120 94.240 5.760 98.670 98.650 1.330 1.350 46.11 224 0.875 bicubic
297 dpn92 gluon_resnet101_v1d 94.220 94.240 5.780 5.760 98.730 98.560 1.270 1.440 37.67 44.57 224 0.875 bicubic
298 ecaresnet50d_pruned dpn92 94.210 94.230 5.790 5.770 98.730 1.270 19.94 37.67 224 0.875 bicubic
299 mixnet_xl ecaresnet50d_pruned 94.200 94.220 5.800 5.780 98.340 98.730 1.660 1.270 11.90 19.94 224 0.875 bicubic
300 eca_resnet33ts resmlp_36_224 94.200 5.800 98.770 98.660 1.230 1.340 19.68 44.69 256 224 0.900 0.875 bicubic
301 gluon_resnet101_v1d tf_efficientnet_lite3 94.200 5.800 98.570 98.640 1.430 1.360 44.57 8.20 224 300 0.875 0.904 bicubic bilinear
302 resmlp_36_224 eca_resnet33ts 94.190 5.810 98.660 98.770 1.340 1.230 44.69 19.68 224 256 0.875 0.900 bicubic
303 resnext50d_32x4d mixnet_xl 94.190 5.810 98.570 98.340 1.430 1.660 25.05 11.90 224 0.875 bicubic
304 tf_efficientnet_lite3 resnext50d_32x4d 94.190 94.180 5.810 5.820 98.640 98.570 1.360 1.430 8.20 25.05 300 224 0.904 0.875 bilinear bicubic
305 levit_192 regnety_080 94.180 94.170 5.820 5.830 98.560 98.680 1.440 1.320 10.95 39.18 224 0.900 0.875 bicubic
306 regnety_080 levit_192 94.180 94.170 5.820 5.830 98.680 98.550 1.320 1.450 39.18 10.95 224 0.875 0.900 bicubic
307 ens_adv_inception_resnet_v2 94.160 5.840 98.610 98.600 1.390 1.400 55.84 299 0.897 bicubic
308 gluon_resnet152_v1c 94.160 5.840 98.640 1.360 60.21 224 0.875 bicubic
309 gmlp_s16_224 94.150 5.850 98.500 1.500 19.42 224 0.875 bicubic
310 regnety_064 efficientnet_b2_pruned 94.150 94.140 5.850 5.860 98.740 98.530 1.260 1.470 30.58 8.31 224 260 0.875 0.890 bicubic
311 vit_base_patch16_sam_224 regnety_064 94.150 94.140 5.850 5.860 98.670 98.730 1.330 1.270 86.57 30.58 224 0.900 0.875 bicubic
312 efficientnet_b2_pruned vit_base_patch16_224_sam 94.140 5.860 98.520 98.670 1.480 1.330 8.31 86.57 260 224 0.890 0.900 bicubic
dpn98 94.120 5.880 98.580 1.420 61.57 224 0.875 bicubic
313 regnetx_160 94.120 5.880 98.740 1.260 54.28 224 0.875 bicubic
314 nf_regnet_b1 94.120 5.880 98.620 98.630 1.380 1.370 10.22 288 0.900 bicubic
315 resnext50_32x4d dpn98 94.110 5.890 98.350 98.580 1.650 1.420 25.03 61.57 224 0.875 bicubic
316 ese_vovnet39b 94.090 5.910 98.660 1.340 24.57 224 0.875 bicubic
317 gluon_resnet152_v1b 94.080 5.920 98.460 1.540 60.19 224 0.875 bicubic
318 xcit_tiny_24_p16_224 94.080 94.070 5.920 5.930 98.510 1.490 12.12 224 1.000 bicubic
319 coat_lite_mini 94.050 94.060 5.950 5.940 98.540 98.550 1.460 1.450 11.01 224 0.900 bicubic
320 eca_halonext26ts 94.050 5.950 98.500 1.500 10.76 256 0.940 bicubic
321 halonet26t 94.020 5.980 98.500 1.500 12.48 256 0.950 bicubic
322 resmlp_24_224 94.020 5.980 98.330 1.670 30.02 224 0.875 bicubic
323 hrnet_w64 94.010 5.990 98.620 1.380 128.06 224 0.875 bilinear
324 dpn131 93.990 6.010 98.720 1.280 79.25 224 0.875 bicubic
hrnet_w64 93.990 6.010 98.620 1.380 128.06 224 0.875 bilinear
halonet26t 93.980 6.020 98.490 1.510 12.48 256 0.950 bicubic
325 dla102x2 93.960 6.040 98.480 1.520 41.28 224 0.875 bilinear
326 hrnet_w48 fbnetv3_b 93.940 93.960 6.060 6.040 98.610 98.630 1.390 1.370 77.47 8.60 224 256 0.875 0.950 bilinear
327 resnetblur50 93.950 6.050 98.580 1.420 25.56 224 0.875 bicubic
328 tf_efficientnetv2_b1 93.940 6.060 98.620 1.380 8.14 240 0.882 bicubic
329 resnetblur50 fbnetv3_d 93.930 6.070 98.580 98.740 1.420 1.260 25.56 10.31 224 256 0.875 0.950 bicubic bilinear
330 tf_efficientnet_cc_b1_8e hrnet_w48 93.920 6.080 98.250 98.610 1.750 1.390 39.72 77.47 240 224 0.882 0.875 bicubic bilinear
331 tf_efficientnet_cc_b1_8e 93.910 6.090 98.260 1.740 39.72 240 0.882 bicubic
332 rexnet_130 93.900 6.100 98.400 1.600 7.56 224 0.875 bicubic
333 regnetx_064 93.890 6.110 98.630 1.370 26.21 224 0.875 bicubic
334 regnetx_080 93.870 6.130 98.520 1.480 39.57 224 0.875 bicubic
335 regnety_040 93.860 6.140 98.640 98.650 1.360 1.350 20.65 224 0.875 bicubic
336 repvgg_b2g4 93.840 6.160 98.600 98.590 1.400 1.410 61.76 224 0.875 bilinear
337 efficientnet_em 93.840 93.830 6.160 6.170 98.810 1.190 6.90 240 0.882 bicubic
338 gluon_resnext50_32x4d lambda_resnet26t 93.820 93.830 6.180 6.170 98.410 98.650 1.590 1.350 25.03 10.96 224 256 0.875 0.940 bicubic
lambda_resnet26t 93.820 6.180 98.650 1.350 10.96 256 0.940 bicubic
339 pit_xs_distilled_224 93.820 6.180 98.670 1.330 11.00 224 0.900 bicubic
340 eca_botnext26ts_256 resnext101_32x8d 93.790 93.820 6.210 6.180 98.500 98.580 1.500 1.420 10.59 88.79 256 224 0.950 0.875 bicubic bilinear
341 resnext101_32x8d gluon_resnext50_32x4d 93.790 93.810 6.210 6.190 98.580 98.410 1.420 1.590 88.79 25.03 224 0.875 bilinear bicubic
342 gluon_resnet50_v1d eca_botnext26ts_256 93.780 6.220 98.400 98.500 1.600 1.500 25.58 10.59 224 256 0.875 0.950 bicubic
343 xception65 gluon_resnet50_v1d 93.770 6.230 98.360 98.390 1.640 1.610 39.92 25.58 299 224 0.903 0.875 bicubic
344 cspresnet50 xception65 93.750 93.760 6.250 6.240 98.630 98.370 1.370 1.630 21.62 39.92 256 299 0.887 0.903 bilinear bicubic
345 gluon_resnet101_v1b 93.730 93.750 6.270 6.250 98.400 98.380 1.600 1.620 44.55 224 0.875 bicubic
346 res2net101_26w_4s 93.750 6.250 98.310 1.690 45.21 224 0.875 bilinear
347 cspresnet50 93.740 6.260 98.640 1.360 21.62 256 0.887 bilinear
348 legacy_seresnext50_32x4d 93.730 6.270 98.580 1.420 27.56 224 0.875 bilinear
349 wide_resnet101_2 93.720 6.280 98.540 1.460 126.89 224 0.875 bilinear
350 res2net101_26w_4s lambda_resnet26rpt_256 93.720 93.710 6.280 6.290 98.320 98.510 1.680 1.490 45.21 10.99 224 256 0.875 0.940 bilinear bicubic
351 legacy_seresnext50_32x4d dpn68b 93.720 93.690 6.280 6.310 98.580 98.520 1.420 1.480 27.56 12.61 224 0.875 bilinear bicubic
352 lambda_resnet26rpt_256 tf_efficientnet_b1_ap 93.720 93.690 6.280 6.310 98.500 98.360 1.500 1.640 10.99 7.79 256 240 0.940 0.882 bicubic
353 tf_efficientnet_b1_ap gluon_resnet101_v1c 93.710 93.660 6.290 6.340 98.360 98.420 1.640 1.580 7.79 44.57 240 224 0.882 0.875 bicubic
dpn68b 93.680 6.320 98.530 1.470 12.61 224 0.875 bicubic
gluon_resnet101_v1c 93.660 6.340 98.410 1.590 44.57 224 0.875 bicubic
354 vit_tiny_patch16_384 93.650 6.350 98.600 1.400 5.79 384 1.000 bicubic
355 gluon_resnet50_v1s tf_efficientnet_b0_ns 93.630 6.370 98.470 98.640 1.530 1.360 25.68 5.29 224 0.875 bicubic
356 tf_efficientnet_b0_ns gluon_resnet50_v1s 93.620 6.380 98.640 98.460 1.360 1.540 5.29 25.68 224 0.875 bicubic
357 resnet33ts cait_xxs24_224 93.600 6.400 98.530 98.440 1.470 1.560 19.68 11.96 256 224 0.900 1.000 bicubic
358 cait_xxs24_224 resnet33ts 93.590 93.600 6.410 6.400 98.440 98.540 1.560 1.460 11.96 19.68 224 256 1.000 0.900 bicubic
359 hrnet_w44 coat_tiny 93.580 93.590 6.420 6.410 98.700 98.420 1.300 1.580 67.06 5.50 224 0.875 0.900 bilinear bicubic
360 coat_tiny hrnet_w44 93.580 93.550 6.420 6.450 98.410 98.700 1.590 1.300 5.50 67.06 224 0.900 0.875 bicubic bilinear
361 regnetx_040 93.550 6.450 98.560 98.550 1.440 1.450 22.12 224 0.875 bicubic
362 hrnet_w32 93.520 93.530 6.480 6.470 98.440 98.460 1.560 1.540 41.23 224 0.875 bilinear
363 eca_halonext26ts dla102x 93.510 93.520 6.490 6.480 98.280 98.510 1.720 1.490 10.76 26.31 256 224 0.940 0.875 bicubic bilinear
364 tf_efficientnet_b1 xcit_nano_12_p8_384_dist 93.510 93.520 6.490 6.480 98.360 98.540 1.640 1.460 7.79 3.05 240 384 0.882 1.000 bicubic
365 dla102x tf_efficientnet_b1 93.510 93.500 6.490 6.500 98.500 98.360 1.500 1.640 26.31 7.79 224 240 0.875 0.882 bilinear bicubic
366 botnet26t_256 93.500 6.500 98.300 1.700 12.49 256 0.950 bicubic
367 repvgg_b2 93.490 6.510 98.730 1.270 89.02 224 0.875 bilinear
368 hrnet_w40 93.490 6.510 98.590 98.580 1.410 1.420 57.56 224 0.875 bilinear
xcit_nano_12_p8_384_dist 93.480 6.520 98.520 1.480 3.05 384 1.000 bicubic
xception 93.480 6.520 98.530 1.470 22.86 299 0.897 bicubic
369 resnet32ts 93.470 6.530 98.490 1.510 17.96 256 0.900 bicubic
370 gluon_inception_v3 xception 93.460 93.470 6.540 6.530 98.560 98.530 1.440 1.470 23.83 22.86 299 0.875 0.897 bicubic
371 res2net50_26w_8s gluon_inception_v3 93.430 93.460 6.570 6.540 98.180 98.570 1.820 1.430 48.40 23.83 224 299 0.875 bilinear bicubic
372 mixnet_l 93.430 93.450 6.570 6.550 98.220 1.780 7.33 224 0.875 bicubic
373 legacy_seresnet152 xception41 93.420 93.430 6.580 6.570 98.340 98.430 1.660 1.570 66.82 26.97 224 299 0.875 0.903 bilinear bicubic
374 xception41 res2net50_26w_8s 93.410 6.590 98.420 98.180 1.580 1.820 26.97 48.40 299 224 0.903 0.875 bicubic bilinear
375 res2net50_26w_6s 93.410 6.590 98.280 1.720 37.05 224 0.875 bilinear
376 legacy_seresnet152 93.400 6.600 98.340 1.660 66.82 224 0.875 bilinear
377 xcit_tiny_12_p16_224_dist 93.400 6.600 98.490 1.510 6.72 224 1.000 bicubic
378 res2net50_26w_6s dla169 93.400 93.340 6.600 6.660 98.280 98.590 1.720 1.410 37.05 53.39 224 0.875 bilinear
379 resnest26d bat_resnext26ts 93.360 93.330 6.640 6.670 98.640 98.350 1.360 1.650 17.07 10.73 224 256 0.875 0.900 bilinear bicubic
380 levit_128 93.340 93.330 6.660 6.670 98.380 1.620 9.21 224 0.900 bicubic
dla169 93.340 6.660 98.600 1.400 53.39 224 0.875 bilinear
381 repvgg_b1 93.330 6.670 98.510 1.490 57.42 224 0.875 bilinear
382 tf_inception_v3 tf_mixnet_l 93.330 93.320 6.670 6.680 98.040 98.030 1.960 1.970 23.83 7.33 299 224 0.875 bicubic
383 tv_resnet152 resnest26d 93.330 93.320 6.670 6.680 98.390 98.630 1.610 1.370 60.19 17.07 224 0.875 bilinear
384 tf_mixnet_l tf_inception_v3 93.310 93.320 6.690 6.680 98.030 1.970 7.33 23.83 224 299 0.875 bicubic
385 bat_resnext26ts tv_resnet152 93.310 6.690 98.350 98.390 1.650 1.610 10.73 60.19 256 224 0.900 0.875 bicubic bilinear
386 legacy_seresnet101 93.300 6.700 98.500 98.510 1.500 1.490 49.33 224 0.875 bilinear
387 selecsls60b 93.290 93.300 6.710 6.700 98.280 1.720 32.77 224 0.875 bicubic
388 efficientnet_b1 93.240 93.250 6.760 6.750 98.290 1.710 7.79 256 1.000 bicubic
389 coat_lite_tiny 93.220 93.230 6.780 6.770 98.270 98.260 1.730 1.740 5.72 224 0.900 bicubic
390 efficientnet_es hrnet_w30 93.200 93.190 6.800 6.810 98.400 98.410 1.600 1.590 5.44 37.71 224 0.875 bicubic bilinear
391 hrnet_w30 dla60_res2net 93.200 93.180 6.800 6.820 98.410 98.420 1.590 1.580 37.71 20.85 224 0.875 bilinear
392 dla60_res2next 93.180 6.820 98.410 1.590 17.03 224 0.875 bilinear
393 dla60_res2net efficientnet_es 93.160 93.140 6.840 6.860 98.410 98.420 1.590 1.580 20.85 5.44 224 0.875 bilinear bicubic
394 dla60x 93.120 6.880 98.510 1.490 17.35 224 0.875 bilinear
395 regnetx_032 93.120 6.880 98.390 1.610 15.30 224 0.875 bicubic
396 pit_xs_224 93.120 93.110 6.880 6.890 98.320 1.680 10.62 224 0.900 bicubic
397 tf_efficientnetv2_b0 93.110 6.890 98.390 1.610 7.14 224 0.875 bicubic
398 dla60x dla102 93.090 93.060 6.910 6.940 98.490 98.550 1.510 1.450 17.35 33.27 224 0.875 bilinear
dla102 93.080 6.920 98.540 1.460 33.27 224 0.875 bilinear
gluon_resnet50_v1c 93.030 6.970 98.370 1.630 25.58 224 0.875 bicubic
regnety_016 93.030 6.970 98.360 1.640 11.20 224 0.875 bicubic
399 rexnet_100 93.030 6.970 98.190 1.810 4.80 224 0.875 bicubic
400 regnety_016 93.030 6.970 98.360 1.640 11.20 224 0.875 bicubic
401 gluon_resnet50_v1c 93.030 6.970 98.390 1.610 25.58 224 0.875 bicubic
402 selecsls60 93.020 6.980 98.310 1.690 30.67 224 0.875 bicubic
403 repvgg_b1g4 93.000 92.980 7.000 7.020 98.430 1.570 39.97 224 0.875 bilinear
404 legacy_seresnet50 92.950 92.960 7.050 7.040 98.190 1.810 28.09 224 0.875 bilinear
405 hardcorenas_f 92.950 7.050 98.160 1.840 8.20 224 0.875 bilinear
406 tf_efficientnet_em 92.950 92.930 7.050 7.070 98.210 98.200 1.790 1.800 6.90 240 0.882 bicubic
407 crossvit_9_dagger_240 adv_inception_v3 92.920 92.890 7.080 7.110 98.250 98.140 1.750 1.860 8.78 23.83 240 299 0.875 bicubic
408 adv_inception_v3 crossvit_9_dagger_240 92.890 7.110 98.130 98.230 1.870 1.770 23.83 8.78 299 240 0.875 bicubic
409 res2next50 92.850 92.860 7.150 7.140 98.180 98.190 1.820 1.810 24.67 224 0.875 bilinear
410 gmixer_24_224 92.840 92.830 7.160 7.170 97.880 2.120 24.72 224 0.875 bicubic
411 resmlp_12_distilled_224 92.830 7.170 98.140 1.860 15.35 224 0.875 bicubic
412 tf_efficientnet_cc_b0_8e 92.820 92.830 7.180 7.170 98.180 1.820 24.01 224 0.875 bicubic
413 seresnext26t_32x4d 92.810 92.820 7.190 7.180 98.370 1.630 16.81 224 0.875 bicubic
414 tv_resnet101 92.810 92.820 7.190 7.180 98.230 98.250 1.770 1.750 44.55 224 0.875 bilinear
415 gcresnext26ts 92.780 7.220 98.260 1.740 10.48 256 0.900 bicubic
416 efficientnet_b1_pruned 92.770 7.230 98.040 1.960 6.33 240 0.882 bicubic
417 gcresnext26ts densenet201 92.770 92.750 7.230 7.250 98.270 98.230 1.730 1.770 10.48 20.01 256 224 0.900 0.875 bicubic
418 tv_resnext50_32x4d resnet26t 92.760 92.750 7.240 7.250 98.280 98.230 1.720 1.770 25.03 16.01 224 256 0.875 0.940 bilinear bicubic
419 densenet201 tv_resnext50_32x4d 92.750 7.250 98.240 98.270 1.760 1.730 20.01 25.03 224 0.875 bicubic bilinear
420 resnet26t res2net50_14w_8s 92.750 92.740 7.250 7.260 98.240 98.180 1.760 1.820 16.01 25.06 256 224 0.940 0.875 bicubic bilinear
421 seresnext26d_32x4d inception_v3 92.740 92.720 7.260 7.280 98.150 97.970 1.850 2.030 16.81 23.83 224 299 0.875 bicubic
422 inception_v3 efficientnet_b0 92.720 92.690 7.280 7.310 97.960 98.070 2.040 1.930 23.83 5.29 299 224 0.875 bicubic
423 res2net50_14w_8s seresnext26d_32x4d 92.720 92.690 7.280 7.310 98.190 98.150 1.810 1.850 25.06 16.81 224 0.875 bilinear bicubic
424 resnet34d seresnext26ts 92.700 92.690 7.300 7.310 98.300 98.290 1.700 1.710 21.82 10.39 224 256 0.875 0.900 bicubic
425 seresnext26ts resnet34d 92.680 7.320 98.300 98.310 1.700 1.690 10.39 21.82 256 224 0.900 0.875 bicubic
426 efficientnet_b0 tf_efficientnet_lite2 92.670 92.650 7.330 7.350 98.080 98.230 1.920 1.770 5.29 6.09 224 260 0.875 0.890 bicubic
427 eca_resnext26ts legacy_seresnext26_32x4d 92.660 92.640 7.340 7.360 98.260 98.130 1.740 1.870 10.30 16.79 256 224 0.900 0.875 bicubic
428 tf_efficientnet_lite2 tf_efficientnet_lite1 92.660 92.620 7.340 7.380 98.230 98.070 1.770 1.930 6.09 5.42 260 240 0.890 0.882 bicubic
429 legacy_seresnext26_32x4d eca_resnext26ts 92.630 92.610 7.370 7.390 98.120 98.260 1.880 1.740 16.79 10.30 224 256 0.875 0.900 bicubic
430 tf_efficientnet_cc_b0_4e 92.620 92.600 7.380 7.400 98.080 1.920 13.31 224 0.875 bicubic
431 tf_efficientnet_lite1 hardcorenas_e 92.620 92.570 7.380 7.430 98.080 98.110 1.920 1.890 5.42 8.07 240 224 0.882 0.875 bicubic bilinear
432 hardcorenas_e gluon_resnet50_v1b 92.580 92.540 7.420 7.460 98.110 98.170 1.890 1.830 8.07 25.56 224 0.875 bilinear bicubic
433 res2net50_48w_2s 92.550 92.540 7.450 7.460 98.080 98.090 1.920 1.910 25.29 224 0.875 bilinear
434 gluon_resnet50_v1b xcit_tiny_12_p16_224 92.540 92.500 7.460 7.500 98.190 98.240 1.810 1.760 25.56 6.72 224 0.875 1.000 bicubic
435 densenet161 92.500 92.490 7.500 7.510 98.290 1.710 28.68 224 0.875 bicubic
436 xcit_tiny_12_p16_224 res2net50_26w_4s 92.490 7.510 98.250 98.060 1.750 1.940 6.72 25.70 224 1.000 0.875 bicubic bilinear
437 res2net50_26w_4s tinynet_a 92.480 92.440 7.520 7.560 98.070 98.080 1.930 1.920 25.70 6.19 224 192 0.875 bilinear bicubic
438 mixnet_m 92.440 92.430 7.560 7.570 97.870 2.130 5.01 224 0.875 bicubic
439 convmixer_1024_20_ks9_p14 92.430 92.420 7.570 7.580 98.270 1.730 24.38 224 0.960 bicubic
440 hardcorenas_d 92.420 92.400 7.580 7.600 98.070 1.930 7.50 224 0.875 bilinear
441 mobilenetv2_120d 92.400 7.600 98.050 1.950 5.83 224 0.875 bicubic
442 skresnet34 92.380 92.390 7.620 7.610 98.140 98.150 1.860 1.850 22.28 224 0.875 bicubic
443 tf_mixnet_m 92.330 7.670 97.890 2.110 5.01 224 0.875 bicubic
444 hrnet_w18 92.310 92.320 7.690 7.680 98.250 1.750 21.30 224 0.875 bilinear
445 selecsls42b ese_vovnet19b_dw 92.300 92.280 7.700 7.720 98.140 98.090 1.860 1.910 32.46 6.54 224 0.875 bicubic
446 mobilenetv3_large_100_miil selecsls42b 92.270 92.280 7.730 7.720 97.640 98.140 2.360 1.860 5.48 32.46 224 0.875 bilinear bicubic
447 ese_vovnet19b_dw mobilenetv3_large_100_miil 92.270 92.260 7.730 7.740 98.100 97.640 1.900 2.360 6.54 5.48 224 0.875 bicubic bilinear
448 tf_efficientnet_b0 92.230 92.260 7.770 7.740 98.000 2.000 5.29 224 0.875 bicubic
449 tf_efficientnet_b0_ap dla60 92.220 92.230 7.780 7.770 98.020 98.110 1.980 1.890 5.29 22.04 224 0.875 bicubic bilinear
450 dla60 resmlp_12_224 92.220 92.210 7.780 7.790 98.110 98.160 1.890 1.840 22.04 15.35 224 0.875 bilinear bicubic
451 resmlp_12_224 tf_efficientnet_b0_ap 92.190 92.200 7.810 7.800 98.160 98.020 1.840 1.980 15.35 5.29 224 0.875 bicubic
452 regnetx_016 92.160 7.840 98.210 1.790 9.19 224 0.875 bicubic
453 gernet_s 92.140 7.860 98.190 1.810 8.17 224 0.875 bilinear
454 xcit_nano_12_p8_224_dist 92.100 7.900 98.160 1.840 3.05 224 1.000 bicubic
455 resnet26d 92.050 92.070 7.950 7.930 97.960 97.970 2.040 2.030 16.01 224 0.875 bicubic
456 vit_small_patch32_224 92.030 92.040 7.970 7.960 98.230 1.770 22.88 224 0.900 bicubic
457 vit_tiny_r_s16_p8_384 92.030 92.040 7.970 7.960 98.290 1.710 6.36 384 1.000 bicubic
458 dpn68 92.020 92.030 7.980 7.970 98.040 98.050 1.960 1.950 12.61 224 0.875 bicubic
459 hardcorenas_c 92.020 7.980 97.840 2.160 5.52 224 0.875 bilinear
460 tf_efficientnet_es 91.990 91.980 8.010 8.020 97.870 2.130 5.44 224 0.875 bicubic
461 levit_128s 91.950 91.960 8.050 8.040 98.060 1.940 7.78 224 0.900 bicubic
462 repvgg_a2 91.940 8.060 98.150 1.850 28.21 224 0.875 bilinear
463 densenet169 91.910 91.920 8.090 8.080 98.100 1.900 14.15 224 0.875 bicubic
464 densenetblur121d 91.910 8.090 98.090 98.070 1.910 1.930 8.00 224 0.875 bicubic
465 tv_resnet50 91.890 91.880 8.110 8.120 98.040 1.960 25.56 224 0.875 bilinear
466 mixer_b16_224 resnext26ts 91.860 91.870 8.140 8.130 97.230 97.920 2.770 2.080 59.88 10.30 224 256 0.875 0.900 bicubic
467 resnext26ts mixer_b16_224 91.860 8.140 97.930 97.250 2.070 2.750 10.30 59.88 256 224 0.900 0.875 bicubic
468 xcit_nano_12_p16_384_dist mobilenetv2_140 91.830 91.840 8.170 8.160 98.010 97.850 1.990 2.150 3.05 6.11 384 224 1.000 0.875 bicubic
469 mobilenetv2_140 mixnet_s 91.830 8.170 97.860 97.690 2.140 2.310 6.11 4.13 224 0.875 bicubic
470 mixnet_s xcit_nano_12_p16_384_dist 91.820 91.830 8.180 8.170 97.690 98.020 2.310 1.980 4.13 3.05 224 384 0.875 1.000 bicubic
471 hardcorenas_b 91.780 8.220 97.780 2.220 5.18 224 0.875 bilinear
472 vit_tiny_patch16_224 91.760 8.240 98.040 1.960 5.72 224 0.900 bicubic
473 hardcorenas_b regnety_008 91.740 91.720 8.260 8.280 97.780 98.180 2.220 1.820 5.18 6.26 224 0.875 bilinear bicubic
474 resnest14d 91.730 91.720 8.270 8.280 97.870 2.130 10.61 224 0.875 bilinear
475 regnety_008 densenet121 91.710 91.580 8.290 8.420 98.180 98.030 1.820 1.970 6.26 7.98 224 0.875 bicubic
476 densenet121 tf_mixnet_s 91.570 91.510 8.430 8.490 98.030 97.620 1.970 2.380 7.98 4.13 224 0.875 bicubic
477 tf_mixnet_s repvgg_b0 91.510 91.420 8.490 8.580 97.610 97.990 2.390 2.010 4.13 15.82 224 0.875 bicubic bilinear
repvgg_b0 91.450 8.550 97.980 2.020 15.82 224 0.875 bilinear
478 regnety_006 91.380 8.620 97.710 2.290 6.06 224 0.875 bicubic
479 mobilenetv3_large_100 hardcorenas_a 91.340 91.350 8.660 8.650 97.710 97.860 2.290 2.140 5.48 5.26 224 0.875 bicubic bilinear
480 hardcorenas_a mobilenetv3_large_100 91.340 91.330 8.660 8.670 97.860 97.710 2.140 2.290 5.26 5.48 224 0.875 bilinear bicubic
481 semnasnet_100 91.280 91.270 8.720 8.730 97.570 97.560 2.430 2.440 3.89 224 0.875 bicubic
482 tf_mobilenetv3_large_100 91.230 91.240 8.770 8.760 97.660 2.340 5.48 224 0.875 bilinear
483 mobilenetv3_rw 91.210 8.790 97.660 2.340 5.48 224 0.875 bicubic
efficientnet_es_pruned 91.190 8.810 97.740 2.260 5.44 224 0.875 bicubic
484 hrnet_w18_small_v2 91.190 8.810 97.900 2.100 15.60 224 0.875 bilinear
485 efficientnet_lite0 efficientnet_es_pruned 91.140 91.180 8.860 8.820 97.630 97.750 2.370 2.250 4.65 5.44 224 0.875 bicubic
486 resnet26 efficientnet_lite0 91.130 8.870 97.740 97.620 2.260 2.380 16.00 4.65 224 0.875 bicubic
487 resnet34 91.120 91.130 8.880 8.870 97.630 97.620 2.370 2.380 21.80 224 0.875 bilinear
488 resnet26 91.120 8.880 97.750 2.250 16.00 224 0.875 bicubic
489 regnetx_008 91.050 8.950 97.710 2.290 7.26 224 0.875 bicubic
490 tf_efficientnet_lite0 91.050 8.950 97.570 97.590 2.430 2.410 4.65 224 0.875 bicubic
491 xcit_nano_12_p8_224 90.990 91.010 9.010 8.990 97.800 2.200 3.05 224 1.000 bicubic
492 gluon_resnet34_v1b 90.990 90.960 9.010 9.040 97.650 97.640 2.350 2.360 21.80 224 0.875 bicubic
493 mobilenetv2_110d 90.970 90.960 9.030 9.040 97.560 2.440 4.52 224 0.875 bicubic
494 tinynet_b 90.930 9.070 97.670 2.330 3.73 188 0.875 bicubic
495 legacy_seresnet34 90.900 9.100 97.580 2.420 21.96 224 0.875 bilinear
496 tv_densenet121 pit_ti_distilled_224 90.900 9.100 97.700 97.710 2.300 2.290 7.98 5.10 224 0.875 0.900 bicubic
497 pit_ti_distilled_224 tv_densenet121 90.880 90.890 9.120 9.110 97.720 97.710 2.280 2.290 5.10 7.98 224 0.900 0.875 bicubic
498 dla34 90.770 9.230 97.650 97.660 2.350 2.340 15.74 224 0.875 bilinear
499 deit_tiny_distilled_patch16_224 90.730 90.710 9.270 9.290 97.580 97.570 2.420 2.430 5.91 224 0.900 bicubic
500 fbnetc_100 90.720 90.710 9.280 9.290 97.210 2.790 5.57 224 0.875 bilinear
501 swsl_resnet18 90.680 90.690 9.320 9.310 97.710 97.700 2.290 2.300 11.69 224 0.875 bilinear
502 crossvit_9_240 90.660 90.640 9.340 9.360 97.740 2.260 8.55 240 0.875 bicubic
503 convit_tiny 90.610 90.640 9.390 9.360 97.730 97.740 2.270 2.260 5.71 224 0.875 bicubic
504 mnasnet_100 90.510 9.490 97.470 2.530 4.38 224 0.875 bicubic
505 regnety_004 90.480 90.490 9.520 9.510 97.560 97.540 2.440 2.460 4.34 224 0.875 bicubic
506 regnetx_006 90.350 9.650 97.430 2.570 6.20 224 0.875 bicubic
507 spnasnet_100 90.350 9.650 97.190 2.810 4.42 224 0.875 bilinear
508 regnetx_006 crossvit_tiny_240 90.320 90.250 9.680 9.750 97.430 97.590 2.570 2.410 6.20 7.01 224 240 0.875 bicubic
509 crossvit_tiny_240 ssl_resnet18 90.250 90.220 9.750 9.780 97.610 97.550 2.390 2.450 7.01 11.69 240 224 0.875 bicubic bilinear
ssl_resnet18 90.230 9.770 97.560 2.440 11.69 224 0.875 bilinear
vgg19_bn 90.100 9.900 97.580 2.420 143.68 224 0.875 bilinear
510 vgg16_bn 90.090 9.910 97.370 2.630 138.37 224 0.875 bilinear
511 vgg19_bn 90.080 9.920 97.580 2.420 143.68 224 0.875 bilinear
512 semnasnet_075 90.080 9.920 97.430 2.570 2.91 224 0.875 bicubic
513 ghostnet_100 90.030 9.970 97.370 2.630 5.18 224 0.875 bilinear
514 pit_ti_224 89.930 89.950 10.070 10.050 97.440 97.450 2.560 2.550 4.85 224 0.900 bicubic
515 tv_resnet34 89.920 89.930 10.080 10.070 97.340 2.660 21.80 224 0.875 bilinear
516 vit_base_patch32_sam_224 vit_base_patch32_224_sam 89.750 10.250 97.000 3.000 88.22 224 0.900 bicubic
517 tf_mobilenetv3_large_075 89.710 89.680 10.290 10.320 97.220 97.210 2.780 2.790 3.99 224 0.875 bilinear
518 xcit_nano_12_p16_224_dist 89.680 10.320 97.090 2.910 3.05 224 1.000 bicubic
519 deit_tiny_patch16_224 89.670 10.330 97.440 2.560 5.72 224 0.900 bicubic
520 skresnet18 89.660 10.340 97.220 97.230 2.780 2.770 11.96 224 0.875 bicubic
521 mobilenetv2_100 89.610 10.390 97.150 2.850 3.50 224 0.875 bicubic
522 resnet18d 89.290 89.270 10.710 10.730 97.140 2.860 11.71 224 0.875 bicubic
523 vit_tiny_r_s16_p8_224 89.190 89.170 10.810 10.830 97.230 2.770 6.34 224 0.900 bicubic
524 vgg19 hrnet_w18_small 89.060 89.050 10.940 10.950 96.870 97.110 3.130 2.890 143.67 13.19 224 0.875 bilinear
525 hrnet_w18_small vgg19 89.060 89.040 10.940 10.960 97.100 96.870 2.900 3.130 13.19 143.67 224 0.875 bilinear
526 tf_mobilenetv3_large_minimal_100 88.950 88.960 11.050 11.040 96.870 96.860 3.130 3.140 3.92 224 0.875 bilinear
527 legacy_seresnet18 regnetx_004 88.880 88.900 11.120 11.100 96.970 97.120 3.030 2.880 11.78 5.16 224 0.875 bicubic
528 regnetx_004 legacy_seresnet18 88.880 11.120 97.120 96.980 2.880 3.020 5.16 11.78 224 0.875 bicubic
529 vgg13_bn lcnet_100 88.780 88.790 11.220 11.210 96.970 96.730 3.030 3.270 133.05 2.95 224 0.875 bilinear bicubic
530 xcit_nano_12_p16_224 vgg13_bn 88.590 88.760 11.410 11.240 96.790 96.970 3.210 3.030 3.05 133.05 224 1.000 0.875 bicubic bilinear
531 xcit_nano_12_p16_224 88.610 11.390 96.790 3.210 3.05 224 1.000 bicubic
532 vgg16 88.550 11.450 96.790 3.210 138.36 224 0.875 bilinear
533 gluon_resnet18_v1b 88.380 88.400 11.620 11.600 96.700 96.680 3.300 3.320 11.69 224 0.875 bicubic
534 vgg11_bn tinynet_c 87.520 87.770 12.480 12.230 96.810 96.370 3.190 3.630 132.87 2.46 224 184 0.875 bilinear bicubic
535 resnet18 vgg11_bn 87.370 87.500 12.630 12.500 96.260 96.820 3.740 3.180 11.69 132.87 224 0.875 bilinear
536 regnety_002 resnet18 87.360 87.390 12.640 12.610 96.570 96.290 3.430 3.710 3.16 11.69 224 0.875 bicubic bilinear
537 mixer_l16_224 regnety_002 87.160 87.380 12.840 12.620 93.530 96.590 6.470 3.410 208.20 3.16 224 0.875 bicubic
538 vgg13 mixer_l16_224 87.030 87.150 12.970 12.850 96.310 93.510 3.690 6.490 133.05 208.20 224 0.875 bilinear bicubic
539 vgg11 vgg13 86.580 87.050 13.420 12.950 96.290 96.320 3.710 3.680 132.86 133.05 224 0.875 bilinear
540 dla60x_c vgg11 86.280 86.550 13.720 13.450 96.160 96.280 3.840 3.720 1.32 132.86 224 0.875 bilinear
541 regnetx_002 dla60x_c 86.200 86.290 13.800 13.710 95.970 96.160 4.030 3.840 2.68 1.32 224 0.875 bicubic bilinear
542 tf_mobilenetv3_small_100 regnetx_002 85.210 86.190 14.790 13.810 95.780 95.980 4.220 4.020 2.54 2.68 224 0.875 bilinear bicubic
543 dla46x_c lcnet_075 84.250 85.990 15.750 14.010 95.280 95.680 4.720 4.320 1.07 2.36 224 0.875 bilinear bicubic
544 dla46_c tf_mobilenetv3_small_100 83.640 85.210 16.360 14.790 94.910 95.770 5.090 4.230 1.30 2.54 224 0.875 bilinear
545 tf_mobilenetv3_small_075 tinynet_d 83.480 84.750 16.520 15.250 94.800 95.180 5.200 4.820 2.04 2.34 224 152 0.875 bilinear bicubic
546 tf_mobilenetv3_small_minimal_100 dla46x_c 81.390 84.250 18.610 15.750 93.670 95.270 6.330 4.730 2.04 1.07 224 0.875 bilinear
547 mnasnet_small 83.990 16.010 94.920 5.080 2.03 224 0.875 bicubic
548 mobilenetv2_050 83.890 16.110 94.710 5.290 1.97 224 0.875 bicubic
549 dla46_c 83.650 16.350 94.920 5.080 1.30 224 0.875 bilinear
550 tf_mobilenetv3_small_075 83.510 16.490 94.800 5.200 2.04 224 0.875 bilinear
551 lcnet_050 81.780 18.220 93.710 6.290 1.88 224 0.875 bicubic
552 tf_mobilenetv3_small_minimal_100 81.380 18.620 93.670 6.330 2.04 224 0.875 bilinear
553 tinynet_e 78.900 21.100 92.560 7.440 2.04 106 0.875 bicubic

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model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation
beit_large_patch16_384,97.820,2.180,99.790,0.210,305.00,384,1.000,bicubic
beit_large_patch16_512,97.770,2.230,99.810,0.190,305.67,512,1.000,bicubic
tf_efficientnet_l2_ns,97.770,2.230,99.890,0.110,480.31,800,0.960,bicubic
beit_large_patch16_384,97.810,2.190,99.790,0.210,305.00,384,1.000,bicubic
beit_large_patch16_512,97.780,2.220,99.820,0.180,305.67,512,1.000,bicubic
tf_efficientnet_l2_ns,97.780,2.220,99.890,0.110,480.31,800,0.960,bicubic
tf_efficientnet_l2_ns_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic
beit_large_patch16_224,97.470,2.530,99.690,0.310,304.43,224,0.900,bicubic
convnext_xlarge_384_in22ft1k,97.550,2.450,99.800,0.200,350.20,384,1.000,bicubic
beit_large_patch16_224,97.480,2.520,99.690,0.310,304.43,224,0.900,bicubic
convnext_large_384_in22ft1k,97.440,2.560,99.780,0.220,197.77,384,1.000,bicubic
vit_large_patch16_384,97.420,2.580,99.780,0.220,304.72,384,1.000,bicubic
beit_base_patch16_384,97.350,2.650,99.710,0.290,86.74,384,1.000,bicubic
tf_efficientnet_b7_ns,97.210,2.790,99.700,0.300,66.35,600,0.949,bicubic
swin_large_patch4_window12_384,97.180,2.820,99.690,0.310,196.74,384,1.000,bicubic
beit_base_patch16_384,97.330,2.670,99.720,0.280,86.74,384,1.000,bicubic
convnext_base_384_in22ft1k,97.280,2.720,99.770,0.230,88.59,384,1.000,bicubic
convnext_large_in22ft1k,97.260,2.740,99.650,0.350,197.77,224,0.875,bicubic
convnext_xlarge_in22ft1k,97.240,2.760,99.730,0.270,350.20,224,0.875,bicubic
tf_efficientnet_b7_ns,97.200,2.800,99.700,0.300,66.35,600,0.949,bicubic
swin_large_patch4_window12_384,97.180,2.820,99.680,0.320,196.74,384,1.000,bicubic
tf_efficientnetv2_xl_in21ft1k,97.150,2.850,99.620,0.380,208.12,512,1.000,bicubic
tf_efficientnetv2_l_in21ft1k,97.110,2.890,99.700,0.300,118.52,480,1.000,bicubic
swin_base_patch4_window12_384,97.070,2.930,99.770,0.230,87.90,384,1.000,bicubic
tf_efficientnet_b6_ns,97.030,2.970,99.710,0.290,43.04,528,0.942,bicubic
vit_base_patch16_384,97.020,2.980,99.700,0.300,86.86,384,1.000,bicubic
ig_resnext101_32x48d,96.960,3.040,99.670,0.330,828.41,224,0.875,bilinear
swin_base_patch4_window12_384,97.120,2.880,99.780,0.220,87.90,384,1.000,bicubic
tf_efficientnetv2_l_in21ft1k,97.110,2.890,99.710,0.290,118.52,480,1.000,bicubic
vit_base_patch8_224,97.080,2.920,99.620,0.380,86.58,224,0.900,bicubic
tf_efficientnet_b6_ns,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic
vit_base_patch16_384,97.020,2.980,99.710,0.290,86.86,384,1.000,bicubic
ig_resnext101_32x48d,96.970,3.030,99.670,0.330,828.41,224,0.875,bilinear
tf_efficientnetv2_m_in21ft1k,96.970,3.030,99.610,0.390,54.14,480,1.000,bicubic
swin_large_patch4_window7_224,96.950,3.050,99.660,0.340,196.53,224,0.900,bicubic
tf_efficientnetv2_m_in21ft1k,96.950,3.050,99.610,0.390,54.14,480,1.000,bicubic
vit_large_r50_s32_384,96.950,3.050,99.720,0.280,329.09,384,1.000,bicubic
xcit_large_24_p16_384_dist,96.930,3.070,99.510,0.490,189.10,384,1.000,bicubic
dm_nfnet_f6,96.910,3.090,99.720,0.280,438.36,576,0.956,bicubic
vit_large_r50_s32_384,96.950,3.050,99.710,0.290,329.09,384,1.000,bicubic
xcit_large_24_p16_384_dist,96.940,3.060,99.510,0.490,189.10,384,1.000,bicubic
dm_nfnet_f6,96.920,3.080,99.720,0.280,438.36,576,0.956,bicubic
cait_m48_448,96.880,3.120,99.620,0.380,356.46,448,1.000,bicubic
resnetv2_152x4_bitm,96.880,3.120,99.660,0.340,936.53,480,1.000,bilinear
tf_efficientnet_b5_ns,96.880,3.120,99.640,0.360,30.39,456,0.934,bicubic
cait_m48_448,96.870,3.130,99.620,0.380,356.46,448,1.000,bicubic
xcit_small_24_p8_384_dist,96.830,3.170,99.630,0.370,47.63,384,1.000,bicubic
cait_m36_384,96.820,3.180,99.660,0.340,271.22,384,1.000,bicubic
dm_nfnet_f5,96.800,3.200,99.670,0.330,377.21,544,0.954,bicubic
dm_nfnet_f4,96.780,3.220,99.620,0.380,316.07,512,0.951,bicubic
tf_efficientnet_b5_ns,96.870,3.130,99.640,0.360,30.39,456,0.934,bicubic
convnext_base_in22ft1k,96.860,3.140,99.650,0.350,88.59,224,0.875,bicubic
cait_m36_384,96.830,3.170,99.660,0.340,271.22,384,1.000,bicubic
dm_nfnet_f5,96.810,3.190,99.670,0.330,377.21,544,0.954,bicubic
xcit_small_24_p8_384_dist,96.810,3.190,99.630,0.370,47.63,384,1.000,bicubic
xcit_medium_24_p8_384_dist,96.780,3.220,99.610,0.390,84.32,384,1.000,bicubic
ig_resnext101_32x32d,96.780,3.220,99.530,0.470,468.53,224,0.875,bilinear
xcit_medium_24_p8_384_dist,96.780,3.220,99.620,0.380,84.32,384,1.000,bicubic
dm_nfnet_f4,96.780,3.220,99.620,0.380,316.07,512,0.951,bicubic
xcit_large_24_p8_384_dist,96.760,3.240,99.560,0.440,188.93,384,1.000,bicubic
dm_nfnet_f3,96.720,3.280,99.630,0.370,254.92,416,0.940,bicubic
dm_nfnet_f3,96.730,3.270,99.630,0.370,254.92,416,0.940,bicubic
tf_efficientnet_b4_ns,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic
vit_large_patch16_224,96.710,3.290,99.650,0.350,304.33,224,0.900,bicubic
tf_efficientnet_b8,96.700,3.300,99.550,0.450,87.41,672,0.954,bicubic
xcit_medium_24_p16_384_dist,96.690,3.310,99.600,0.400,84.40,384,1.000,bicubic
swin_base_patch4_window7_224,96.670,3.330,99.670,0.330,87.77,224,0.900,bicubic
beit_base_patch16_224,96.650,3.350,99.660,0.340,86.53,224,0.900,bicubic
tf_efficientnetv2_l,96.650,3.350,99.570,0.430,118.52,480,1.000,bicubic
cait_s36_384,96.630,3.370,99.590,0.410,68.37,384,1.000,bicubic
xcit_large_24_p8_224_dist,96.620,3.380,99.460,0.540,188.93,224,1.000,bicubic
cait_s24_384,96.580,3.420,99.550,0.450,47.06,384,1.000,bicubic
tf_efficientnet_b8,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic
xcit_medium_24_p16_384_dist,96.700,3.300,99.600,0.400,84.40,384,1.000,bicubic
swin_base_patch4_window7_224,96.680,3.320,99.660,0.340,87.77,224,0.900,bicubic
beit_base_patch16_224,96.660,3.340,99.660,0.340,86.53,224,0.900,bicubic
tf_efficientnetv2_l,96.650,3.350,99.560,0.440,118.52,480,1.000,bicubic
xcit_large_24_p8_224_dist,96.640,3.360,99.460,0.540,188.93,224,1.000,bicubic
cait_s36_384,96.630,3.370,99.600,0.400,68.37,384,1.000,bicubic
regnetz_e8,96.600,3.400,99.610,0.390,57.70,320,1.000,bicubic
tf_efficientnet_b7,96.580,3.420,99.510,0.490,66.35,600,0.949,bicubic
tf_efficientnet_b8_ap,96.560,3.440,99.550,0.450,87.41,672,0.954,bicubic
tf_efficientnetv2_m,96.550,3.450,99.570,0.430,54.14,480,1.000,bicubic
xcit_small_24_p8_224_dist,96.550,3.450,99.560,0.440,47.63,224,1.000,bicubic
cait_s24_384,96.570,3.430,99.550,0.450,47.06,384,1.000,bicubic
tf_efficientnet_b8_ap,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic
xcit_small_24_p8_224_dist,96.550,3.450,99.570,0.430,47.63,224,1.000,bicubic
tf_efficientnetv2_m,96.540,3.460,99.570,0.430,54.14,480,1.000,bicubic
resnetv2_152x2_bitm,96.520,3.480,99.590,0.410,236.34,448,1.000,bilinear
xcit_medium_24_p8_224_dist,96.500,3.500,99.500,0.500,84.32,224,1.000,bicubic
deit_base_distilled_patch16_384,96.490,3.510,99.590,0.410,87.63,384,1.000,bicubic
xcit_small_12_p8_384_dist,96.480,3.520,99.480,0.520,26.21,384,1.000,bicubic
vit_base_r50_s16_384,96.470,3.530,99.660,0.340,98.95,384,1.000,bicubic
xcit_medium_24_p8_224_dist,96.520,3.480,99.510,0.490,84.32,224,1.000,bicubic
deit_base_distilled_patch16_384,96.510,3.490,99.590,0.410,87.63,384,1.000,bicubic
xcit_small_12_p8_384_dist,96.480,3.520,99.490,0.510,26.21,384,1.000,bicubic
tf_efficientnetv2_s_in21ft1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic
dm_nfnet_f2,96.460,3.540,99.540,0.460,193.78,352,0.920,bicubic
ecaresnet269d,96.460,3.540,99.610,0.390,102.09,352,1.000,bicubic
tf_efficientnetv2_s_in21ft1k,96.460,3.540,99.570,0.430,21.46,384,1.000,bicubic
eca_nfnet_l2,96.460,3.540,99.630,0.370,56.72,384,1.000,bicubic
dm_nfnet_f2,96.450,3.550,99.540,0.460,193.78,352,0.920,bicubic
ig_resnext101_32x16d,96.430,3.570,99.540,0.460,194.03,224,0.875,bilinear
eca_nfnet_l2,96.450,3.550,99.630,0.370,56.72,384,1.000,bicubic
vit_base_r50_s16_384,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic
ig_resnext101_32x16d,96.440,3.560,99.540,0.460,194.03,224,0.875,bilinear
resnetrs420,96.400,3.600,99.540,0.460,191.89,416,1.000,bicubic
dm_nfnet_f1,96.370,3.630,99.480,0.520,132.63,320,0.910,bicubic
tf_efficientnet_b6_ap,96.360,3.640,99.550,0.450,43.04,528,0.942,bicubic
xcit_small_24_p16_384_dist,96.360,3.640,99.590,0.410,47.67,384,1.000,bicubic
tf_efficientnet_b7_ap,96.350,3.650,99.600,0.400,66.35,600,0.949,bicubic
resmlp_big_24_224_in22ft1k,96.340,3.660,99.510,0.490,129.14,224,0.875,bicubic
seresnet152d,96.330,3.670,99.510,0.490,66.84,320,1.000,bicubic
dm_nfnet_f1,96.380,3.620,99.470,0.530,132.63,320,0.910,bicubic
tf_efficientnet_b6_ap,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic
resmlp_big_24_224_in22ft1k,96.350,3.650,99.520,0.480,129.14,224,0.875,bicubic
tf_efficientnet_b7_ap,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic
xcit_small_24_p16_384_dist,96.340,3.660,99.580,0.420,47.67,384,1.000,bicubic
xcit_small_12_p16_384_dist,96.330,3.670,99.490,0.510,26.25,384,1.000,bicubic
xcit_large_24_p16_224_dist,96.310,3.690,99.500,0.500,189.10,224,1.000,bicubic
xcit_large_24_p16_224_dist,96.320,3.680,99.500,0.500,189.10,224,1.000,bicubic
seresnet152d,96.310,3.690,99.510,0.490,66.84,320,1.000,bicubic
vit_base_patch16_224,96.300,3.700,99.560,0.440,86.57,224,0.900,bicubic
resnetv2_50x3_bitm,96.290,3.710,99.630,0.370,217.32,448,1.000,bilinear
resnetv2_101x3_bitm,96.290,3.710,99.580,0.420,387.93,448,1.000,bilinear
tf_efficientnet_b6,96.280,3.720,99.520,0.480,43.04,528,0.942,bicubic
tf_efficientnet_b6,96.290,3.710,99.520,0.480,43.04,528,0.942,bicubic
swsl_resnext101_32x16d,96.280,3.720,99.500,0.500,194.03,224,0.875,bilinear
efficientnetv2_rw_m,96.270,3.730,99.560,0.440,53.24,416,1.000,bicubic
resnetv2_50x3_bitm,96.270,3.730,99.630,0.370,217.32,448,1.000,bilinear
xcit_medium_24_p16_224_dist,96.260,3.740,99.400,0.600,84.40,224,1.000,bicubic
xcit_tiny_24_p8_384_dist,96.250,3.750,99.440,0.560,12.11,384,1.000,bicubic
resnetv2_101x3_bitm,96.250,3.750,99.580,0.420,387.93,448,1.000,bilinear
resnetrs350,96.240,3.760,99.470,0.530,163.96,384,1.000,bicubic
swsl_resnext101_32x8d,96.230,3.770,99.590,0.410,88.79,224,0.875,bilinear
vit_large_r50_s32_224,96.190,3.810,99.530,0.470,328.99,224,0.900,bicubic
resnetv2_152x2_bit_teacher_384,96.170,3.830,99.510,0.490,236.34,384,1.000,bicubic
resnest269e,96.120,3.880,99.520,0.480,110.93,416,0.928,bicubic
crossvit_18_dagger_408,96.110,3.890,99.470,0.530,44.61,408,1.000,bicubic
swsl_resnext101_32x8d,96.240,3.760,99.590,0.410,88.79,224,0.875,bilinear
xcit_tiny_24_p8_384_dist,96.240,3.760,99.440,0.560,12.11,384,1.000,bicubic
resnetv2_152x2_bit_teacher_384,96.190,3.810,99.500,0.500,236.34,384,1.000,bicubic
vit_large_r50_s32_224,96.180,3.820,99.530,0.470,328.99,224,0.900,bicubic
crossvit_18_dagger_408,96.130,3.870,99.470,0.530,44.61,408,1.000,bicubic
resnest269e,96.130,3.870,99.520,0.480,110.93,416,0.928,bicubic
resnet200d,96.110,3.890,99.460,0.540,64.69,320,1.000,bicubic
tf_efficientnet_b3_ns,96.110,3.890,99.470,0.530,12.23,300,0.904,bicubic
tf_efficientnet_b3_ns,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic
tf_efficientnet_b5_ap,96.080,3.920,99.540,0.460,30.39,456,0.934,bicubic
resnest200e,96.080,3.920,99.470,0.530,70.20,320,0.909,bicubic
pit_b_distilled_224,96.080,3.920,99.380,0.620,74.79,224,0.900,bicubic
resnetrs270,96.070,3.930,99.480,0.520,129.86,352,1.000,bicubic
xcit_large_24_p8_224,96.060,3.940,99.150,0.850,188.93,224,1.000,bicubic
xcit_large_24_p8_224,96.080,3.920,99.150,0.850,188.93,224,1.000,bicubic
pit_b_distilled_224,96.070,3.930,99.380,0.620,74.79,224,0.900,bicubic
resnest200e,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic
resnetrs270,96.060,3.940,99.480,0.520,129.86,352,1.000,bicubic
vit_small_r26_s32_384,96.060,3.940,99.550,0.450,36.47,384,1.000,bicubic
swsl_resnext101_32x4d,96.050,3.950,99.540,0.460,44.18,224,0.875,bilinear
vit_base_patch16_224_miil,96.040,3.960,99.350,0.650,86.54,224,0.875,bilinear
swsl_resnext101_32x4d,96.040,3.960,99.530,0.470,44.18,224,0.875,bilinear
vit_base_patch16_224_miil,96.030,3.970,99.350,0.650,86.54,224,0.875,bilinear
convnext_large,96.020,3.980,99.470,0.530,197.77,224,0.875,bicubic
cait_xs24_384,96.010,3.990,99.430,0.570,26.67,384,1.000,bicubic
resnetrs200,96.000,4.000,99.440,0.560,93.21,320,1.000,bicubic
regnetz_d8,96.010,3.990,99.520,0.480,23.37,320,1.000,bicubic
resnetrs200,95.990,4.010,99.440,0.560,93.21,320,1.000,bicubic
tf_efficientnet_b5,95.980,4.020,99.450,0.550,30.39,456,0.934,bicubic
vit_small_patch16_384,95.980,4.020,99.600,0.400,22.20,384,1.000,bicubic
vit_small_patch16_384,95.980,4.020,99.590,0.410,22.20,384,1.000,bicubic
resnetrs152,95.960,4.040,99.380,0.620,86.62,320,1.000,bicubic
xcit_small_12_p8_224_dist,95.960,4.040,99.420,0.580,26.21,224,1.000,bicubic
ig_resnext101_32x8d,95.950,4.050,99.390,0.610,88.79,224,0.875,bilinear
eca_nfnet_l1,95.920,4.080,99.500,0.500,41.41,320,1.000,bicubic
convnext_base,95.950,4.050,99.380,0.620,88.59,224,0.875,bicubic
ig_resnext101_32x8d,95.940,4.060,99.380,0.620,88.79,224,0.875,bilinear
eca_nfnet_l1,95.930,4.070,99.490,0.510,41.41,320,1.000,bicubic
xcit_small_24_p8_224,95.910,4.090,99.180,0.820,47.63,224,1.000,bicubic
vit_base_patch32_384,95.910,4.090,99.440,0.560,88.30,384,1.000,bicubic
regnety_160,95.900,4.100,99.560,0.440,83.59,288,1.000,bicubic
xcit_medium_24_p8_224,95.870,4.130,99.090,0.910,84.32,224,1.000,bicubic
vit_base_patch32_384,95.900,4.100,99.440,0.560,88.30,384,1.000,bicubic
regnety_160,95.880,4.120,99.560,0.440,83.59,288,1.000,bicubic
resmlp_big_24_distilled_224,95.870,4.130,99.440,0.560,129.14,224,0.875,bicubic
regnetz_d,95.860,4.140,99.440,0.560,27.58,320,0.950,bicubic
resnet152d,95.850,4.150,99.430,0.570,60.21,320,1.000,bicubic
crossvit_15_dagger_408,95.820,4.180,99.300,0.700,28.50,408,1.000,bicubic
xcit_small_24_p16_224_dist,95.810,4.190,99.340,0.660,47.67,224,1.000,bicubic
deit_base_distilled_patch16_224,95.780,4.220,99.280,0.720,87.34,224,0.900,bicubic
resnet152d,95.870,4.130,99.430,0.570,60.21,320,1.000,bicubic
xcit_medium_24_p8_224,95.870,4.130,99.080,0.920,84.32,224,1.000,bicubic
regnetz_d32,95.860,4.140,99.430,0.570,27.58,320,0.950,bicubic
crossvit_15_dagger_408,95.820,4.180,99.310,0.690,28.50,408,1.000,bicubic
xcit_small_24_p16_224_dist,95.800,4.200,99.340,0.660,47.67,224,1.000,bicubic
deit_base_distilled_patch16_224,95.750,4.250,99.280,0.720,87.34,224,0.900,bicubic
resnet101d,95.750,4.250,99.440,0.560,44.57,320,1.000,bicubic
xcit_small_12_p16_224_dist,95.750,4.250,99.290,0.710,26.25,224,1.000,bicubic
resnetv2_152x2_bit_teacher,95.730,4.270,99.430,0.570,236.34,224,0.875,bicubic
twins_pcpvt_large,95.720,4.280,99.490,0.510,60.99,224,0.900,bicubic
resnetv2_152x2_bit_teacher,95.750,4.250,99.430,0.570,236.34,224,0.875,bicubic
xcit_small_12_p16_224_dist,95.740,4.260,99.300,0.700,26.25,224,1.000,bicubic
twins_svt_large,95.720,4.280,99.370,0.630,99.27,224,0.900,bicubic
twins_pcpvt_large,95.720,4.280,99.490,0.510,60.99,224,0.900,bicubic
swin_small_patch4_window7_224,95.720,4.280,99.290,0.710,49.61,224,0.900,bicubic
tf_efficientnetv2_s,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic
dm_nfnet_f0,95.710,4.290,99.330,0.670,71.49,256,0.900,bicubic
efficientnetv2_rw_s,95.700,4.300,99.380,0.620,23.94,384,1.000,bicubic
deit_base_patch16_384,95.660,4.340,99.240,0.760,86.86,384,1.000,bicubic
cait_s24_224,95.640,4.360,99.390,0.610,46.92,224,1.000,bicubic
tf_efficientnet_b4,95.590,4.410,99.320,0.680,19.34,380,0.922,bicubic
swsl_resnext50_32x4d,95.590,4.410,99.440,0.560,25.03,224,0.875,bilinear
resnest101e,95.580,4.420,99.270,0.730,48.28,256,0.875,bilinear
dm_nfnet_f0,95.690,4.310,99.330,0.670,71.49,256,0.900,bicubic
cait_s24_224,95.650,4.350,99.390,0.610,46.92,224,1.000,bicubic
deit_base_patch16_384,95.650,4.350,99.240,0.760,86.86,384,1.000,bicubic
convnext_small,95.630,4.370,99.260,0.740,50.22,224,0.875,bicubic
swsl_resnext50_32x4d,95.600,4.400,99.440,0.560,25.03,224,0.875,bilinear
tf_efficientnet_b4,95.590,4.410,99.330,0.670,19.34,380,0.922,bicubic
twins_svt_base,95.570,4.430,99.230,0.770,56.07,224,0.900,bicubic
efficientnet_b4,95.540,4.460,99.400,0.600,19.34,384,1.000,bicubic
jx_nest_small,95.540,4.460,99.230,0.770,38.35,224,0.875,bicubic
jx_nest_base,95.530,4.470,99.300,0.700,67.72,224,0.875,bicubic
tf_efficientnet_b2_ns,95.530,4.470,99.340,0.660,9.11,260,0.890,bicubic
tresnet_xl_448,95.520,4.480,99.340,0.660,78.44,448,0.875,bilinear
tf_efficientnet_b4_ap,95.500,4.500,99.390,0.610,19.34,380,0.922,bicubic
regnety_032,95.470,4.530,99.320,0.680,19.44,288,1.000,bicubic
twins_pcpvt_base,95.470,4.530,99.380,0.620,43.83,224,0.900,bicubic
xcit_tiny_24_p16_384_dist,95.460,4.540,99.350,0.650,12.12,384,1.000,bicubic
xcit_tiny_24_p8_224_dist,95.460,4.540,99.370,0.630,12.11,224,1.000,bicubic
resnest101e,95.560,4.440,99.270,0.730,48.28,256,0.875,bilinear
resnet152,95.560,4.440,99.270,0.730,60.19,224,0.950,bicubic
jx_nest_base,95.540,4.460,99.300,0.700,67.72,224,0.875,bicubic
jx_nest_small,95.540,4.460,99.220,0.780,38.35,224,0.875,bicubic
efficientnet_b4,95.520,4.480,99.400,0.600,19.34,384,1.000,bicubic
tf_efficientnet_b2_ns,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic
tresnet_xl_448,95.510,4.490,99.340,0.660,78.44,448,0.875,bilinear
tf_efficientnet_b4_ap,95.490,4.510,99.390,0.610,19.34,380,0.922,bicubic
regnety_032,95.480,4.520,99.320,0.680,19.44,288,1.000,bicubic
xcit_tiny_24_p16_384_dist,95.480,4.520,99.360,0.640,12.12,384,1.000,bicubic
twins_pcpvt_base,95.460,4.540,99.390,0.610,43.83,224,0.900,bicubic
xcit_tiny_24_p8_224_dist,95.460,4.540,99.360,0.640,12.11,224,1.000,bicubic
eca_nfnet_l0,95.450,4.550,99.390,0.610,24.14,288,1.000,bicubic
nfnet_l0,95.420,4.580,99.430,0.570,35.07,288,1.000,bicubic
xcit_small_12_p8_224,95.420,4.580,99.200,0.800,26.21,224,1.000,bicubic
regnetz_c,95.410,4.590,99.310,0.690,13.46,320,0.940,bicubic
ssl_resnext101_32x16d,95.400,4.600,99.410,0.590,194.03,224,0.875,bilinear
tresnet_m,95.400,4.600,99.150,0.850,31.39,224,0.875,bilinear
ssl_resnext101_32x16d,95.410,4.590,99.410,0.590,194.03,224,0.875,bilinear
tresnet_l_448,95.400,4.600,99.300,0.700,55.99,448,0.875,bilinear
nfnet_l0,95.390,4.610,99.420,0.580,35.07,288,1.000,bicubic
regnetz_c16,95.390,4.610,99.310,0.690,13.46,320,0.940,bicubic
resnetv2_50x1_bit_distilled,95.390,4.610,99.430,0.570,25.55,224,0.875,bicubic
tresnet_l_448,95.390,4.610,99.280,0.720,55.99,448,0.875,bilinear
pnasnet5large,95.370,4.630,99.130,0.870,86.06,331,0.911,bicubic
tresnet_m,95.380,4.620,99.150,0.850,31.39,224,0.875,bilinear
pnasnet5large,95.360,4.640,99.130,0.870,86.06,331,0.911,bicubic
xcit_tiny_12_p8_384_dist,95.340,4.660,99.340,0.660,6.71,384,1.000,bicubic
resnetv2_101x1_bitm,95.330,4.670,99.380,0.620,44.54,448,1.000,bilinear
ssl_resnext101_32x8d,95.320,4.680,99.310,0.690,88.79,224,0.875,bilinear
ssl_resnext101_32x8d,95.330,4.670,99.310,0.690,88.79,224,0.875,bilinear
resnetv2_101x1_bitm,95.320,4.680,99.370,0.630,44.54,448,1.000,bilinear
gc_efficientnetv2_rw_t,95.280,4.720,99.220,0.780,13.68,288,1.000,bicubic
vit_large_patch32_384,95.250,4.750,99.320,0.680,306.63,384,1.000,bicubic
cait_xxs36_384,95.240,4.760,99.330,0.670,17.37,384,1.000,bicubic
resnetrs101,95.230,4.770,99.210,0.790,63.62,288,0.940,bicubic
swsl_resnet50,95.220,4.780,99.400,0.600,25.56,224,0.875,bilinear
levit_384,95.200,4.800,99.160,0.840,39.13,224,0.900,bicubic
resnet51q,95.190,4.810,99.280,0.720,35.70,288,1.000,bilinear
crossvit_18_dagger_240,95.180,4.820,99.120,0.880,44.27,240,0.875,bicubic
nasnetalarge,95.170,4.830,99.130,0.870,88.75,331,0.911,bicubic
resnetrs101,95.250,4.750,99.210,0.790,63.62,288,0.940,bicubic
vit_large_patch32_384,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic
cait_xxs36_384,95.230,4.770,99.320,0.680,17.37,384,1.000,bicubic
levit_384,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic
resnet51q,95.200,4.800,99.280,0.720,35.70,288,1.000,bilinear
swsl_resnet50,95.200,4.800,99.390,0.610,25.56,224,0.875,bilinear
crossvit_18_dagger_240,95.190,4.810,99.120,0.880,44.27,240,0.875,bicubic
ecaresnet101d,95.160,4.840,99.230,0.770,44.57,224,0.875,bicubic
efficientnet_b3,95.150,4.850,99.210,0.790,12.23,320,1.000,bicubic
ssl_resnext101_32x4d,95.140,4.860,99.310,0.690,44.18,224,0.875,bilinear
xcit_medium_24_p16_224,95.130,4.870,98.930,1.070,84.40,224,1.000,bicubic
tf_efficientnetv2_b3,95.120,4.880,99.190,0.810,14.36,300,0.904,bicubic
vit_small_r26_s32_224,95.120,4.880,99.220,0.780,36.43,224,0.900,bicubic
coat_lite_small,95.110,4.890,99.030,0.970,19.84,224,0.900,bicubic
ecaresnet50t,95.110,4.890,99.290,0.710,25.57,320,0.950,bicubic
ssl_resnext101_32x4d,95.160,4.840,99.310,0.690,44.18,224,0.875,bilinear
nasnetalarge,95.150,4.850,99.130,0.870,88.75,331,0.911,bicubic
efficientnet_b3,95.130,4.870,99.210,0.790,12.23,320,1.000,bicubic
fbnetv3_g,95.130,4.870,99.200,0.800,16.62,288,0.950,bilinear
vit_small_r26_s32_224,95.130,4.870,99.220,0.780,36.43,224,0.900,bicubic
xcit_medium_24_p16_224,95.130,4.870,98.920,1.080,84.40,224,1.000,bicubic
tf_efficientnetv2_b3,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic
resnet61q,95.110,4.890,99.080,0.920,36.85,288,1.000,bicubic
convit_base,95.100,4.900,99.130,0.870,86.54,224,0.875,bicubic
tresnet_xl,95.080,4.920,99.250,0.750,78.44,224,0.875,bilinear
efficientnetv2_rw_t,95.080,4.920,99.220,0.780,13.65,288,1.000,bicubic
xcit_small_24_p16_224,95.070,4.930,99.060,0.940,47.67,224,1.000,bicubic
crossvit_base_240,95.060,4.940,98.980,1.020,105.03,240,0.875,bicubic
crossvit_18_240,95.050,4.950,99.120,0.880,43.27,240,0.875,bicubic
deit_base_patch16_224,95.020,4.980,98.970,1.030,86.57,224,0.900,bicubic
visformer_small,94.970,5.030,99.210,0.790,40.22,224,0.900,bicubic
crossvit_15_dagger_240,94.970,5.030,99.150,0.850,28.21,240,0.875,bicubic
tf_efficientnet_b3_ap,94.960,5.040,99.110,0.890,12.23,300,0.904,bicubic
convit_base,95.100,4.900,99.140,0.860,86.54,224,0.875,bicubic
xcit_small_24_p16_224,95.080,4.920,99.060,0.940,47.67,224,1.000,bicubic
coat_lite_small,95.080,4.920,99.030,0.970,19.84,224,0.900,bicubic
crossvit_18_240,95.070,4.930,99.120,0.880,43.27,240,0.875,bicubic
crossvit_base_240,95.070,4.930,98.980,1.020,105.03,240,0.875,bicubic
ecaresnet50t,95.070,4.930,99.290,0.710,25.57,320,0.950,bicubic
efficientnetv2_rw_t,95.070,4.930,99.220,0.780,13.65,288,1.000,bicubic
tresnet_xl,95.060,4.940,99.260,0.740,78.44,224,0.875,bilinear
halo2botnet50ts_256,95.010,4.990,99.040,0.960,22.64,256,0.950,bicubic
deit_base_patch16_224,95.010,4.990,98.980,1.020,86.57,224,0.900,bicubic
convnext_tiny,94.990,5.010,99.200,0.800,28.59,224,0.875,bicubic
crossvit_15_dagger_240,94.980,5.020,99.160,0.840,28.21,240,0.875,bicubic
convmixer_1536_20,94.970,5.030,99.170,0.830,51.63,224,0.960,bicubic
resnet101,94.970,5.030,99.080,0.920,44.55,224,0.950,bicubic
tf_efficientnet_b3_ap,94.970,5.030,99.110,0.890,12.23,300,0.904,bicubic
visformer_small,94.960,5.040,99.210,0.790,40.22,224,0.900,bicubic
xcit_large_24_p16_224,94.950,5.050,98.830,1.170,189.10,224,1.000,bicubic
convmixer_1536_20,94.950,5.050,99.170,0.830,51.63,224,0.960,bicubic
jx_nest_tiny,94.950,5.050,99.100,0.900,17.06,224,0.875,bicubic
cait_xxs24_384,94.940,5.060,99.130,0.870,12.03,384,1.000,bicubic
jx_nest_tiny,94.940,5.060,99.100,0.900,17.06,224,0.875,bicubic
resnetv2_101,94.930,5.070,99.110,0.890,44.54,224,0.950,bicubic
convit_small,94.920,5.080,99.120,0.880,27.78,224,0.875,bicubic
gernet_l,94.920,5.080,99.200,0.800,31.08,256,0.875,bilinear
tf_efficientnet_b3,94.910,5.090,99.100,0.900,12.23,300,0.904,bicubic
vit_small_patch16_224,94.900,5.100,99.280,0.720,22.05,224,0.900,bicubic
gernet_l,94.930,5.070,99.200,0.800,31.08,256,0.875,bilinear
resnetv2_101,94.930,5.070,99.120,0.880,44.54,224,0.950,bicubic
convit_small,94.920,5.080,99.100,0.900,27.78,224,0.875,bicubic
tf_efficientnet_b3,94.910,5.090,99.110,0.890,12.23,300,0.904,bicubic
tresnet_l,94.900,5.100,99.030,0.970,55.99,224,0.875,bilinear
mixer_b16_224_miil,94.890,5.110,99.080,0.920,59.88,224,0.875,bilinear
tf_efficientnet_b1_ns,94.880,5.120,99.250,0.750,7.79,240,0.882,bicubic
xcit_tiny_24_p8_224,94.870,5.130,99.190,0.810,12.11,224,1.000,bicubic
mixer_b16_224_miil,94.880,5.120,99.080,0.920,59.88,224,0.875,bilinear
vit_small_patch16_224,94.880,5.120,99.270,0.730,22.05,224,0.900,bicubic
xcit_tiny_24_p8_224,94.880,5.120,99.190,0.810,12.11,224,1.000,bicubic
tf_efficientnet_lite4,94.870,5.130,99.090,0.910,13.01,380,0.920,bilinear
xcit_small_12_p16_224,94.810,5.190,99.060,0.940,26.25,224,1.000,bicubic
seresnext50_32x4d,94.800,5.200,99.130,0.870,27.56,224,0.875,bicubic
pit_b_224,94.790,5.210,98.810,1.190,73.76,224,0.900,bicubic
tf_efficientnet_b1_ns,94.860,5.140,99.250,0.750,7.79,240,0.882,bicubic
xcit_small_12_p16_224,94.820,5.180,99.060,0.940,26.25,224,1.000,bicubic
seresnext50_32x4d,94.810,5.190,99.130,0.870,27.56,224,0.875,bicubic
pit_b_224,94.790,5.210,98.820,1.180,73.76,224,0.900,bicubic
lamhalobotnet50ts_256,94.780,5.220,98.980,1.020,22.57,256,0.950,bicubic
twins_svt_small,94.770,5.230,99.080,0.920,24.06,224,0.900,bicubic
coat_mini,94.760,5.240,98.950,1.050,10.34,224,0.900,bicubic
resnetv2_50x1_bitm,94.760,5.240,99.180,0.820,25.55,448,1.000,bilinear
coat_mini,94.770,5.230,98.950,1.050,10.34,224,0.900,bicubic
resnetv2_50x1_bitm,94.750,5.250,99.180,0.820,25.55,448,1.000,bilinear
pit_s_distilled_224,94.750,5.250,99.180,0.820,24.04,224,0.900,bicubic
xcit_tiny_12_p8_224_dist,94.740,5.260,99.180,0.820,6.71,224,1.000,bicubic
legacy_senet154,94.730,5.270,99.100,0.900,115.09,224,0.875,bilinear
crossvit_15_240,94.720,5.280,99.080,0.920,27.53,240,0.875,bicubic
gluon_senet154,94.710,5.290,98.970,1.030,115.09,224,0.875,bicubic
halonet50ts,94.710,5.290,98.820,1.180,22.73,256,0.940,bicubic
resnest50d_4s2x40d,94.710,5.290,99.130,0.870,30.42,224,0.875,bicubic
xcit_tiny_12_p8_224_dist,94.710,5.290,99.180,0.820,6.71,224,1.000,bicubic
gluon_resnet152_v1s,94.700,5.300,99.060,0.940,60.32,224,0.875,bicubic
ssl_resnext50_32x4d,94.700,5.300,99.240,0.760,25.03,224,0.875,bilinear
gluon_senet154,94.700,5.300,98.970,1.030,115.09,224,0.875,bicubic
gluon_resnet152_v1s,94.690,5.310,99.050,0.950,60.32,224,0.875,bicubic
regnetz_b,94.690,5.310,99.160,0.840,9.72,288,0.940,bicubic
crossvit_15_240,94.680,5.320,99.070,0.930,27.53,240,0.875,bicubic
regnetz_b16,94.680,5.320,99.160,0.840,9.72,288,0.940,bicubic
efficientnet_el,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic
rexnet_200,94.670,5.330,99.090,0.910,16.37,224,0.875,bicubic
tresnet_m_448,94.670,5.330,99.170,0.830,31.39,448,0.875,bilinear
gluon_seresnext101_64x4d,94.660,5.340,98.970,1.030,88.23,224,0.875,bicubic
wide_resnet50_2,94.650,5.350,99.050,0.950,68.88,224,0.875,bicubic
swin_tiny_patch4_window7_224,94.640,5.360,99.120,0.880,28.29,224,0.900,bicubic
gluon_seresnext101_64x4d,94.660,5.340,98.980,1.020,88.23,224,0.875,bicubic
rexnet_200,94.660,5.340,99.090,0.910,16.37,224,0.875,bicubic
wide_resnet50_2,94.660,5.340,99.050,0.950,68.88,224,0.875,bicubic
tresnet_m_448,94.650,5.350,99.150,0.850,31.39,448,0.875,bilinear
resnest50d,94.620,5.380,99.030,0.970,27.48,224,0.875,bilinear
gcresnet50t,94.610,5.390,98.990,1.010,25.90,256,0.900,bicubic
lamhalobotnet50ts_256,94.610,5.390,98.610,1.390,22.57,256,0.950,bicubic
twins_pcpvt_small,94.600,5.400,99.140,0.860,24.11,224,0.900,bicubic
deit_small_distilled_patch16_224,94.590,5.410,99.090,0.910,22.44,224,0.900,bicubic
pit_s_224,94.590,5.410,98.930,1.070,23.46,224,0.900,bicubic
crossvit_small_240,94.590,5.410,99.120,0.880,26.86,240,0.875,bicubic
swin_tiny_patch4_window7_224,94.620,5.380,99.120,0.880,28.29,224,0.900,bicubic
gcresnet50t,94.620,5.380,98.980,1.020,25.90,256,0.900,bicubic
deit_small_distilled_patch16_224,94.600,5.400,99.100,0.900,22.44,224,0.900,bicubic
twins_pcpvt_small,94.600,5.400,99.150,0.850,24.11,224,0.900,bicubic
vit_small_patch32_384,94.600,5.400,99.140,0.860,22.92,384,1.000,bicubic
pit_s_224,94.590,5.410,98.920,1.080,23.46,224,0.900,bicubic
crossvit_small_240,94.580,5.420,99.120,0.880,26.86,240,0.875,bicubic
efficientnet_b3_pruned,94.580,5.420,99.070,0.930,9.86,300,0.904,bicubic
lambda_resnet50ts,94.570,5.430,98.650,1.350,21.54,256,0.950,bicubic
resnext50_32x4d,94.570,5.430,98.800,1.200,25.03,224,0.950,bicubic
tnt_s_patch16_224,94.570,5.430,99.180,0.820,23.76,224,0.900,bicubic
vit_small_patch32_384,94.570,5.430,99.140,0.860,22.92,384,1.000,bicubic
lambda_resnet50ts,94.560,5.440,98.650,1.350,21.54,256,0.950,bicubic
repvgg_b3,94.560,5.440,98.910,1.090,123.09,224,0.875,bilinear
gernet_m,94.550,5.450,98.920,1.080,21.14,224,0.875,bilinear
halo2botnet50ts_256,94.550,5.450,98.760,1.240,22.64,256,0.950,bicubic
gernet_m,94.550,5.450,98.930,1.070,21.14,224,0.875,bilinear
resmlp_36_distilled_224,94.550,5.450,99.160,0.840,44.69,224,0.875,bicubic
xcit_tiny_12_p16_384_dist,94.540,5.460,99.170,0.830,6.72,384,1.000,bicubic
sehalonet33ts,94.530,5.470,98.780,1.220,13.69,256,0.940,bicubic
regnety_320,94.500,5.500,99.170,0.830,145.05,224,0.875,bicubic
haloregnetz_b,94.500,5.500,98.960,1.040,11.68,224,0.940,bicubic
repvgg_b3g4,94.490,5.510,99.020,0.980,83.83,224,0.875,bilinear
gluon_resnet152_v1d,94.460,5.540,99.000,1.000,60.21,224,0.875,bicubic
ecaresnet101d_pruned,94.440,5.560,99.100,0.900,24.88,224,0.875,bicubic
gluon_seresnext101_32x4d,94.430,5.570,99.090,0.910,48.96,224,0.875,bicubic
sehalonet33ts,94.530,5.470,98.760,1.240,13.69,256,0.940,bicubic
xcit_tiny_12_p16_384_dist,94.530,5.470,99.170,0.830,6.72,384,1.000,bicubic
regnety_320,94.520,5.480,99.170,0.830,145.05,224,0.875,bicubic
haloregnetz_b,94.520,5.480,98.960,1.040,11.68,224,0.940,bicubic
repvgg_b3g4,94.500,5.500,99.020,0.980,83.83,224,0.875,bilinear
ecaresnet101d_pruned,94.450,5.550,99.100,0.900,24.88,224,0.875,bicubic
gluon_seresnext101_32x4d,94.450,5.550,99.090,0.910,48.96,224,0.875,bicubic
gluon_resnet152_v1d,94.440,5.560,99.010,0.990,60.21,224,0.875,bicubic
convmixer_768_32,94.430,5.570,99.110,0.890,21.11,224,0.960,bicubic
halonet50ts,94.420,5.580,98.760,1.240,22.73,256,0.940,bicubic
gcresnext50ts,94.410,5.590,98.990,1.010,15.67,256,0.900,bicubic
levit_256,94.410,5.590,99.060,0.940,18.89,224,0.900,bicubic
levit_256,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic
resnest50d_1s4x24d,94.390,5.610,99.070,0.930,25.68,224,0.875,bicubic
vit_base_patch32_224,94.390,5.610,99.060,0.940,88.22,224,0.900,bicubic
inception_v4,94.380,5.620,98.820,1.180,42.68,299,0.875,bicubic
nf_resnet50,94.380,5.620,99.070,0.930,25.56,288,0.940,bicubic
resnest50d_1s4x24d,94.380,5.620,99.060,0.940,25.68,224,0.875,bicubic
vit_base_patch32_224,94.380,5.620,99.060,0.940,88.22,224,0.900,bicubic
efficientnet_b2,94.370,5.630,99.050,0.950,9.11,288,1.000,bicubic
inception_v4,94.370,5.630,98.820,1.180,42.68,299,0.875,bicubic
xcit_tiny_12_p8_224,94.370,5.630,99.070,0.930,6.71,224,1.000,bicubic
tf_efficientnet_el,94.350,5.650,99.090,0.910,10.59,300,0.904,bicubic
tf_efficientnet_el,94.360,5.640,99.100,0.900,10.59,300,0.904,bicubic
xcit_tiny_12_p8_224,94.360,5.640,99.070,0.930,6.71,224,1.000,bicubic
gluon_resnext101_64x4d,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic
resmlp_24_distilled_224,94.340,5.660,99.090,0.910,30.02,224,0.875,bicubic
gluon_resnext101_64x4d,94.330,5.670,98.880,1.120,83.46,224,0.875,bicubic
inception_resnet_v2,94.320,5.680,98.800,1.200,55.84,299,0.897,bicubic
ssl_resnet50,94.320,5.680,99.160,0.840,25.56,224,0.875,bilinear
resnetv2_50,94.290,5.710,98.930,1.070,25.55,224,0.950,bicubic
regnetx_120,94.290,5.710,99.200,0.800,46.11,224,0.875,bicubic
tf_efficientnet_b2_ap,94.280,5.720,98.950,1.050,9.11,260,0.890,bicubic
rexnet_150,94.270,5.730,99.090,0.910,9.73,224,0.875,bicubic
inception_resnet_v2,94.340,5.660,98.800,1.200,55.84,299,0.897,bicubic
sebotnet33ts_256,94.310,5.690,98.600,1.400,13.70,256,0.940,bicubic
ssl_resnet50,94.310,5.690,99.150,0.850,25.56,224,0.875,bilinear
resmlp_big_24_224,94.270,5.730,98.820,1.180,129.14,224,0.875,bicubic
seresnet33ts,94.260,5.740,98.780,1.220,19.78,256,0.900,bicubic
mixnet_xl,94.220,5.780,98.810,1.190,11.90,224,0.875,bicubic
xcit_tiny_24_p16_224_dist,94.220,5.780,98.960,1.040,12.12,224,1.000,bicubic
ecaresnet50d,94.210,5.790,99.010,0.990,25.58,224,0.875,bicubic
regnetx_320,94.210,5.790,99.050,0.950,107.81,224,0.875,bicubic
tf_efficientnet_b2,94.200,5.800,99.030,0.970,9.11,260,0.890,bicubic
gluon_resnet101_v1s,94.180,5.820,99.020,0.980,44.67,224,0.875,bicubic
gluon_resnet101_v1d,94.180,5.820,98.950,1.050,44.57,224,0.875,bicubic
resnetv2_50,94.270,5.730,98.930,1.070,25.55,224,0.950,bicubic
rexnet_150,94.270,5.730,99.080,0.920,9.73,224,0.875,bicubic
seresnet33ts,94.270,5.730,98.780,1.220,19.78,256,0.900,bicubic
tf_efficientnet_b2_ap,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic
regnetx_120,94.260,5.740,99.190,0.810,46.11,224,0.875,bicubic
mixnet_xl,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic
regnetx_320,94.220,5.780,99.050,0.950,107.81,224,0.875,bicubic
tf_efficientnet_b2,94.210,5.790,99.030,0.970,9.11,260,0.890,bicubic
xcit_tiny_24_p16_224_dist,94.210,5.790,98.960,1.040,12.12,224,1.000,bicubic
ecaresnet50d,94.200,5.800,99.020,0.980,25.58,224,0.875,bicubic
dpn92,94.180,5.820,98.930,1.070,37.67,224,0.875,bicubic
gluon_seresnext50_32x4d,94.180,5.820,98.910,1.090,27.56,224,0.875,bicubic
legacy_seresnext101_32x4d,94.170,5.830,98.970,1.030,48.96,224,0.875,bilinear
regnety_064,94.140,5.860,99.030,0.970,30.58,224,0.875,bicubic
resnet50_gn,94.180,5.820,98.920,1.080,25.56,224,0.940,bicubic
gluon_seresnext50_32x4d,94.170,5.830,98.910,1.090,27.56,224,0.875,bicubic
gluon_resnet101_v1s,94.170,5.830,99.010,0.990,44.67,224,0.875,bicubic
gluon_resnet101_v1d,94.170,5.830,98.940,1.060,44.57,224,0.875,bicubic
ecaresnetlight,94.140,5.860,98.950,1.050,30.16,224,0.875,bicubic
ens_adv_inception_resnet_v2,94.140,5.860,98.790,1.210,55.84,299,0.897,bicubic
ens_adv_inception_resnet_v2,94.130,5.870,98.790,1.210,55.84,299,0.897,bicubic
regnety_064,94.130,5.870,99.030,0.970,30.58,224,0.875,bicubic
gluon_resnext101_32x4d,94.120,5.880,98.940,1.060,44.18,224,0.875,bicubic
tf_efficientnet_lite3,94.110,5.890,98.960,1.040,8.20,300,0.904,bilinear
cspdarknet53,94.100,5.900,98.980,1.020,27.64,256,0.887,bilinear
seresnet50,94.080,5.920,98.950,1.050,28.09,224,0.875,bicubic
legacy_seresnext101_32x4d,94.120,5.880,98.970,1.030,48.96,224,0.875,bilinear
tf_efficientnet_lite3,94.120,5.880,98.960,1.040,8.20,300,0.904,bilinear
cspdarknet53,94.090,5.910,98.980,1.020,27.64,256,0.887,bilinear
efficientnet_el_pruned,94.080,5.920,99.020,0.980,10.59,300,0.904,bicubic
seresnet50,94.080,5.920,98.960,1.040,28.09,224,0.875,bicubic
resnet50d,94.070,5.930,98.920,1.080,25.58,224,0.875,bicubic
regnety_120,94.060,5.940,99.020,0.980,51.82,224,0.875,bicubic
tf_efficientnetv2_b2,94.060,5.940,98.940,1.060,10.10,260,0.890,bicubic
efficientnet_el_pruned,94.060,5.940,99.020,0.980,10.59,300,0.904,bicubic
gluon_xception65,94.040,5.960,99.030,0.970,39.92,299,0.903,bicubic
resnetrs50,94.030,5.970,98.830,1.170,35.69,224,0.910,bicubic
hrnet_w48,94.030,5.970,99.030,0.970,77.47,224,0.875,bilinear
gluon_resnet152_v1b,94.020,5.980,98.750,1.250,60.19,224,0.875,bicubic
dla102x2,94.010,5.990,99.030,0.970,41.28,224,0.875,bilinear
tf_efficientnetv2_b2,94.070,5.930,98.930,1.070,10.10,260,0.890,bicubic
gluon_resnet152_v1b,94.030,5.970,98.740,1.260,60.19,224,0.875,bicubic
hrnet_w48,94.030,5.970,99.040,0.960,77.47,224,0.875,bilinear
gluon_xception65,94.020,5.980,99.020,0.980,39.92,299,0.903,bicubic
resnetrs50,94.020,5.980,98.850,1.150,35.69,224,0.910,bicubic
regnety_120,94.010,5.990,99.030,0.970,51.82,224,0.875,bicubic
dla102x2,94.000,6.000,99.030,0.970,41.28,224,0.875,bilinear
deit_small_patch16_224,93.990,6.010,98.960,1.040,22.05,224,0.900,bicubic
dpn107,93.960,6.040,98.840,1.160,86.92,224,0.875,bicubic
resnet50,93.950,6.050,98.470,1.530,25.56,224,0.950,bicubic
dpn107,93.960,6.040,98.830,1.170,86.92,224,0.875,bicubic
ecaresnet26t,93.950,6.050,98.920,1.080,16.01,320,0.950,bicubic
skresnext50_32x4d,93.950,6.050,98.830,1.170,27.48,224,0.875,bicubic
cait_xxs36_224,93.940,6.060,98.880,1.120,17.30,224,1.000,bicubic
ecaresnet26t,93.930,6.070,98.930,1.070,16.01,320,0.950,bicubic
dpn98,93.930,6.070,98.920,1.080,61.57,224,0.875,bicubic
xception71,93.900,6.100,98.950,1.050,42.34,299,0.903,bicubic
regnety_080,93.900,6.100,98.990,1.010,39.18,224,0.875,bicubic
regnetx_160,93.900,6.100,99.080,0.920,54.28,224,0.875,bicubic
vit_base_patch16_sam_224,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic
nf_regnet_b1,93.890,6.110,98.750,1.250,10.22,288,0.900,bicubic
gluon_resnet152_v1c,93.880,6.120,98.800,1.200,60.21,224,0.875,bicubic
eca_resnet33ts,93.870,6.130,98.890,1.110,19.68,256,0.900,bicubic
resnext50_32x4d,93.850,6.150,98.820,1.180,25.03,224,0.875,bicubic
cspresnet50,93.850,6.150,98.870,1.130,21.62,256,0.887,bilinear
xcit_tiny_24_p16_224,93.850,6.150,98.770,1.230,12.12,224,1.000,bicubic
hrnet_w64,93.850,6.150,98.930,1.070,128.06,224,0.875,bilinear
cait_xxs36_224,93.930,6.070,98.890,1.110,17.30,224,1.000,bicubic
resnet50,93.930,6.070,98.470,1.530,25.56,224,0.950,bicubic
dpn98,93.920,6.080,98.920,1.080,61.57,224,0.875,bicubic
gluon_resnet152_v1c,93.890,6.110,98.800,1.200,60.21,224,0.875,bicubic
regnetx_160,93.890,6.110,99.090,0.910,54.28,224,0.875,bicubic
regnety_080,93.890,6.110,99.000,1.000,39.18,224,0.875,bicubic
vit_base_patch16_224_sam,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic
nf_regnet_b1,93.880,6.120,98.740,1.260,10.22,288,0.900,bicubic
xception71,93.880,6.120,98.950,1.050,42.34,299,0.903,bicubic
cspresnet50,93.860,6.140,98.860,1.140,21.62,256,0.887,bilinear
eca_resnet33ts,93.860,6.140,98.890,1.110,19.68,256,0.900,bicubic
xcit_tiny_24_p16_224,93.850,6.150,98.760,1.240,12.12,224,1.000,bicubic
ese_vovnet39b,93.850,6.150,98.900,1.100,24.57,224,0.875,bicubic
gcresnet33ts,93.820,6.180,98.930,1.070,19.88,256,0.900,bicubic
fbnetv3_d,93.840,6.160,98.910,1.090,10.31,256,0.950,bilinear
hrnet_w64,93.840,6.160,98.930,1.070,128.06,224,0.875,bilinear
ecaresnet50d_pruned,93.820,6.180,99.000,1.000,19.94,224,0.875,bicubic
repvgg_b2g4,93.820,6.180,98.920,1.080,61.76,224,0.875,bilinear
resnext50d_32x4d,93.800,6.200,98.730,1.270,25.05,224,0.875,bicubic
gcresnet33ts,93.820,6.180,98.910,1.090,19.88,256,0.900,bicubic
repvgg_b2g4,93.810,6.190,98.930,1.070,61.76,224,0.875,bilinear
resnext50d_32x4d,93.810,6.190,98.740,1.260,25.05,224,0.875,bicubic
efficientnet_b2_pruned,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic
dla169,93.800,6.200,98.840,1.160,53.39,224,0.875,bilinear
regnetx_080,93.790,6.210,98.900,1.100,39.57,224,0.875,bicubic
dla169,93.780,6.220,98.830,1.170,53.39,224,0.875,bilinear
cspresnext50,93.780,6.220,98.840,1.160,20.57,224,0.875,bilinear
gluon_resnet101_v1b,93.770,6.230,98.690,1.310,44.55,224,0.875,bicubic
resnext101_32x8d,93.770,6.230,98.950,1.050,88.79,224,0.875,bilinear
gluon_resnet101_v1b,93.770,6.230,98.720,1.280,44.55,224,0.875,bicubic
cspresnext50,93.770,6.230,98.840,1.160,20.57,224,0.875,bilinear
dpn131,93.760,6.240,98.850,1.150,79.25,224,0.875,bicubic
tf_efficientnet_b0_ns,93.760,6.240,98.980,1.020,5.29,224,0.875,bicubic
efficientnet_em,93.750,6.250,98.920,1.080,6.90,240,0.882,bicubic
xception65,93.740,6.260,98.870,1.130,39.92,299,0.903,bicubic
xception65,93.760,6.240,98.860,1.140,39.92,299,0.903,bicubic
dpn131,93.750,6.250,98.840,1.160,79.25,224,0.875,bicubic
efficientnet_em,93.740,6.260,98.930,1.070,6.90,240,0.882,bicubic
tf_efficientnet_b0_ns,93.740,6.260,98.980,1.020,5.29,224,0.875,bicubic
wide_resnet101_2,93.720,6.280,98.810,1.190,126.89,224,0.875,bilinear
hrnet_w40,93.710,6.290,98.800,1.200,57.56,224,0.875,bilinear
levit_192,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic
resnetblur50,93.710,6.290,98.800,1.200,25.56,224,0.875,bicubic
resnetblur50,93.710,6.290,98.810,1.190,25.56,224,0.875,bicubic
tf_efficientnet_b1,93.710,6.290,98.800,1.200,7.79,240,0.882,bicubic
tf_efficientnetv2_b1,93.700,6.300,98.810,1.190,8.14,240,0.882,bicubic
rexnet_130,93.690,6.310,98.720,1.280,7.56,224,0.875,bicubic
regnetx_040,93.670,6.330,98.950,1.050,22.12,224,0.875,bicubic
resmlp_36_224,93.670,6.330,98.950,1.050,44.69,224,0.875,bicubic
gluon_resnext50_32x4d,93.670,6.330,98.700,1.300,25.03,224,0.875,bicubic
gluon_resnet101_v1c,93.660,6.340,98.760,1.240,44.57,224,0.875,bicubic
xception,93.650,6.350,98.770,1.230,22.86,299,0.897,bicubic
regnetx_064,93.650,6.350,99.050,0.950,26.21,224,0.875,bicubic
tf_efficientnet_b1_ap,93.640,6.360,98.780,1.220,7.79,240,0.882,bicubic
hrnet_w44,93.620,6.380,98.950,1.050,67.06,224,0.875,bilinear
resnet33ts,93.620,6.380,98.770,1.230,19.68,256,0.900,bicubic
halonet26t,93.610,6.390,98.640,1.360,12.48,256,0.950,bicubic
regnety_040,93.610,6.390,98.960,1.040,20.65,224,0.875,bicubic
dpn68b,93.600,6.400,98.710,1.290,12.61,224,0.875,bicubic
gluon_resnet50_v1s,93.590,6.410,98.830,1.170,25.68,224,0.875,bicubic
gluon_inception_v3,93.590,6.410,98.840,1.160,23.83,299,0.875,bicubic
res2net50_26w_6s,93.580,6.420,98.740,1.260,37.05,224,0.875,bilinear
tf_efficientnet_cc_b1_8e,93.580,6.420,98.690,1.310,39.72,240,0.882,bicubic
repvgg_b2,93.570,6.430,99.070,0.930,89.02,224,0.875,bilinear
tf_efficientnetv2_b1,93.710,6.290,98.820,1.180,8.14,240,0.882,bicubic
levit_192,93.710,6.290,98.800,1.200,10.95,224,0.900,bicubic
gluon_resnet101_v1c,93.690,6.310,98.760,1.240,44.57,224,0.875,bicubic
regnetx_040,93.680,6.320,98.940,1.060,22.12,224,0.875,bicubic
rexnet_130,93.680,6.320,98.710,1.290,7.56,224,0.875,bicubic
resmlp_36_224,93.650,6.350,98.950,1.050,44.69,224,0.875,bicubic
fbnetv3_b,93.650,6.350,98.910,1.090,8.60,256,0.950,bilinear
gluon_resnext50_32x4d,93.650,6.350,98.690,1.310,25.03,224,0.875,bicubic
xception,93.640,6.360,98.760,1.240,22.86,299,0.897,bicubic
resnet33ts,93.630,6.370,98.760,1.240,19.68,256,0.900,bicubic
tf_efficientnet_b1_ap,93.630,6.370,98.800,1.200,7.79,240,0.882,bicubic
dpn68b,93.620,6.380,98.700,1.300,12.61,224,0.875,bicubic
regnetx_064,93.620,6.380,99.050,0.950,26.21,224,0.875,bicubic
res2net50_26w_6s,93.610,6.390,98.740,1.260,37.05,224,0.875,bilinear
hrnet_w44,93.610,6.390,98.960,1.040,67.06,224,0.875,bilinear
regnety_040,93.610,6.390,98.950,1.050,20.65,224,0.875,bicubic
halonet26t,93.600,6.400,98.640,1.360,12.48,256,0.950,bicubic
gluon_resnet50_v1s,93.590,6.410,98.840,1.160,25.68,224,0.875,bicubic
repvgg_b2,93.590,6.410,99.070,0.930,89.02,224,0.875,bilinear
dla60_res2next,93.570,6.430,98.790,1.210,17.03,224,0.875,bilinear
resnet32ts,93.570,6.430,98.750,1.250,17.96,256,0.900,bicubic
dla60_res2next,93.560,6.440,98.800,1.200,17.03,224,0.875,bilinear
gluon_resnet50_v1d,93.550,6.450,98.710,1.290,25.58,224,0.875,bicubic
res2net101_26w_4s,93.520,6.480,98.630,1.370,45.21,224,0.875,bilinear
dla102x,93.510,6.490,98.850,1.150,26.31,224,0.875,bilinear
tf_efficientnet_cc_b1_8e,93.570,6.430,98.690,1.310,39.72,240,0.882,bicubic
eca_halonext26ts,93.560,6.440,98.680,1.320,10.76,256,0.940,bicubic
gluon_inception_v3,93.540,6.460,98.830,1.170,23.83,299,0.875,bicubic
gluon_resnet50_v1d,93.530,6.470,98.710,1.290,25.58,224,0.875,bicubic
dla102x,93.520,6.480,98.850,1.150,26.31,224,0.875,bilinear
res2net101_26w_4s,93.520,6.480,98.600,1.400,45.21,224,0.875,bilinear
coat_tiny,93.510,6.490,98.690,1.310,5.50,224,0.900,bicubic
gmlp_s16_224,93.510,6.490,98.780,1.220,19.42,224,0.875,bicubic
coat_tiny,93.500,6.500,98.680,1.320,5.50,224,0.900,bicubic
selecsls60b,93.500,6.500,98.840,1.160,32.77,224,0.875,bicubic
cait_xxs24_224,93.490,6.510,98.770,1.230,11.96,224,1.000,bicubic
coat_lite_mini,93.490,6.510,98.780,1.220,11.01,224,0.900,bicubic
selecsls60b,93.480,6.520,98.840,1.160,32.77,224,0.875,bicubic
xception41,93.480,6.520,98.760,1.240,26.97,299,0.903,bicubic
legacy_seresnet152,93.460,6.540,98.850,1.150,66.82,224,0.875,bilinear
lambda_resnet26rpt_256,93.440,6.560,98.880,1.120,10.99,256,0.940,bicubic
botnet26t_256,93.440,6.560,98.660,1.340,12.49,256,0.950,bicubic
res2net50_26w_8s,93.430,6.570,98.670,1.330,48.40,224,0.875,bilinear
resmlp_24_224,93.430,6.570,98.810,1.190,30.02,224,0.875,bicubic
vit_tiny_patch16_384,93.430,6.570,98.840,1.160,5.79,384,1.000,bicubic
hrnet_w30,93.410,6.590,98.830,1.170,37.71,224,0.875,bilinear
legacy_seresnext50_32x4d,93.410,6.590,98.800,1.200,27.56,224,0.875,bilinear
repvgg_b1,93.410,6.590,98.780,1.220,57.42,224,0.875,bilinear
lambda_resnet26t,93.400,6.600,98.760,1.240,10.96,256,0.940,bicubic
dla60_res2net,93.380,6.620,98.830,1.170,20.85,224,0.875,bilinear
eca_botnext26ts_256,93.360,6.640,98.690,1.310,10.59,256,0.950,bicubic
xcit_tiny_12_p16_224_dist,93.340,6.660,98.750,1.250,6.72,224,1.000,bicubic
dla102,93.290,6.710,98.780,1.220,33.27,224,0.875,bilinear
legacy_seresnet101,93.290,6.710,98.750,1.250,49.33,224,0.875,bilinear
mixnet_l,93.290,6.710,98.710,1.290,7.33,224,0.875,bicubic
regnetx_032,93.270,6.730,98.740,1.260,15.30,224,0.875,bicubic
resnest26d,93.260,6.740,98.840,1.160,17.07,224,0.875,bilinear
tv_resnet152,93.260,6.740,98.750,1.250,60.19,224,0.875,bilinear
xcit_nano_12_p8_384_dist,93.250,6.750,98.850,1.150,3.05,384,1.000,bicubic
pit_xs_distilled_224,93.230,6.770,98.820,1.180,11.00,224,0.900,bicubic
tf_inception_v3,93.210,6.790,98.490,1.510,23.83,299,0.875,bicubic
dla60x,93.210,6.790,98.720,1.280,17.35,224,0.875,bilinear
tf_efficientnet_em,93.200,6.800,98.680,1.320,6.90,240,0.882,bicubic
xception41,93.480,6.520,98.750,1.250,26.97,299,0.903,bicubic
coat_lite_mini,93.460,6.540,98.780,1.220,11.01,224,0.900,bicubic
legacy_seresnet152,93.440,6.560,98.850,1.150,66.82,224,0.875,bilinear
resmlp_24_224,93.440,6.560,98.810,1.190,30.02,224,0.875,bicubic
botnet26t_256,93.430,6.570,98.660,1.340,12.49,256,0.950,bicubic
lambda_resnet26rpt_256,93.430,6.570,98.880,1.120,10.99,256,0.940,bicubic
legacy_seresnext50_32x4d,93.430,6.570,98.800,1.200,27.56,224,0.875,bilinear
vit_tiny_patch16_384,93.430,6.570,98.830,1.170,5.79,384,1.000,bicubic
res2net50_26w_8s,93.420,6.580,98.690,1.310,48.40,224,0.875,bilinear
repvgg_b1,93.410,6.590,98.790,1.210,57.42,224,0.875,bilinear
lambda_resnet26t,93.400,6.600,98.740,1.260,10.96,256,0.940,bicubic
hrnet_w30,93.390,6.610,98.830,1.170,37.71,224,0.875,bilinear
dla60_res2net,93.380,6.620,98.860,1.140,20.85,224,0.875,bilinear
eca_botnext26ts_256,93.360,6.640,98.700,1.300,10.59,256,0.950,bicubic
xcit_tiny_12_p16_224_dist,93.340,6.660,98.740,1.260,6.72,224,1.000,bicubic
xcit_nano_12_p8_384_dist,93.280,6.720,98.850,1.150,3.05,384,1.000,bicubic
mixnet_l,93.270,6.730,98.700,1.300,7.33,224,0.875,bicubic
legacy_seresnet101,93.270,6.730,98.740,1.260,49.33,224,0.875,bilinear
dla102,93.260,6.740,98.770,1.230,33.27,224,0.875,bilinear
regnetx_032,93.250,6.750,98.730,1.270,15.30,224,0.875,bicubic
pit_xs_distilled_224,93.240,6.760,98.830,1.170,11.00,224,0.900,bicubic
resnest26d,93.240,6.760,98.850,1.150,17.07,224,0.875,bilinear
tv_resnet152,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear
tf_inception_v3,93.200,6.800,98.480,1.520,23.83,299,0.875,bicubic
dla60x,93.190,6.810,98.710,1.290,17.35,224,0.875,bilinear
res2net50_26w_4s,93.180,6.820,98.670,1.330,25.70,224,0.875,bilinear
eca_halonext26ts,93.140,6.860,98.690,1.310,10.76,256,0.940,bicubic
res2next50,93.110,6.890,98.660,1.340,24.67,224,0.875,bilinear
bat_resnext26ts,93.100,6.900,98.720,1.280,10.73,256,0.900,bicubic
tf_efficientnet_em,93.180,6.820,98.670,1.330,6.90,240,0.882,bicubic
res2next50,93.140,6.860,98.650,1.350,24.67,224,0.875,bilinear
bat_resnext26ts,93.100,6.900,98.730,1.270,10.73,256,0.900,bicubic
tf_efficientnetv2_b0,93.060,6.940,98.700,1.300,7.14,224,0.875,bicubic
tf_mixnet_l,93.050,6.950,98.540,1.460,7.33,224,0.875,bicubic
levit_128,93.040,6.960,98.690,1.310,9.21,224,0.900,bicubic
repvgg_b1g4,93.040,6.960,98.820,1.180,39.97,224,0.875,bilinear
regnety_016,93.030,6.970,98.690,1.310,11.20,224,0.875,bicubic
efficientnet_b1,93.020,6.980,98.710,1.290,7.79,256,1.000,bicubic
res2net50_14w_8s,93.020,6.980,98.700,1.300,25.06,224,0.875,bilinear
selecsls60,93.000,7.000,98.830,1.170,30.67,224,0.875,bicubic
adv_inception_v3,92.990,7.010,98.480,1.520,23.83,299,0.875,bicubic
levit_128,93.040,6.960,98.680,1.320,9.21,224,0.900,bicubic
tf_mixnet_l,93.040,6.960,98.540,1.460,7.33,224,0.875,bicubic
repvgg_b1g4,93.030,6.970,98.820,1.180,39.97,224,0.875,bilinear
res2net50_14w_8s,93.030,6.970,98.700,1.300,25.06,224,0.875,bilinear
efficientnet_b1,93.030,6.970,98.710,1.290,7.79,256,1.000,bicubic
selecsls60,93.010,6.990,98.830,1.170,30.67,224,0.875,bicubic
adv_inception_v3,93.010,6.990,98.490,1.510,23.83,299,0.875,bicubic
regnety_016,93.000,7.000,98.680,1.320,11.20,224,0.875,bicubic
hardcorenas_f,92.980,7.020,98.620,1.380,8.20,224,0.875,bilinear
hardcorenas_e,92.960,7.040,98.570,1.430,8.07,224,0.875,bilinear
efficientnet_b1_pruned,92.960,7.040,98.520,1.480,6.33,240,0.882,bicubic
efficientnet_b1_pruned,92.970,7.030,98.520,1.480,6.33,240,0.882,bicubic
hardcorenas_e,92.950,7.050,98.570,1.430,8.07,224,0.875,bilinear
hrnet_w32,92.950,7.050,98.840,1.160,41.23,224,0.875,bilinear
efficientnet_es,92.940,7.060,98.690,1.310,5.44,224,0.875,bicubic
gluon_resnet50_v1c,92.920,7.080,98.710,1.290,25.58,224,0.875,bicubic
tv_resnext50_32x4d,92.910,7.090,98.730,1.270,25.03,224,0.875,bilinear
pit_xs_224,92.900,7.100,98.790,1.210,10.62,224,0.900,bicubic
inception_v3,92.900,7.100,98.320,1.680,23.83,299,0.875,bicubic
efficientnet_es,92.920,7.080,98.690,1.310,5.44,224,0.875,bicubic
pit_xs_224,92.910,7.090,98.770,1.230,10.62,224,0.900,bicubic
tv_resnext50_32x4d,92.910,7.090,98.720,1.280,25.03,224,0.875,bilinear
inception_v3,92.900,7.100,98.330,1.670,23.83,299,0.875,bicubic
densenet161,92.890,7.110,98.810,1.190,28.68,224,0.875,bicubic
tv_resnet101,92.880,7.120,98.660,1.340,44.55,224,0.875,bilinear
densenet161,92.880,7.120,98.810,1.190,28.68,224,0.875,bicubic
resmlp_12_distilled_224,92.870,7.130,98.630,1.370,15.35,224,0.875,bicubic
tf_efficientnet_cc_b0_8e,92.850,7.150,98.460,1.540,24.01,224,0.875,bicubic
resmlp_12_distilled_224,92.870,7.130,98.620,1.380,15.35,224,0.875,bicubic
tf_efficientnet_cc_b0_8e,92.870,7.130,98.460,1.540,24.01,224,0.875,bicubic
coat_lite_tiny,92.860,7.140,98.630,1.370,5.72,224,0.900,bicubic
rexnet_100,92.840,7.160,98.620,1.380,4.80,224,0.875,bicubic
tf_efficientnet_cc_b0_4e,92.840,7.160,98.440,1.560,13.31,224,0.875,bicubic
coat_lite_tiny,92.830,7.170,98.640,1.360,5.72,224,0.900,bicubic
res2net50_48w_2s,92.800,7.200,98.470,1.530,25.29,224,0.875,bilinear
seresnext26ts,92.790,7.210,98.600,1.400,10.39,256,0.900,bicubic
seresnext26t_32x4d,92.770,7.230,98.550,1.450,16.81,224,0.875,bicubic
hrnet_w18,92.750,7.250,98.650,1.350,21.30,224,0.875,bilinear
seresnext26t_32x4d,92.820,7.180,98.560,1.440,16.81,224,0.875,bicubic
seresnext26ts,92.810,7.190,98.600,1.400,10.39,256,0.900,bicubic
res2net50_48w_2s,92.790,7.210,98.480,1.520,25.29,224,0.875,bilinear
tinynet_a,92.790,7.210,98.560,1.440,6.19,192,0.875,bicubic
crossvit_9_dagger_240,92.750,7.250,98.510,1.490,8.78,240,0.875,bicubic
dla60,92.690,7.310,98.630,1.370,22.04,224,0.875,bilinear
densenet201,92.690,7.310,98.660,1.340,20.01,224,0.875,bicubic
resnet26t,92.680,7.320,98.600,1.400,16.01,256,0.940,bicubic
gmixer_24_224,92.670,7.330,98.260,1.740,24.72,224,0.875,bicubic
legacy_seresnet50,92.670,7.330,98.660,1.340,28.09,224,0.875,bilinear
repvgg_a2,92.660,7.340,98.530,1.470,28.21,224,0.875,bilinear
resnet34d,92.640,7.360,98.440,1.560,21.82,224,0.875,bicubic
mobilenetv2_120d,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic
tf_efficientnet_b0_ap,92.600,7.400,98.370,1.630,5.29,224,0.875,bicubic
hrnet_w18,92.750,7.250,98.660,1.340,21.30,224,0.875,bilinear
densenet201,92.690,7.310,98.650,1.350,20.01,224,0.875,bicubic
gmixer_24_224,92.680,7.320,98.280,1.720,24.72,224,0.875,bicubic
repvgg_a2,92.680,7.320,98.520,1.480,28.21,224,0.875,bilinear
dla60,92.670,7.330,98.630,1.370,22.04,224,0.875,bilinear
legacy_seresnet50,92.670,7.330,98.650,1.350,28.09,224,0.875,bilinear
resnet26t,92.670,7.330,98.580,1.420,16.01,256,0.940,bicubic
resnet34d,92.640,7.360,98.420,1.580,21.82,224,0.875,bicubic
mobilenetv2_120d,92.610,7.390,98.500,1.500,5.83,224,0.875,bicubic
tf_efficientnet_b0_ap,92.610,7.390,98.370,1.630,5.29,224,0.875,bicubic
hardcorenas_d,92.600,7.400,98.430,1.570,7.50,224,0.875,bilinear
legacy_seresnext26_32x4d,92.590,7.410,98.420,1.580,16.79,224,0.875,bicubic
tf_efficientnet_lite2,92.570,7.430,98.550,1.450,6.09,260,0.890,bicubic
regnetx_016,92.560,7.440,98.550,1.450,9.19,224,0.875,bicubic
skresnet34,92.560,7.440,98.510,1.490,22.28,224,0.875,bicubic
gluon_resnet50_v1b,92.550,7.450,98.550,1.450,25.56,224,0.875,bicubic
legacy_seresnext26_32x4d,92.580,7.420,98.410,1.590,16.79,224,0.875,bicubic
tf_efficientnet_lite2,92.580,7.420,98.550,1.450,6.09,260,0.890,bicubic
skresnet34,92.570,7.430,98.520,1.480,22.28,224,0.875,bicubic
gluon_resnet50_v1b,92.560,7.440,98.550,1.450,25.56,224,0.875,bicubic
regnetx_016,92.540,7.460,98.550,1.450,9.19,224,0.875,bicubic
efficientnet_b0,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic
selecsls42b,92.480,7.520,98.440,1.560,32.46,224,0.875,bicubic
gcresnext26ts,92.470,7.530,98.500,1.500,10.48,256,0.900,bicubic
efficientnet_b0,92.470,7.530,98.680,1.320,5.29,224,0.875,bicubic
gcresnext26ts,92.470,7.530,98.490,1.510,10.48,256,0.900,bicubic
xcit_tiny_12_p16_224,92.460,7.540,98.630,1.370,6.72,224,1.000,bicubic
gernet_s,92.440,7.560,98.490,1.510,8.17,224,0.875,bilinear
gernet_s,92.440,7.560,98.500,1.500,8.17,224,0.875,bilinear
seresnext26d_32x4d,92.430,7.570,98.540,1.460,16.81,224,0.875,bicubic
eca_resnext26ts,92.420,7.580,98.610,1.390,10.30,256,0.900,bicubic
xcit_nano_12_p8_224_dist,92.410,7.590,98.510,1.490,3.05,224,1.000,bicubic
xcit_nano_12_p8_224_dist,92.420,7.580,98.530,1.470,3.05,224,1.000,bicubic
eca_resnext26ts,92.410,7.590,98.620,1.380,10.30,256,0.900,bicubic
densenetblur121d,92.400,7.600,98.410,1.590,8.00,224,0.875,bicubic
tf_efficientnet_b0,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic
densenetblur121d,92.400,7.600,98.420,1.580,8.00,224,0.875,bicubic
convmixer_1024_20_ks9_p14,92.350,7.650,98.420,1.580,24.38,224,0.960,bicubic
hardcorenas_c,92.330,7.670,98.340,1.660,5.52,224,0.875,bilinear
tf_efficientnet_lite1,92.320,7.680,98.490,1.510,5.42,240,0.882,bicubic
hardcorenas_c,92.330,7.670,98.350,1.650,5.52,224,0.875,bilinear
tf_efficientnet_lite1,92.310,7.690,98.490,1.510,5.42,240,0.882,bicubic
densenet169,92.290,7.710,98.590,1.410,14.15,224,0.875,bicubic
mixnet_m,92.260,7.740,98.370,1.630,5.01,224,0.875,bicubic
dpn68,92.260,7.740,98.600,1.400,12.61,224,0.875,bicubic
resnet26d,92.250,7.750,98.470,1.530,16.01,224,0.875,bicubic
mixnet_m,92.270,7.730,98.350,1.650,5.01,224,0.875,bicubic
dpn68,92.260,7.740,98.610,1.390,12.61,224,0.875,bicubic
mobilenetv3_large_100_miil,92.250,7.750,98.250,1.750,5.48,224,0.875,bilinear
resnext26ts,92.210,7.790,98.280,1.720,10.30,256,0.900,bicubic
tf_mixnet_m,92.170,7.830,98.420,1.580,5.01,224,0.875,bicubic
vit_small_patch32_224,92.150,7.850,98.510,1.490,22.88,224,0.900,bicubic
resmlp_12_224,92.130,7.870,98.570,1.430,15.35,224,0.875,bicubic
tv_resnet50,92.110,7.890,98.420,1.580,25.56,224,0.875,bilinear
xcit_nano_12_p16_384_dist,92.100,7.900,98.520,1.480,3.05,384,1.000,bicubic
resnet26d,92.250,7.750,98.450,1.550,16.01,224,0.875,bicubic
resnext26ts,92.220,7.780,98.250,1.750,10.30,256,0.900,bicubic
tf_mixnet_m,92.200,7.800,98.420,1.580,5.01,224,0.875,bicubic
vit_small_patch32_224,92.160,7.840,98.510,1.490,22.88,224,0.900,bicubic
resmlp_12_224,92.120,7.880,98.570,1.430,15.35,224,0.875,bicubic
tv_resnet50,92.120,7.880,98.420,1.580,25.56,224,0.875,bilinear
xcit_nano_12_p16_384_dist,92.110,7.890,98.520,1.480,3.05,384,1.000,bicubic
tf_efficientnet_es,92.100,7.900,98.430,1.570,5.44,224,0.875,bicubic
mobilenetv2_140,92.030,7.970,98.250,1.750,6.11,224,0.875,bicubic
ese_vovnet19b_dw,92.020,7.980,98.520,1.480,6.54,224,0.875,bicubic
hardcorenas_b,91.970,8.030,98.400,1.600,5.18,224,0.875,bilinear
mobilenetv2_140,92.050,7.950,98.250,1.750,6.11,224,0.875,bicubic
ese_vovnet19b_dw,92.010,7.990,98.510,1.490,6.54,224,0.875,bicubic
densenet121,91.940,8.060,98.280,1.720,7.98,224,0.875,bicubic
vit_tiny_patch16_224,91.930,8.070,98.330,1.670,5.72,224,0.900,bicubic
regnety_008,91.890,8.110,98.420,1.580,6.26,224,0.875,bicubic
hardcorenas_b,91.930,8.070,98.400,1.600,5.18,224,0.875,bilinear
regnety_008,91.910,8.090,98.420,1.580,6.26,224,0.875,bicubic
vit_tiny_patch16_224,91.910,8.090,98.340,1.660,5.72,224,0.900,bicubic
mixnet_s,91.780,8.220,98.300,1.700,4.13,224,0.875,bicubic
vit_tiny_r_s16_p8_384,91.720,8.280,98.430,1.570,6.36,384,1.000,bicubic
efficientnet_es_pruned,91.710,8.290,98.400,1.600,5.44,224,0.875,bicubic
tf_mixnet_s,91.690,8.310,98.240,1.760,4.13,224,0.875,bicubic
repvgg_b0,91.670,8.330,98.450,1.550,15.82,224,0.875,bilinear
semnasnet_100,91.660,8.340,98.260,1.740,3.89,224,0.875,bicubic
hardcorenas_a,91.610,8.390,98.170,1.830,5.26,224,0.875,bilinear
regnety_006,91.580,8.420,98.430,1.570,6.06,224,0.875,bicubic
mobilenetv3_rw,91.550,8.450,98.270,1.730,5.48,224,0.875,bicubic
vit_tiny_r_s16_p8_384,91.730,8.270,98.430,1.570,6.36,384,1.000,bicubic
efficientnet_es_pruned,91.710,8.290,98.410,1.590,5.44,224,0.875,bicubic
repvgg_b0,91.680,8.320,98.450,1.550,15.82,224,0.875,bilinear
tf_mixnet_s,91.680,8.320,98.240,1.760,4.13,224,0.875,bicubic
semnasnet_100,91.660,8.340,98.270,1.730,3.89,224,0.875,bicubic
hardcorenas_a,91.620,8.380,98.170,1.830,5.26,224,0.875,bilinear
regnety_006,91.560,8.440,98.430,1.570,6.06,224,0.875,bicubic
mobilenetv3_rw,91.540,8.460,98.270,1.730,5.48,224,0.875,bicubic
levit_128s,91.500,8.500,98.400,1.600,7.78,224,0.900,bicubic
legacy_seresnet34,91.480,8.520,98.200,1.800,21.96,224,0.875,bilinear
mobilenetv3_large_100,91.480,8.520,98.330,1.670,5.48,224,0.875,bicubic
resnet26,91.460,8.540,98.270,1.730,16.00,224,0.875,bicubic
tf_mobilenetv3_large_100,91.410,8.590,98.250,1.750,5.48,224,0.875,bilinear
tv_densenet121,91.400,8.600,98.250,1.750,7.98,224,0.875,bicubic
mobilenetv2_110d,91.320,8.680,98.180,1.820,4.52,224,0.875,bicubic
tf_efficientnet_lite0,91.280,8.720,98.090,1.910,4.65,224,0.875,bicubic
fbnetc_100,91.260,8.740,97.820,2.180,5.57,224,0.875,bilinear
dla34,91.250,8.750,98.180,1.820,15.74,224,0.875,bilinear
mobilenetv3_large_100,91.480,8.520,98.320,1.680,5.48,224,0.875,bicubic
resnet26,91.450,8.550,98.270,1.730,16.00,224,0.875,bicubic
tf_mobilenetv3_large_100,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear
tv_densenet121,91.410,8.590,98.250,1.750,7.98,224,0.875,bicubic
mobilenetv2_110d,91.350,8.650,98.190,1.810,4.52,224,0.875,bicubic
tf_efficientnet_lite0,91.300,8.700,98.090,1.910,4.65,224,0.875,bicubic
fbnetc_100,91.260,8.740,97.830,2.170,5.57,224,0.875,bilinear
efficientnet_lite0,91.250,8.750,98.250,1.750,4.65,224,0.875,bicubic
mnasnet_100,91.200,8.800,98.040,1.960,4.38,224,0.875,bicubic
resnet34,91.190,8.810,98.230,1.770,21.80,224,0.875,bilinear
regnetx_008,91.190,8.810,98.370,1.630,7.26,224,0.875,bicubic
hrnet_w18_small_v2,91.170,8.830,98.350,1.650,15.60,224,0.875,bilinear
resnest14d,91.150,8.850,98.350,1.650,10.61,224,0.875,bilinear
mixer_b16_224,91.130,8.870,97.410,2.590,59.88,224,0.875,bicubic
xcit_nano_12_p8_224,91.100,8.900,98.240,1.760,3.05,224,1.000,bicubic
swsl_resnet18,91.090,8.910,98.210,1.790,11.69,224,0.875,bilinear
gluon_resnet34_v1b,91.090,8.910,98.180,1.820,21.80,224,0.875,bicubic
deit_tiny_distilled_patch16_224,91.090,8.910,98.270,1.730,5.91,224,0.900,bicubic
crossvit_9_240,91.070,8.930,98.310,1.690,8.55,240,0.875,bicubic
vgg19_bn,90.990,9.010,98.120,1.880,143.68,224,0.875,bilinear
pit_ti_distilled_224,90.900,9.100,98.230,1.770,5.10,224,0.900,bicubic
regnetx_006,90.750,9.250,98.100,1.900,6.20,224,0.875,bicubic
regnety_004,90.750,9.250,98.080,1.920,4.34,224,0.875,bicubic
dla34,91.230,8.770,98.180,1.820,15.74,224,0.875,bilinear
mnasnet_100,91.200,8.800,98.050,1.950,4.38,224,0.875,bicubic
resnet34,91.200,8.800,98.240,1.760,21.80,224,0.875,bilinear
hrnet_w18_small_v2,91.170,8.830,98.340,1.660,15.60,224,0.875,bilinear
regnetx_008,91.160,8.840,98.380,1.620,7.26,224,0.875,bicubic
mixer_b16_224,91.140,8.860,97.400,2.600,59.88,224,0.875,bicubic
tinynet_b,91.130,8.870,98.070,1.930,3.73,188,0.875,bicubic
resnest14d,91.120,8.880,98.330,1.670,10.61,224,0.875,bilinear
xcit_nano_12_p8_224,91.120,8.880,98.240,1.760,3.05,224,1.000,bicubic
deit_tiny_distilled_patch16_224,91.110,8.890,98.270,1.730,5.91,224,0.900,bicubic
gluon_resnet34_v1b,91.100,8.900,98.180,1.820,21.80,224,0.875,bicubic
swsl_resnet18,91.080,8.920,98.210,1.790,11.69,224,0.875,bilinear
crossvit_9_240,91.050,8.950,98.310,1.690,8.55,240,0.875,bicubic
vgg19_bn,90.990,9.010,98.110,1.890,143.68,224,0.875,bilinear
pit_ti_distilled_224,90.900,9.100,98.220,1.780,5.10,224,0.900,bicubic
regnetx_006,90.770,9.230,98.100,1.900,6.20,224,0.875,bicubic
regnety_004,90.770,9.230,98.080,1.920,4.34,224,0.875,bicubic
ssl_resnet18,90.700,9.300,98.030,1.970,11.69,224,0.875,bilinear
spnasnet_100,90.610,9.390,97.950,2.050,4.42,224,0.875,bilinear
convit_tiny,90.550,9.450,98.210,1.790,5.71,224,0.875,bicubic
crossvit_tiny_240,90.530,9.470,97.950,2.050,7.01,240,0.875,bicubic
vgg16_bn,90.520,9.480,97.990,2.010,138.37,224,0.875,bilinear
ghostnet_100,90.440,9.560,97.840,2.160,5.18,224,0.875,bilinear
pit_ti_224,90.440,9.560,98.020,1.980,4.85,224,0.900,bicubic
tf_mobilenetv3_large_075,90.310,9.690,97.880,2.120,3.99,224,0.875,bilinear
tv_resnet34,90.310,9.690,97.970,2.030,21.80,224,0.875,bilinear
xcit_nano_12_p16_224_dist,90.190,9.810,97.760,2.240,3.05,224,1.000,bicubic
spnasnet_100,90.600,9.400,97.950,2.050,4.42,224,0.875,bilinear
convit_tiny,90.550,9.450,98.220,1.780,5.71,224,0.875,bicubic
crossvit_tiny_240,90.540,9.460,97.950,2.050,7.01,240,0.875,bicubic
vgg16_bn,90.540,9.460,97.990,2.010,138.37,224,0.875,bilinear
ghostnet_100,90.440,9.560,97.830,2.170,5.18,224,0.875,bilinear
pit_ti_224,90.440,9.560,98.010,1.990,4.85,224,0.900,bicubic
tf_mobilenetv3_large_075,90.320,9.680,97.870,2.130,3.99,224,0.875,bilinear
tv_resnet34,90.290,9.710,97.980,2.020,21.80,224,0.875,bilinear
semnasnet_075,90.200,9.800,97.970,2.030,2.91,224,0.875,bicubic
skresnet18,90.170,9.830,97.780,2.220,11.96,224,0.875,bicubic
resnet18d,89.990,10.010,97.840,2.160,11.71,224,0.875,bicubic
hrnet_w18_small,89.900,10.100,97.900,2.100,13.19,224,0.875,bilinear
vit_base_patch32_sam_224,89.870,10.130,97.600,2.400,88.22,224,0.900,bicubic
mobilenetv2_100,89.840,10.160,97.840,2.160,3.50,224,0.875,bicubic
vgg19,89.690,10.310,97.550,2.450,143.67,224,0.875,bilinear
deit_tiny_patch16_224,89.600,10.400,97.960,2.040,5.72,224,0.900,bicubic
regnetx_004,89.450,10.550,97.780,2.220,5.16,224,0.875,bicubic
vit_tiny_r_s16_p8_224,89.400,10.600,97.690,2.310,6.34,224,0.900,bicubic
vgg16,89.380,10.620,97.520,2.480,138.36,224,0.875,bilinear
legacy_seresnet18,89.250,10.750,97.690,2.310,11.78,224,0.875,bicubic
vgg13_bn,89.210,10.790,97.540,2.460,133.05,224,0.875,bilinear
tf_mobilenetv3_large_minimal_100,89.160,10.840,97.320,2.680,3.92,224,0.875,bilinear
xcit_nano_12_p16_224,88.960,11.040,97.410,2.590,3.05,224,1.000,bicubic
gluon_resnet18_v1b,88.670,11.330,97.110,2.890,11.69,224,0.875,bicubic
vgg11_bn,88.390,11.610,97.280,2.720,132.87,224,0.875,bilinear
regnety_002,88.210,11.790,97.420,2.580,3.16,224,0.875,bicubic
resnet18,88.160,11.840,97.120,2.880,11.69,224,0.875,bilinear
vgg13,87.550,12.450,97.120,2.880,133.05,224,0.875,bilinear
regnetx_002,87.360,12.640,96.990,3.010,2.68,224,0.875,bicubic
vgg11,87.330,12.670,97.110,2.890,132.86,224,0.875,bilinear
dla60x_c,87.080,12.920,97.140,2.860,1.32,224,0.875,bilinear
mixer_l16_224,86.960,13.040,94.030,5.970,208.20,224,0.875,bicubic
tf_mobilenetv3_small_100,85.990,14.010,96.410,3.590,2.54,224,0.875,bilinear
dla46x_c,85.470,14.530,96.450,3.550,1.07,224,0.875,bilinear
dla46_c,84.710,15.290,96.210,3.790,1.30,224,0.875,bilinear
xcit_nano_12_p16_224_dist,90.170,9.830,97.760,2.240,3.05,224,1.000,bicubic
resnet18d,89.990,10.010,97.830,2.170,11.71,224,0.875,bicubic
hrnet_w18_small,89.880,10.120,97.900,2.100,13.19,224,0.875,bilinear
vit_base_patch32_224_sam,89.860,10.140,97.600,2.400,88.22,224,0.900,bicubic
mobilenetv2_100,89.830,10.170,97.830,2.170,3.50,224,0.875,bicubic
vgg19,89.680,10.320,97.550,2.450,143.67,224,0.875,bilinear
deit_tiny_patch16_224,89.620,10.380,97.960,2.040,5.72,224,0.900,bicubic
regnetx_004,89.470,10.530,97.770,2.230,5.16,224,0.875,bicubic
vgg16,89.360,10.640,97.520,2.480,138.36,224,0.875,bilinear
vit_tiny_r_s16_p8_224,89.350,10.650,97.700,2.300,6.34,224,0.900,bicubic
legacy_seresnet18,89.260,10.740,97.680,2.320,11.78,224,0.875,bicubic
vgg13_bn,89.200,10.800,97.520,2.480,133.05,224,0.875,bilinear
tf_mobilenetv3_large_minimal_100,89.170,10.830,97.320,2.680,3.92,224,0.875,bilinear
xcit_nano_12_p16_224,88.970,11.030,97.390,2.610,3.05,224,1.000,bicubic
lcnet_100,88.950,11.050,97.380,2.620,2.95,224,0.875,bicubic
gluon_resnet18_v1b,88.660,11.340,97.100,2.900,11.69,224,0.875,bicubic
tinynet_c,88.420,11.580,97.260,2.740,2.46,184,0.875,bicubic
vgg11_bn,88.390,11.610,97.270,2.730,132.87,224,0.875,bilinear
regnety_002,88.190,11.810,97.420,2.580,3.16,224,0.875,bicubic
resnet18,88.150,11.850,97.120,2.880,11.69,224,0.875,bilinear
vgg13,87.570,12.430,97.120,2.880,133.05,224,0.875,bilinear
regnetx_002,87.380,12.620,96.990,3.010,2.68,224,0.875,bicubic
vgg11,87.340,12.660,97.110,2.890,132.86,224,0.875,bilinear
dla60x_c,87.110,12.890,97.140,2.860,1.32,224,0.875,bilinear
mixer_l16_224,86.970,13.030,94.040,5.960,208.20,224,0.875,bicubic
lcnet_075,86.940,13.060,96.530,3.470,2.36,224,0.875,bicubic
tf_mobilenetv3_small_100,85.970,14.030,96.410,3.590,2.54,224,0.875,bilinear
dla46x_c,85.480,14.520,96.440,3.560,1.07,224,0.875,bilinear
tinynet_d,85.430,14.570,96.020,3.980,2.34,152,0.875,bicubic
mobilenetv2_050,84.990,15.010,95.620,4.380,1.97,224,0.875,bicubic
mnasnet_small,84.920,15.080,95.930,4.070,2.03,224,0.875,bicubic
dla46_c,84.670,15.330,96.200,3.800,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075,84.530,15.470,95.890,4.110,2.04,224,0.875,bilinear
lcnet_050,83.000,17.000,95.020,4.980,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100,82.680,17.320,95.010,4.990,2.04,224,0.875,bilinear
tinynet_e,79.800,20.200,93.980,6.020,2.04,106,0.875,bicubic

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
2 beit_large_patch16_384 97.820 97.810 2.180 2.190 99.790 0.210 305.00 384 1.000 bicubic
3 beit_large_patch16_512 97.770 97.780 2.230 2.220 99.810 99.820 0.190 0.180 305.67 512 1.000 bicubic
4 tf_efficientnet_l2_ns 97.770 97.780 2.230 2.220 99.890 0.110 480.31 800 0.960 bicubic
5 tf_efficientnet_l2_ns_475 97.750 2.250 99.820 0.180 480.31 475 0.936 bicubic
6 beit_large_patch16_224 convnext_xlarge_384_in22ft1k 97.470 97.550 2.530 2.450 99.690 99.800 0.310 0.200 304.43 350.20 224 384 0.900 1.000 bicubic
7 beit_large_patch16_224 97.480 2.520 99.690 0.310 304.43 224 0.900 bicubic
8 convnext_large_384_in22ft1k 97.440 2.560 99.780 0.220 197.77 384 1.000 bicubic
9 vit_large_patch16_384 97.420 2.580 99.780 0.220 304.72 384 1.000 bicubic
10 beit_base_patch16_384 97.350 97.330 2.650 2.670 99.710 99.720 0.290 0.280 86.74 384 1.000 bicubic
11 tf_efficientnet_b7_ns convnext_base_384_in22ft1k 97.210 97.280 2.790 2.720 99.700 99.770 0.300 0.230 66.35 88.59 600 384 0.949 1.000 bicubic
12 swin_large_patch4_window12_384 convnext_large_in22ft1k 97.180 97.260 2.820 2.740 99.690 99.650 0.310 0.350 196.74 197.77 384 224 1.000 0.875 bicubic
13 convnext_xlarge_in22ft1k 97.240 2.760 99.730 0.270 350.20 224 0.875 bicubic
14 tf_efficientnet_b7_ns 97.200 2.800 99.700 0.300 66.35 600 0.949 bicubic
15 swin_large_patch4_window12_384 97.180 2.820 99.680 0.320 196.74 384 1.000 bicubic
16 tf_efficientnetv2_xl_in21ft1k 97.150 2.850 99.620 0.380 208.12 512 1.000 bicubic
17 tf_efficientnetv2_l_in21ft1k swin_base_patch4_window12_384 97.110 97.120 2.890 2.880 99.700 99.780 0.300 0.220 118.52 87.90 480 384 1.000 bicubic
18 swin_base_patch4_window12_384 tf_efficientnetv2_l_in21ft1k 97.070 97.110 2.930 2.890 99.770 99.710 0.230 0.290 87.90 118.52 384 480 1.000 bicubic
19 tf_efficientnet_b6_ns vit_base_patch8_224 97.030 97.080 2.970 2.920 99.710 99.620 0.290 0.380 43.04 86.58 528 224 0.942 0.900 bicubic
20 vit_base_patch16_384 tf_efficientnet_b6_ns 97.020 2.980 99.700 99.710 0.300 0.290 86.86 43.04 384 528 1.000 0.942 bicubic
21 ig_resnext101_32x48d vit_base_patch16_384 96.960 97.020 3.040 2.980 99.670 99.710 0.330 0.290 828.41 86.86 224 384 0.875 1.000 bilinear bicubic
22 ig_resnext101_32x48d 96.970 3.030 99.670 0.330 828.41 224 0.875 bilinear
23 tf_efficientnetv2_m_in21ft1k 96.970 3.030 99.610 0.390 54.14 480 1.000 bicubic
24 swin_large_patch4_window7_224 96.950 3.050 99.660 0.340 196.53 224 0.900 bicubic
25 tf_efficientnetv2_m_in21ft1k vit_large_r50_s32_384 96.950 3.050 99.610 99.710 0.390 0.290 54.14 329.09 480 384 1.000 bicubic
26 vit_large_r50_s32_384 xcit_large_24_p16_384_dist 96.950 96.940 3.050 3.060 99.720 99.510 0.280 0.490 329.09 189.10 384 1.000 bicubic
27 xcit_large_24_p16_384_dist dm_nfnet_f6 96.930 96.920 3.070 3.080 99.510 99.720 0.490 0.280 189.10 438.36 384 576 1.000 0.956 bicubic
28 dm_nfnet_f6 cait_m48_448 96.910 96.880 3.090 3.120 99.720 99.620 0.280 0.380 438.36 356.46 576 448 0.956 1.000 bicubic
29 resnetv2_152x4_bitm 96.880 3.120 99.660 0.340 936.53 480 1.000 bilinear
30 tf_efficientnet_b5_ns 96.880 96.870 3.120 3.130 99.640 0.360 30.39 456 0.934 bicubic
31 cait_m48_448 convnext_base_in22ft1k 96.870 96.860 3.130 3.140 99.620 99.650 0.380 0.350 356.46 88.59 448 224 1.000 0.875 bicubic
32 xcit_small_24_p8_384_dist cait_m36_384 96.830 3.170 99.630 99.660 0.370 0.340 47.63 271.22 384 1.000 bicubic
33 cait_m36_384 dm_nfnet_f5 96.820 96.810 3.180 3.190 99.660 99.670 0.340 0.330 271.22 377.21 384 544 1.000 0.954 bicubic
34 dm_nfnet_f5 xcit_small_24_p8_384_dist 96.800 96.810 3.200 3.190 99.670 99.630 0.330 0.370 377.21 47.63 544 384 0.954 1.000 bicubic
35 dm_nfnet_f4 xcit_medium_24_p8_384_dist 96.780 3.220 99.620 99.610 0.380 0.390 316.07 84.32 512 384 0.951 1.000 bicubic
36 ig_resnext101_32x32d 96.780 3.220 99.530 0.470 468.53 224 0.875 bilinear
37 xcit_medium_24_p8_384_dist dm_nfnet_f4 96.780 3.220 99.620 0.380 84.32 316.07 384 512 1.000 0.951 bicubic
38 xcit_large_24_p8_384_dist 96.760 3.240 99.560 0.440 188.93 384 1.000 bicubic
39 dm_nfnet_f3 96.720 96.730 3.280 3.270 99.630 0.370 254.92 416 0.940 bicubic
40 tf_efficientnet_b4_ns 96.710 3.290 99.640 0.360 19.34 380 0.922 bicubic
41 vit_large_patch16_224 96.710 3.290 99.650 0.350 304.33 224 0.900 bicubic
42 tf_efficientnet_b8 96.700 3.300 99.550 99.530 0.450 0.470 87.41 672 0.954 bicubic
43 xcit_medium_24_p16_384_dist 96.690 96.700 3.310 3.300 99.600 0.400 84.40 384 1.000 bicubic
44 swin_base_patch4_window7_224 96.670 96.680 3.330 3.320 99.670 99.660 0.330 0.340 87.77 224 0.900 bicubic
45 beit_base_patch16_224 96.650 96.660 3.350 3.340 99.660 0.340 86.53 224 0.900 bicubic
46 tf_efficientnetv2_l 96.650 3.350 99.570 99.560 0.430 0.440 118.52 480 1.000 bicubic
47 cait_s36_384 xcit_large_24_p8_224_dist 96.630 96.640 3.370 3.360 99.590 99.460 0.410 0.540 68.37 188.93 384 224 1.000 bicubic
48 xcit_large_24_p8_224_dist cait_s36_384 96.620 96.630 3.380 3.370 99.460 99.600 0.540 0.400 188.93 68.37 224 384 1.000 bicubic
49 cait_s24_384 regnetz_e8 96.580 96.600 3.420 3.400 99.550 99.610 0.450 0.390 47.06 57.70 384 320 1.000 bicubic
50 tf_efficientnet_b7 96.580 3.420 99.510 0.490 66.35 600 0.949 bicubic
51 tf_efficientnet_b8_ap cait_s24_384 96.560 96.570 3.440 3.430 99.550 0.450 87.41 47.06 672 384 0.954 1.000 bicubic
52 tf_efficientnetv2_m tf_efficientnet_b8_ap 96.550 3.450 99.570 99.540 0.430 0.460 54.14 87.41 480 672 1.000 0.954 bicubic
53 xcit_small_24_p8_224_dist 96.550 3.450 99.560 99.570 0.440 0.430 47.63 224 1.000 bicubic
54 tf_efficientnetv2_m 96.540 3.460 99.570 0.430 54.14 480 1.000 bicubic
55 resnetv2_152x2_bitm 96.520 3.480 99.590 0.410 236.34 448 1.000 bilinear
56 xcit_medium_24_p8_224_dist 96.500 96.520 3.500 3.480 99.500 99.510 0.500 0.490 84.32 224 1.000 bicubic
57 deit_base_distilled_patch16_384 96.490 96.510 3.510 3.490 99.590 0.410 87.63 384 1.000 bicubic
58 xcit_small_12_p8_384_dist 96.480 3.520 99.480 99.490 0.520 0.510 26.21 384 1.000 bicubic
59 vit_base_r50_s16_384 tf_efficientnetv2_s_in21ft1k 96.470 3.530 99.660 99.570 0.340 0.430 98.95 21.46 384 1.000 bicubic
60 dm_nfnet_f2 96.460 3.540 99.540 0.460 193.78 352 0.920 bicubic
61 ecaresnet269d 96.460 3.540 99.610 0.390 102.09 352 1.000 bicubic
62 tf_efficientnetv2_s_in21ft1k eca_nfnet_l2 96.460 96.450 3.540 3.550 99.570 99.630 0.430 0.370 21.46 56.72 384 1.000 bicubic
63 eca_nfnet_l2 vit_base_r50_s16_384 96.460 96.450 3.540 3.550 99.630 99.660 0.370 0.340 56.72 98.95 384 1.000 bicubic
64 dm_nfnet_f2 ig_resnext101_32x16d 96.450 96.440 3.550 3.560 99.540 0.460 193.78 194.03 352 224 0.920 0.875 bicubic bilinear
ig_resnext101_32x16d 96.430 3.570 99.540 0.460 194.03 224 0.875 bilinear
65 resnetrs420 96.400 3.600 99.540 0.460 191.89 416 1.000 bicubic
66 dm_nfnet_f1 96.370 96.380 3.630 3.620 99.480 99.470 0.520 0.530 132.63 320 0.910 bicubic
67 tf_efficientnet_b6_ap 96.360 96.370 3.640 3.630 99.550 0.450 43.04 528 0.942 bicubic
68 xcit_small_24_p16_384_dist resmlp_big_24_224_in22ft1k 96.360 96.350 3.640 3.650 99.590 99.520 0.410 0.480 47.67 129.14 384 224 1.000 0.875 bicubic
69 tf_efficientnet_b7_ap 96.350 3.650 99.600 99.590 0.400 0.410 66.35 600 0.949 bicubic
70 resmlp_big_24_224_in22ft1k xcit_small_24_p16_384_dist 96.340 3.660 99.510 99.580 0.490 0.420 129.14 47.67 224 384 0.875 1.000 bicubic
seresnet152d 96.330 3.670 99.510 0.490 66.84 320 1.000 bicubic
71 xcit_small_12_p16_384_dist 96.330 3.670 99.490 0.510 26.25 384 1.000 bicubic
72 xcit_large_24_p16_224_dist 96.310 96.320 3.690 3.680 99.500 0.500 189.10 224 1.000 bicubic
73 seresnet152d 96.310 3.690 99.510 0.490 66.84 320 1.000 bicubic
74 vit_base_patch16_224 96.300 3.700 99.560 0.440 86.57 224 0.900 bicubic
75 resnetv2_50x3_bitm tf_efficientnet_b6 96.290 3.710 99.630 99.520 0.370 0.480 217.32 43.04 448 528 1.000 0.942 bilinear bicubic
resnetv2_101x3_bitm 96.290 3.710 99.580 0.420 387.93 448 1.000 bilinear
tf_efficientnet_b6 96.280 3.720 99.520 0.480 43.04 528 0.942 bicubic
76 swsl_resnext101_32x16d 96.280 3.720 99.500 0.500 194.03 224 0.875 bilinear
77 efficientnetv2_rw_m 96.270 3.730 99.560 0.440 53.24 416 1.000 bicubic
78 resnetv2_50x3_bitm 96.270 3.730 99.630 0.370 217.32 448 1.000 bilinear
79 xcit_medium_24_p16_224_dist 96.260 3.740 99.400 0.600 84.40 224 1.000 bicubic
80 xcit_tiny_24_p8_384_dist resnetv2_101x3_bitm 96.250 3.750 99.440 99.580 0.560 0.420 12.11 387.93 384 448 1.000 bicubic bilinear
81 resnetrs350 96.240 3.760 99.470 0.530 163.96 384 1.000 bicubic
82 swsl_resnext101_32x8d 96.230 96.240 3.770 3.760 99.590 0.410 88.79 224 0.875 bilinear
83 vit_large_r50_s32_224 xcit_tiny_24_p8_384_dist 96.190 96.240 3.810 3.760 99.530 99.440 0.470 0.560 328.99 12.11 224 384 0.900 1.000 bicubic
84 resnetv2_152x2_bit_teacher_384 96.170 96.190 3.830 3.810 99.510 99.500 0.490 0.500 236.34 384 1.000 bicubic
85 resnest269e vit_large_r50_s32_224 96.120 96.180 3.880 3.820 99.520 99.530 0.480 0.470 110.93 328.99 416 224 0.928 0.900 bicubic
86 crossvit_18_dagger_408 96.110 96.130 3.890 3.870 99.470 0.530 44.61 408 1.000 bicubic
87 resnest269e 96.130 3.870 99.520 0.480 110.93 416 0.928 bicubic
88 resnet200d 96.110 3.890 99.460 0.540 64.69 320 1.000 bicubic
89 tf_efficientnet_b3_ns 96.110 96.100 3.890 3.900 99.470 99.480 0.530 0.520 12.23 300 0.904 bicubic
90 tf_efficientnet_b5_ap 96.080 3.920 99.540 0.460 30.39 456 0.934 bicubic
91 resnest200e xcit_large_24_p8_224 96.080 3.920 99.470 99.150 0.530 0.850 70.20 188.93 320 224 0.909 1.000 bicubic
92 pit_b_distilled_224 96.080 96.070 3.920 3.930 99.380 0.620 74.79 224 0.900 bicubic
93 resnetrs270 resnest200e 96.070 3.930 99.480 0.520 129.86 70.20 352 320 1.000 0.909 bicubic
94 xcit_large_24_p8_224 resnetrs270 96.060 3.940 99.150 99.480 0.850 0.520 188.93 129.86 224 352 1.000 bicubic
95 vit_small_r26_s32_384 96.060 3.940 99.550 0.450 36.47 384 1.000 bicubic
96 swsl_resnext101_32x4d 96.050 96.040 3.950 3.960 99.540 99.530 0.460 0.470 44.18 224 0.875 bilinear
97 vit_base_patch16_224_miil 96.040 96.030 3.960 3.970 99.350 0.650 86.54 224 0.875 bilinear
98 convnext_large 96.020 3.980 99.470 0.530 197.77 224 0.875 bicubic
99 cait_xs24_384 96.010 3.990 99.430 0.570 26.67 384 1.000 bicubic
100 resnetrs200 regnetz_d8 96.000 96.010 4.000 3.990 99.440 99.520 0.560 0.480 93.21 23.37 320 1.000 bicubic
101 resnetrs200 95.990 4.010 99.440 0.560 93.21 320 1.000 bicubic
102 tf_efficientnet_b5 95.980 4.020 99.450 0.550 30.39 456 0.934 bicubic
103 vit_small_patch16_384 95.980 4.020 99.600 99.590 0.400 0.410 22.20 384 1.000 bicubic
104 resnetrs152 95.960 4.040 99.380 0.620 86.62 320 1.000 bicubic
105 xcit_small_12_p8_224_dist 95.960 4.040 99.420 0.580 26.21 224 1.000 bicubic
106 ig_resnext101_32x8d convnext_base 95.950 4.050 99.390 99.380 0.610 0.620 88.79 88.59 224 0.875 bilinear bicubic
107 eca_nfnet_l1 ig_resnext101_32x8d 95.920 95.940 4.080 4.060 99.500 99.380 0.500 0.620 41.41 88.79 320 224 1.000 0.875 bicubic bilinear
108 eca_nfnet_l1 95.930 4.070 99.490 0.510 41.41 320 1.000 bicubic
109 xcit_small_24_p8_224 95.910 4.090 99.180 0.820 47.63 224 1.000 bicubic
110 vit_base_patch32_384 95.910 95.900 4.090 4.100 99.440 0.560 88.30 384 1.000 bicubic
111 regnety_160 95.900 95.880 4.100 4.120 99.560 0.440 83.59 288 1.000 bicubic
xcit_medium_24_p8_224 95.870 4.130 99.090 0.910 84.32 224 1.000 bicubic
112 resmlp_big_24_distilled_224 95.870 4.130 99.440 0.560 129.14 224 0.875 bicubic
113 regnetz_d resnet152d 95.860 95.870 4.140 4.130 99.440 99.430 0.560 0.570 27.58 60.21 320 0.950 1.000 bicubic
114 resnet152d xcit_medium_24_p8_224 95.850 95.870 4.150 4.130 99.430 99.080 0.570 0.920 60.21 84.32 320 224 1.000 bicubic
115 crossvit_15_dagger_408 regnetz_d32 95.820 95.860 4.180 4.140 99.300 99.430 0.700 0.570 28.50 27.58 408 320 1.000 0.950 bicubic
116 xcit_small_24_p16_224_dist crossvit_15_dagger_408 95.810 95.820 4.190 4.180 99.340 99.310 0.660 0.690 47.67 28.50 224 408 1.000 bicubic
117 deit_base_distilled_patch16_224 xcit_small_24_p16_224_dist 95.780 95.800 4.220 4.200 99.280 99.340 0.720 0.660 87.34 47.67 224 0.900 1.000 bicubic
118 deit_base_distilled_patch16_224 95.750 4.250 99.280 0.720 87.34 224 0.900 bicubic
119 resnet101d 95.750 4.250 99.440 0.560 44.57 320 1.000 bicubic
120 xcit_small_12_p16_224_dist resnetv2_152x2_bit_teacher 95.750 4.250 99.290 99.430 0.710 0.570 26.25 236.34 224 1.000 0.875 bicubic
121 resnetv2_152x2_bit_teacher xcit_small_12_p16_224_dist 95.730 95.740 4.270 4.260 99.430 99.300 0.570 0.700 236.34 26.25 224 0.875 1.000 bicubic
twins_pcpvt_large 95.720 4.280 99.490 0.510 60.99 224 0.900 bicubic
122 twins_svt_large 95.720 4.280 99.370 0.630 99.27 224 0.900 bicubic
123 twins_pcpvt_large 95.720 4.280 99.490 0.510 60.99 224 0.900 bicubic
124 swin_small_patch4_window7_224 95.720 4.280 99.290 0.710 49.61 224 0.900 bicubic
125 tf_efficientnetv2_s 95.710 4.290 99.400 0.600 21.46 384 1.000 bicubic
dm_nfnet_f0 95.710 4.290 99.330 0.670 71.49 256 0.900 bicubic
126 efficientnetv2_rw_s 95.700 4.300 99.380 0.620 23.94 384 1.000 bicubic
127 deit_base_patch16_384 dm_nfnet_f0 95.660 95.690 4.340 4.310 99.240 99.330 0.760 0.670 86.86 71.49 384 256 1.000 0.900 bicubic
128 cait_s24_224 95.640 95.650 4.360 4.350 99.390 0.610 46.92 224 1.000 bicubic
129 tf_efficientnet_b4 deit_base_patch16_384 95.590 95.650 4.410 4.350 99.320 99.240 0.680 0.760 19.34 86.86 380 384 0.922 1.000 bicubic
130 swsl_resnext50_32x4d convnext_small 95.590 95.630 4.410 4.370 99.440 99.260 0.560 0.740 25.03 50.22 224 0.875 bilinear bicubic
131 resnest101e swsl_resnext50_32x4d 95.580 95.600 4.420 4.400 99.270 99.440 0.730 0.560 48.28 25.03 256 224 0.875 bilinear
132 tf_efficientnet_b4 95.590 4.410 99.330 0.670 19.34 380 0.922 bicubic
133 twins_svt_base 95.570 4.430 99.230 0.770 56.07 224 0.900 bicubic
134 efficientnet_b4 resnest101e 95.540 95.560 4.460 4.440 99.400 99.270 0.600 0.730 19.34 48.28 384 256 1.000 0.875 bicubic bilinear
135 jx_nest_small resnet152 95.540 95.560 4.460 4.440 99.230 99.270 0.770 0.730 38.35 60.19 224 0.875 0.950 bicubic
136 jx_nest_base 95.530 95.540 4.470 4.460 99.300 0.700 67.72 224 0.875 bicubic
137 tf_efficientnet_b2_ns jx_nest_small 95.530 95.540 4.470 4.460 99.340 99.220 0.660 0.780 9.11 38.35 260 224 0.890 0.875 bicubic
138 tresnet_xl_448 efficientnet_b4 95.520 4.480 99.340 99.400 0.660 0.600 78.44 19.34 448 384 0.875 1.000 bilinear bicubic
139 tf_efficientnet_b4_ap tf_efficientnet_b2_ns 95.500 95.520 4.500 4.480 99.390 99.340 0.610 0.660 19.34 9.11 380 260 0.922 0.890 bicubic
140 regnety_032 tresnet_xl_448 95.470 95.510 4.530 4.490 99.320 99.340 0.680 0.660 19.44 78.44 288 448 1.000 0.875 bicubic bilinear
141 twins_pcpvt_base tf_efficientnet_b4_ap 95.470 95.490 4.530 4.510 99.380 99.390 0.620 0.610 43.83 19.34 224 380 0.900 0.922 bicubic
142 xcit_tiny_24_p16_384_dist regnety_032 95.460 95.480 4.540 4.520 99.350 99.320 0.650 0.680 12.12 19.44 384 288 1.000 bicubic
143 xcit_tiny_24_p8_224_dist xcit_tiny_24_p16_384_dist 95.460 95.480 4.540 4.520 99.370 99.360 0.630 0.640 12.11 12.12 224 384 1.000 bicubic
144 twins_pcpvt_base 95.460 4.540 99.390 0.610 43.83 224 0.900 bicubic
145 xcit_tiny_24_p8_224_dist 95.460 4.540 99.360 0.640 12.11 224 1.000 bicubic
146 eca_nfnet_l0 95.450 4.550 99.390 0.610 24.14 288 1.000 bicubic
nfnet_l0 95.420 4.580 99.430 0.570 35.07 288 1.000 bicubic
147 xcit_small_12_p8_224 95.420 4.580 99.200 0.800 26.21 224 1.000 bicubic
148 regnetz_c ssl_resnext101_32x16d 95.410 4.590 99.310 99.410 0.690 0.590 13.46 194.03 320 224 0.940 0.875 bicubic bilinear
149 ssl_resnext101_32x16d tresnet_l_448 95.400 4.600 99.410 99.300 0.590 0.700 194.03 55.99 224 448 0.875 bilinear
150 tresnet_m nfnet_l0 95.400 95.390 4.600 4.610 99.150 99.420 0.850 0.580 31.39 35.07 224 288 0.875 1.000 bilinear bicubic
151 regnetz_c16 95.390 4.610 99.310 0.690 13.46 320 0.940 bicubic
152 resnetv2_50x1_bit_distilled 95.390 4.610 99.430 0.570 25.55 224 0.875 bicubic
153 tresnet_l_448 tresnet_m 95.390 95.380 4.610 4.620 99.280 99.150 0.720 0.850 55.99 31.39 448 224 0.875 bilinear
154 pnasnet5large 95.370 95.360 4.630 4.640 99.130 0.870 86.06 331 0.911 bicubic
155 xcit_tiny_12_p8_384_dist 95.340 4.660 99.340 0.660 6.71 384 1.000 bicubic
156 resnetv2_101x1_bitm ssl_resnext101_32x8d 95.330 4.670 99.380 99.310 0.620 0.690 44.54 88.79 448 224 1.000 0.875 bilinear
157 ssl_resnext101_32x8d resnetv2_101x1_bitm 95.320 4.680 99.310 99.370 0.690 0.630 88.79 44.54 224 448 0.875 1.000 bilinear
158 gc_efficientnetv2_rw_t 95.280 4.720 99.220 0.780 13.68 288 1.000 bicubic
159 vit_large_patch32_384 resnetrs101 95.250 4.750 99.320 99.210 0.680 0.790 306.63 63.62 384 288 1.000 0.940 bicubic
160 cait_xxs36_384 vit_large_patch32_384 95.240 4.760 99.330 99.320 0.670 0.680 17.37 306.63 384 1.000 bicubic
161 resnetrs101 cait_xxs36_384 95.230 4.770 99.210 99.320 0.790 0.680 63.62 17.37 288 384 0.940 1.000 bicubic
162 swsl_resnet50 levit_384 95.220 95.210 4.780 4.790 99.400 99.160 0.600 0.840 25.56 39.13 224 0.875 0.900 bilinear bicubic
163 levit_384 resnet51q 95.200 4.800 99.160 99.280 0.840 0.720 39.13 35.70 224 288 0.900 1.000 bicubic bilinear
164 resnet51q swsl_resnet50 95.190 95.200 4.810 4.800 99.280 99.390 0.720 0.610 35.70 25.56 288 224 1.000 0.875 bilinear
165 crossvit_18_dagger_240 95.180 95.190 4.820 4.810 99.120 0.880 44.27 240 0.875 bicubic
nasnetalarge 95.170 4.830 99.130 0.870 88.75 331 0.911 bicubic
166 ecaresnet101d 95.160 4.840 99.230 0.770 44.57 224 0.875 bicubic
167 efficientnet_b3 ssl_resnext101_32x4d 95.150 95.160 4.850 4.840 99.210 99.310 0.790 0.690 12.23 44.18 320 224 1.000 0.875 bicubic bilinear
168 ssl_resnext101_32x4d nasnetalarge 95.140 95.150 4.860 4.850 99.310 99.130 0.690 0.870 44.18 88.75 224 331 0.875 0.911 bilinear bicubic
169 xcit_medium_24_p16_224 efficientnet_b3 95.130 4.870 98.930 99.210 1.070 0.790 84.40 12.23 224 320 1.000 bicubic
170 tf_efficientnetv2_b3 fbnetv3_g 95.120 95.130 4.880 4.870 99.190 99.200 0.810 0.800 14.36 16.62 300 288 0.904 0.950 bicubic bilinear
171 vit_small_r26_s32_224 95.120 95.130 4.880 4.870 99.220 0.780 36.43 224 0.900 bicubic
172 coat_lite_small xcit_medium_24_p16_224 95.110 95.130 4.890 4.870 99.030 98.920 0.970 1.080 19.84 84.40 224 0.900 1.000 bicubic
173 ecaresnet50t tf_efficientnetv2_b3 95.110 95.120 4.890 4.880 99.290 99.200 0.710 0.800 25.57 14.36 320 300 0.950 0.904 bicubic
174 resnet61q 95.110 4.890 99.080 0.920 36.85 288 1.000 bicubic
175 convit_base 95.100 4.900 99.130 99.140 0.870 0.860 86.54 224 0.875 bicubic
176 tresnet_xl xcit_small_24_p16_224 95.080 4.920 99.250 99.060 0.750 0.940 78.44 47.67 224 0.875 1.000 bilinear bicubic
177 efficientnetv2_rw_t coat_lite_small 95.080 4.920 99.220 99.030 0.780 0.970 13.65 19.84 288 224 1.000 0.900 bicubic
178 xcit_small_24_p16_224 crossvit_18_240 95.070 4.930 99.060 99.120 0.940 0.880 47.67 43.27 224 240 1.000 0.875 bicubic
179 crossvit_base_240 95.060 95.070 4.940 4.930 98.980 1.020 105.03 240 0.875 bicubic
180 crossvit_18_240 ecaresnet50t 95.050 95.070 4.950 4.930 99.120 99.290 0.880 0.710 43.27 25.57 240 320 0.875 0.950 bicubic
181 deit_base_patch16_224 efficientnetv2_rw_t 95.020 95.070 4.980 4.930 98.970 99.220 1.030 0.780 86.57 13.65 224 288 0.900 1.000 bicubic
182 visformer_small tresnet_xl 94.970 95.060 5.030 4.940 99.210 99.260 0.790 0.740 40.22 78.44 224 0.900 0.875 bicubic bilinear
183 crossvit_15_dagger_240 halo2botnet50ts_256 94.970 95.010 5.030 4.990 99.150 99.040 0.850 0.960 28.21 22.64 240 256 0.875 0.950 bicubic
184 tf_efficientnet_b3_ap deit_base_patch16_224 94.960 95.010 5.040 4.990 99.110 98.980 0.890 1.020 12.23 86.57 300 224 0.904 0.900 bicubic
185 convnext_tiny 94.990 5.010 99.200 0.800 28.59 224 0.875 bicubic
186 crossvit_15_dagger_240 94.980 5.020 99.160 0.840 28.21 240 0.875 bicubic
187 convmixer_1536_20 94.970 5.030 99.170 0.830 51.63 224 0.960 bicubic
188 resnet101 94.970 5.030 99.080 0.920 44.55 224 0.950 bicubic
189 tf_efficientnet_b3_ap 94.970 5.030 99.110 0.890 12.23 300 0.904 bicubic
190 visformer_small 94.960 5.040 99.210 0.790 40.22 224 0.900 bicubic
191 xcit_large_24_p16_224 94.950 5.050 98.830 1.170 189.10 224 1.000 bicubic
192 convmixer_1536_20 jx_nest_tiny 94.950 5.050 99.170 99.100 0.830 0.900 51.63 17.06 224 0.960 0.875 bicubic
193 cait_xxs24_384 94.940 5.060 99.130 0.870 12.03 384 1.000 bicubic
194 jx_nest_tiny gernet_l 94.940 94.930 5.060 5.070 99.100 99.200 0.900 0.800 17.06 31.08 224 256 0.875 bicubic bilinear
195 resnetv2_101 94.930 5.070 99.110 99.120 0.890 0.880 44.54 224 0.950 bicubic
196 convit_small 94.920 5.080 99.120 99.100 0.880 0.900 27.78 224 0.875 bicubic
197 gernet_l tf_efficientnet_b3 94.920 94.910 5.080 5.090 99.200 99.110 0.800 0.890 31.08 12.23 256 300 0.875 0.904 bilinear bicubic
tf_efficientnet_b3 94.910 5.090 99.100 0.900 12.23 300 0.904 bicubic
vit_small_patch16_224 94.900 5.100 99.280 0.720 22.05 224 0.900 bicubic
198 tresnet_l 94.900 5.100 99.030 0.970 55.99 224 0.875 bilinear
199 mixer_b16_224_miil 94.890 94.880 5.110 5.120 99.080 0.920 59.88 224 0.875 bilinear
200 tf_efficientnet_b1_ns vit_small_patch16_224 94.880 5.120 99.250 99.270 0.750 0.730 7.79 22.05 240 224 0.882 0.900 bicubic
201 xcit_tiny_24_p8_224 94.870 94.880 5.130 5.120 99.190 0.810 12.11 224 1.000 bicubic
202 tf_efficientnet_lite4 94.870 5.130 99.090 0.910 13.01 380 0.920 bilinear
203 xcit_small_12_p16_224 tf_efficientnet_b1_ns 94.810 94.860 5.190 5.140 99.060 99.250 0.940 0.750 26.25 7.79 224 240 1.000 0.882 bicubic
204 seresnext50_32x4d xcit_small_12_p16_224 94.800 94.820 5.200 5.180 99.130 99.060 0.870 0.940 27.56 26.25 224 0.875 1.000 bicubic
205 pit_b_224 seresnext50_32x4d 94.790 94.810 5.210 5.190 98.810 99.130 1.190 0.870 73.76 27.56 224 0.900 0.875 bicubic
206 pit_b_224 94.790 5.210 98.820 1.180 73.76 224 0.900 bicubic
207 lamhalobotnet50ts_256 94.780 5.220 98.980 1.020 22.57 256 0.950 bicubic
208 twins_svt_small 94.770 5.230 99.080 0.920 24.06 224 0.900 bicubic
209 coat_mini 94.760 94.770 5.240 5.230 98.950 1.050 10.34 224 0.900 bicubic
210 resnetv2_50x1_bitm 94.760 94.750 5.240 5.250 99.180 0.820 25.55 448 1.000 bilinear
211 pit_s_distilled_224 94.750 5.250 99.180 0.820 24.04 224 0.900 bicubic
xcit_tiny_12_p8_224_dist 94.740 5.260 99.180 0.820 6.71 224 1.000 bicubic
212 legacy_senet154 94.730 5.270 99.100 0.900 115.09 224 0.875 bilinear
213 crossvit_15_240 94.720 5.280 99.080 0.920 27.53 240 0.875 bicubic
214 gluon_senet154 94.710 5.290 98.970 1.030 115.09 224 0.875 bicubic
215 halonet50ts 94.710 5.290 98.820 1.180 22.73 256 0.940 bicubic
216 resnest50d_4s2x40d 94.710 5.290 99.130 0.870 30.42 224 0.875 bicubic
217 xcit_tiny_12_p8_224_dist 94.710 5.290 99.180 0.820 6.71 224 1.000 bicubic
218 gluon_resnet152_v1s 94.700 5.300 99.060 0.940 60.32 224 0.875 bicubic
219 ssl_resnext50_32x4d 94.700 5.300 99.240 0.760 25.03 224 0.875 bilinear
220 gluon_senet154 regnetz_b16 94.700 94.680 5.300 5.320 98.970 99.160 1.030 0.840 115.09 9.72 224 288 0.875 0.940 bicubic
gluon_resnet152_v1s 94.690 5.310 99.050 0.950 60.32 224 0.875 bicubic
regnetz_b 94.690 5.310 99.160 0.840 9.72 288 0.940 bicubic
crossvit_15_240 94.680 5.320 99.070 0.930 27.53 240 0.875 bicubic
221 efficientnet_el 94.670 5.330 99.130 0.870 10.59 300 0.904 bicubic
222 rexnet_200 gluon_seresnext101_64x4d 94.670 94.660 5.330 5.340 99.090 98.980 0.910 1.020 16.37 88.23 224 0.875 bicubic
223 tresnet_m_448 rexnet_200 94.670 94.660 5.330 5.340 99.170 99.090 0.830 0.910 31.39 16.37 448 224 0.875 bilinear bicubic
224 gluon_seresnext101_64x4d wide_resnet50_2 94.660 5.340 98.970 99.050 1.030 0.950 88.23 68.88 224 0.875 bicubic
225 wide_resnet50_2 tresnet_m_448 94.650 5.350 99.050 99.150 0.950 0.850 68.88 31.39 224 448 0.875 bicubic bilinear
swin_tiny_patch4_window7_224 94.640 5.360 99.120 0.880 28.29 224 0.900 bicubic
226 resnest50d 94.620 5.380 99.030 0.970 27.48 224 0.875 bilinear
227 gcresnet50t swin_tiny_patch4_window7_224 94.610 94.620 5.390 5.380 98.990 99.120 1.010 0.880 25.90 28.29 256 224 0.900 bicubic
228 lamhalobotnet50ts_256 gcresnet50t 94.610 94.620 5.390 5.380 98.610 98.980 1.390 1.020 22.57 25.90 256 0.950 0.900 bicubic
229 twins_pcpvt_small deit_small_distilled_patch16_224 94.600 5.400 99.140 99.100 0.860 0.900 24.11 22.44 224 0.900 bicubic
230 deit_small_distilled_patch16_224 twins_pcpvt_small 94.590 94.600 5.410 5.400 99.090 99.150 0.910 0.850 22.44 24.11 224 0.900 bicubic
231 pit_s_224 vit_small_patch32_384 94.590 94.600 5.410 5.400 98.930 99.140 1.070 0.860 23.46 22.92 224 384 0.900 1.000 bicubic
232 crossvit_small_240 pit_s_224 94.590 5.410 99.120 98.920 0.880 1.080 26.86 23.46 240 224 0.875 0.900 bicubic
233 crossvit_small_240 94.580 5.420 99.120 0.880 26.86 240 0.875 bicubic
234 efficientnet_b3_pruned 94.580 5.420 99.070 0.930 9.86 300 0.904 bicubic
235 lambda_resnet50ts 94.570 5.430 98.650 1.350 21.54 256 0.950 bicubic
236 resnext50_32x4d 94.570 5.430 98.800 1.200 25.03 224 0.950 bicubic
237 tnt_s_patch16_224 94.570 5.430 99.180 0.820 23.76 224 0.900 bicubic
vit_small_patch32_384 94.570 5.430 99.140 0.860 22.92 384 1.000 bicubic
lambda_resnet50ts 94.560 5.440 98.650 1.350 21.54 256 0.950 bicubic
238 repvgg_b3 94.560 5.440 98.910 1.090 123.09 224 0.875 bilinear
239 gernet_m 94.550 5.450 98.920 98.930 1.080 1.070 21.14 224 0.875 bilinear
halo2botnet50ts_256 94.550 5.450 98.760 1.240 22.64 256 0.950 bicubic
240 resmlp_36_distilled_224 94.550 5.450 99.160 0.840 44.69 224 0.875 bicubic
241 xcit_tiny_12_p16_384_dist sehalonet33ts 94.540 94.530 5.460 5.470 99.170 98.760 0.830 1.240 6.72 13.69 384 256 1.000 0.940 bicubic
242 sehalonet33ts xcit_tiny_12_p16_384_dist 94.530 5.470 98.780 99.170 1.220 0.830 13.69 6.72 256 384 0.940 1.000 bicubic
243 regnety_320 94.500 94.520 5.500 5.480 99.170 0.830 145.05 224 0.875 bicubic
244 haloregnetz_b 94.500 94.520 5.500 5.480 98.960 1.040 11.68 224 0.940 bicubic
245 repvgg_b3g4 94.490 94.500 5.510 5.500 99.020 0.980 83.83 224 0.875 bilinear
246 gluon_resnet152_v1d ecaresnet101d_pruned 94.460 94.450 5.540 5.550 99.000 99.100 1.000 0.900 60.21 24.88 224 0.875 bicubic
247 ecaresnet101d_pruned gluon_seresnext101_32x4d 94.440 94.450 5.560 5.550 99.100 99.090 0.900 0.910 24.88 48.96 224 0.875 bicubic
248 gluon_seresnext101_32x4d gluon_resnet152_v1d 94.430 94.440 5.570 5.560 99.090 99.010 0.910 0.990 48.96 60.21 224 0.875 bicubic
249 convmixer_768_32 94.430 5.570 99.110 0.890 21.11 224 0.960 bicubic
halonet50ts 94.420 5.580 98.760 1.240 22.73 256 0.940 bicubic
250 gcresnext50ts 94.410 5.590 98.990 1.010 15.67 256 0.900 bicubic
251 levit_256 94.410 94.400 5.590 5.600 99.060 0.940 18.89 224 0.900 bicubic
252 resnest50d_1s4x24d 94.390 5.610 99.070 0.930 25.68 224 0.875 bicubic
253 vit_base_patch32_224 94.390 5.610 99.060 0.940 88.22 224 0.900 bicubic
254 inception_v4 94.380 5.620 98.820 1.180 42.68 299 0.875 bicubic
255 nf_resnet50 94.380 5.620 99.070 0.930 25.56 288 0.940 bicubic
resnest50d_1s4x24d 94.380 5.620 99.060 0.940 25.68 224 0.875 bicubic
vit_base_patch32_224 94.380 5.620 99.060 0.940 88.22 224 0.900 bicubic
256 efficientnet_b2 94.370 5.630 99.050 0.950 9.11 288 1.000 bicubic
257 inception_v4 tf_efficientnet_el 94.370 94.360 5.630 5.640 98.820 99.100 1.180 0.900 42.68 10.59 299 300 0.875 0.904 bicubic
258 xcit_tiny_12_p8_224 94.370 94.360 5.630 5.640 99.070 0.930 6.71 224 1.000 bicubic
259 tf_efficientnet_el gluon_resnext101_64x4d 94.350 5.650 99.090 98.880 0.910 1.120 10.59 83.46 300 224 0.904 0.875 bicubic
260 resmlp_24_distilled_224 94.340 5.660 99.090 0.910 30.02 224 0.875 bicubic
261 gluon_resnext101_64x4d inception_resnet_v2 94.330 94.340 5.670 5.660 98.880 98.800 1.120 1.200 83.46 55.84 224 299 0.875 0.897 bicubic
262 inception_resnet_v2 sebotnet33ts_256 94.320 94.310 5.680 5.690 98.800 98.600 1.200 1.400 55.84 13.70 299 256 0.897 0.940 bicubic
263 ssl_resnet50 94.320 94.310 5.680 5.690 99.160 99.150 0.840 0.850 25.56 224 0.875 bilinear
resnetv2_50 94.290 5.710 98.930 1.070 25.55 224 0.950 bicubic
regnetx_120 94.290 5.710 99.200 0.800 46.11 224 0.875 bicubic
tf_efficientnet_b2_ap 94.280 5.720 98.950 1.050 9.11 260 0.890 bicubic
rexnet_150 94.270 5.730 99.090 0.910 9.73 224 0.875 bicubic
264 resmlp_big_24_224 94.270 5.730 98.820 1.180 129.14 224 0.875 bicubic
265 seresnet33ts resnetv2_50 94.260 94.270 5.740 5.730 98.780 98.930 1.220 1.070 19.78 25.55 256 224 0.900 0.950 bicubic
266 mixnet_xl rexnet_150 94.220 94.270 5.780 5.730 98.810 99.080 1.190 0.920 11.90 9.73 224 0.875 bicubic
267 xcit_tiny_24_p16_224_dist seresnet33ts 94.220 94.270 5.780 5.730 98.960 98.780 1.040 1.220 12.12 19.78 224 256 1.000 0.900 bicubic
268 ecaresnet50d tf_efficientnet_b2_ap 94.210 94.270 5.790 5.730 99.010 98.950 0.990 1.050 25.58 9.11 224 260 0.875 0.890 bicubic
269 regnetx_320 regnetx_120 94.210 94.260 5.790 5.740 99.050 99.190 0.950 0.810 107.81 46.11 224 0.875 bicubic
270 tf_efficientnet_b2 mixnet_xl 94.200 94.230 5.800 5.770 99.030 98.820 0.970 1.180 9.11 11.90 260 224 0.890 0.875 bicubic
271 gluon_resnet101_v1s regnetx_320 94.180 94.220 5.820 5.780 99.020 99.050 0.980 0.950 44.67 107.81 224 0.875 bicubic
272 gluon_resnet101_v1d tf_efficientnet_b2 94.180 94.210 5.820 5.790 98.950 99.030 1.050 0.970 44.57 9.11 224 260 0.875 0.890 bicubic
273 xcit_tiny_24_p16_224_dist 94.210 5.790 98.960 1.040 12.12 224 1.000 bicubic
274 ecaresnet50d 94.200 5.800 99.020 0.980 25.58 224 0.875 bicubic
275 dpn92 94.180 5.820 98.930 1.070 37.67 224 0.875 bicubic
276 gluon_seresnext50_32x4d resnet50_gn 94.180 5.820 98.910 98.920 1.090 1.080 27.56 25.56 224 0.875 0.940 bicubic
277 legacy_seresnext101_32x4d gluon_seresnext50_32x4d 94.170 5.830 98.970 98.910 1.030 1.090 48.96 27.56 224 0.875 bilinear bicubic
278 regnety_064 gluon_resnet101_v1s 94.140 94.170 5.860 5.830 99.030 99.010 0.970 0.990 30.58 44.67 224 0.875 bicubic
279 gluon_resnet101_v1d 94.170 5.830 98.940 1.060 44.57 224 0.875 bicubic
280 ecaresnetlight 94.140 5.860 98.950 1.050 30.16 224 0.875 bicubic
281 ens_adv_inception_resnet_v2 94.140 94.130 5.860 5.870 98.790 1.210 55.84 299 0.897 bicubic
282 regnety_064 94.130 5.870 99.030 0.970 30.58 224 0.875 bicubic
283 gluon_resnext101_32x4d 94.120 5.880 98.940 1.060 44.18 224 0.875 bicubic
284 tf_efficientnet_lite3 legacy_seresnext101_32x4d 94.110 94.120 5.890 5.880 98.960 98.970 1.040 1.030 8.20 48.96 300 224 0.904 0.875 bilinear
285 cspdarknet53 tf_efficientnet_lite3 94.100 94.120 5.900 5.880 98.980 98.960 1.020 1.040 27.64 8.20 256 300 0.887 0.904 bilinear
286 seresnet50 cspdarknet53 94.080 94.090 5.920 5.910 98.950 98.980 1.050 1.020 28.09 27.64 224 256 0.875 0.887 bicubic bilinear
287 efficientnet_el_pruned 94.080 5.920 99.020 0.980 10.59 300 0.904 bicubic
288 seresnet50 94.080 5.920 98.960 1.040 28.09 224 0.875 bicubic
289 resnet50d 94.070 5.930 98.920 1.080 25.58 224 0.875 bicubic
290 regnety_120 tf_efficientnetv2_b2 94.060 94.070 5.940 5.930 99.020 98.930 0.980 1.070 51.82 10.10 224 260 0.875 0.890 bicubic
291 tf_efficientnetv2_b2 gluon_resnet152_v1b 94.060 94.030 5.940 5.970 98.940 98.740 1.060 1.260 10.10 60.19 260 224 0.890 0.875 bicubic
292 efficientnet_el_pruned hrnet_w48 94.060 94.030 5.940 5.970 99.020 99.040 0.980 0.960 10.59 77.47 300 224 0.904 0.875 bicubic bilinear
293 gluon_xception65 94.040 94.020 5.960 5.980 99.030 99.020 0.970 0.980 39.92 299 0.903 bicubic
294 resnetrs50 94.030 94.020 5.970 5.980 98.830 98.850 1.170 1.150 35.69 224 0.910 bicubic
295 hrnet_w48 regnety_120 94.030 94.010 5.970 5.990 99.030 0.970 77.47 51.82 224 0.875 bilinear bicubic
296 gluon_resnet152_v1b dla102x2 94.020 94.000 5.980 6.000 98.750 99.030 1.250 0.970 60.19 41.28 224 0.875 bicubic bilinear
dla102x2 94.010 5.990 99.030 0.970 41.28 224 0.875 bilinear
297 deit_small_patch16_224 93.990 6.010 98.960 1.040 22.05 224 0.900 bicubic
298 dpn107 93.960 6.040 98.840 98.830 1.160 1.170 86.92 224 0.875 bicubic
299 resnet50 ecaresnet26t 93.950 6.050 98.470 98.920 1.530 1.080 25.56 16.01 224 320 0.950 bicubic
300 skresnext50_32x4d 93.950 6.050 98.830 1.170 27.48 224 0.875 bicubic
301 cait_xxs36_224 93.940 93.930 6.060 6.070 98.880 98.890 1.120 1.110 17.30 224 1.000 bicubic
302 ecaresnet26t resnet50 93.930 6.070 98.930 98.470 1.070 1.530 16.01 25.56 320 224 0.950 bicubic
303 dpn98 93.930 93.920 6.070 6.080 98.920 1.080 61.57 224 0.875 bicubic
304 xception71 gluon_resnet152_v1c 93.900 93.890 6.100 6.110 98.950 98.800 1.050 1.200 42.34 60.21 299 224 0.903 0.875 bicubic
305 regnety_080 regnetx_160 93.900 93.890 6.100 6.110 98.990 99.090 1.010 0.910 39.18 54.28 224 0.875 bicubic
306 regnetx_160 regnety_080 93.900 93.890 6.100 6.110 99.080 99.000 0.920 1.000 54.28 39.18 224 0.875 bicubic
307 vit_base_patch16_sam_224 vit_base_patch16_224_sam 93.890 6.110 98.890 1.110 86.57 224 0.900 bicubic
308 nf_regnet_b1 93.890 93.880 6.110 6.120 98.750 98.740 1.250 1.260 10.22 288 0.900 bicubic
309 gluon_resnet152_v1c xception71 93.880 6.120 98.800 98.950 1.200 1.050 60.21 42.34 224 299 0.875 0.903 bicubic
310 eca_resnet33ts cspresnet50 93.870 93.860 6.130 6.140 98.890 98.860 1.110 1.140 19.68 21.62 256 0.900 0.887 bicubic bilinear
311 resnext50_32x4d eca_resnet33ts 93.850 93.860 6.150 6.140 98.820 98.890 1.180 1.110 25.03 19.68 224 256 0.875 0.900 bicubic
312 cspresnet50 xcit_tiny_24_p16_224 93.850 6.150 98.870 98.760 1.130 1.240 21.62 12.12 256 224 0.887 1.000 bilinear bicubic
xcit_tiny_24_p16_224 93.850 6.150 98.770 1.230 12.12 224 1.000 bicubic
hrnet_w64 93.850 6.150 98.930 1.070 128.06 224 0.875 bilinear
313 ese_vovnet39b 93.850 6.150 98.900 1.100 24.57 224 0.875 bicubic
314 gcresnet33ts fbnetv3_d 93.820 93.840 6.180 6.160 98.930 98.910 1.070 1.090 19.88 10.31 256 0.900 0.950 bicubic bilinear
315 hrnet_w64 93.840 6.160 98.930 1.070 128.06 224 0.875 bilinear
316 ecaresnet50d_pruned 93.820 6.180 99.000 1.000 19.94 224 0.875 bicubic
317 repvgg_b2g4 gcresnet33ts 93.820 6.180 98.920 98.910 1.080 1.090 61.76 19.88 224 256 0.875 0.900 bilinear bicubic
318 resnext50d_32x4d repvgg_b2g4 93.800 93.810 6.200 6.190 98.730 98.930 1.270 1.070 25.05 61.76 224 0.875 bicubic bilinear
319 resnext50d_32x4d 93.810 6.190 98.740 1.260 25.05 224 0.875 bicubic
320 efficientnet_b2_pruned 93.800 6.200 98.910 1.090 8.31 260 0.890 bicubic
321 dla169 93.800 6.200 98.840 1.160 53.39 224 0.875 bilinear
322 regnetx_080 93.790 6.210 98.900 1.100 39.57 224 0.875 bicubic
323 dla169 cspresnext50 93.780 6.220 98.830 98.840 1.170 1.160 53.39 20.57 224 0.875 bilinear
324 gluon_resnet101_v1b 93.770 6.230 98.690 1.310 44.55 224 0.875 bicubic
325 resnext101_32x8d 93.770 6.230 98.950 1.050 88.79 224 0.875 bilinear
326 gluon_resnet101_v1b xception65 93.770 93.760 6.230 6.240 98.720 98.860 1.280 1.140 44.55 39.92 224 299 0.875 0.903 bicubic
327 cspresnext50 dpn131 93.770 93.750 6.230 6.250 98.840 1.160 20.57 79.25 224 0.875 bilinear bicubic
328 dpn131 efficientnet_em 93.760 93.740 6.240 6.260 98.850 98.930 1.150 1.070 79.25 6.90 224 240 0.875 0.882 bicubic
329 tf_efficientnet_b0_ns 93.760 93.740 6.240 6.260 98.980 1.020 5.29 224 0.875 bicubic
efficientnet_em 93.750 6.250 98.920 1.080 6.90 240 0.882 bicubic
xception65 93.740 6.260 98.870 1.130 39.92 299 0.903 bicubic
330 wide_resnet101_2 93.720 6.280 98.810 1.190 126.89 224 0.875 bilinear
331 hrnet_w40 93.710 6.290 98.800 1.200 57.56 224 0.875 bilinear
332 levit_192 resnetblur50 93.710 6.290 98.790 98.810 1.210 1.190 10.95 25.56 224 0.900 0.875 bicubic
resnetblur50 93.710 6.290 98.800 1.200 25.56 224 0.875 bicubic
333 tf_efficientnet_b1 93.710 6.290 98.800 1.200 7.79 240 0.882 bicubic
334 tf_efficientnetv2_b1 93.700 93.710 6.300 6.290 98.810 98.820 1.190 1.180 8.14 240 0.882 bicubic
335 rexnet_130 levit_192 93.690 93.710 6.310 6.290 98.720 98.800 1.280 1.200 7.56 10.95 224 0.875 0.900 bicubic
336 regnetx_040 gluon_resnet101_v1c 93.670 93.690 6.330 6.310 98.950 98.760 1.050 1.240 22.12 44.57 224 0.875 bicubic
337 resmlp_36_224 regnetx_040 93.670 93.680 6.330 6.320 98.950 98.940 1.050 1.060 44.69 22.12 224 0.875 bicubic
338 gluon_resnext50_32x4d rexnet_130 93.670 93.680 6.330 6.320 98.700 98.710 1.300 1.290 25.03 7.56 224 0.875 bicubic
339 gluon_resnet101_v1c resmlp_36_224 93.660 93.650 6.340 6.350 98.760 98.950 1.240 1.050 44.57 44.69 224 0.875 bicubic
340 xception fbnetv3_b 93.650 6.350 98.770 98.910 1.230 1.090 22.86 8.60 299 256 0.897 0.950 bicubic bilinear
341 regnetx_064 gluon_resnext50_32x4d 93.650 6.350 99.050 98.690 0.950 1.310 26.21 25.03 224 0.875 bicubic
342 tf_efficientnet_b1_ap xception 93.640 6.360 98.780 98.760 1.220 1.240 7.79 22.86 240 299 0.882 0.897 bicubic
343 hrnet_w44 resnet33ts 93.620 93.630 6.380 6.370 98.950 98.760 1.050 1.240 67.06 19.68 224 256 0.875 0.900 bilinear bicubic
344 resnet33ts tf_efficientnet_b1_ap 93.620 93.630 6.380 6.370 98.770 98.800 1.230 1.200 19.68 7.79 256 240 0.900 0.882 bicubic
345 halonet26t dpn68b 93.610 93.620 6.390 6.380 98.640 98.700 1.360 1.300 12.48 12.61 256 224 0.950 0.875 bicubic
346 regnety_040 regnetx_064 93.610 93.620 6.390 6.380 98.960 99.050 1.040 0.950 20.65 26.21 224 0.875 bicubic
347 dpn68b res2net50_26w_6s 93.600 93.610 6.400 6.390 98.710 98.740 1.290 1.260 12.61 37.05 224 0.875 bicubic bilinear
348 gluon_resnet50_v1s hrnet_w44 93.590 93.610 6.410 6.390 98.830 98.960 1.170 1.040 25.68 67.06 224 0.875 bicubic bilinear
349 gluon_inception_v3 regnety_040 93.590 93.610 6.410 6.390 98.840 98.950 1.160 1.050 23.83 20.65 299 224 0.875 bicubic
350 res2net50_26w_6s halonet26t 93.580 93.600 6.420 6.400 98.740 98.640 1.260 1.360 37.05 12.48 224 256 0.875 0.950 bilinear bicubic
351 tf_efficientnet_cc_b1_8e gluon_resnet50_v1s 93.580 93.590 6.420 6.410 98.690 98.840 1.310 1.160 39.72 25.68 240 224 0.882 0.875 bicubic
352 repvgg_b2 93.570 93.590 6.430 6.410 99.070 0.930 89.02 224 0.875 bilinear
353 dla60_res2next 93.570 6.430 98.790 1.210 17.03 224 0.875 bilinear
354 resnet32ts 93.570 6.430 98.750 1.250 17.96 256 0.900 bicubic
355 dla60_res2next tf_efficientnet_cc_b1_8e 93.560 93.570 6.440 6.430 98.800 98.690 1.200 1.310 17.03 39.72 224 240 0.875 0.882 bilinear bicubic
356 gluon_resnet50_v1d eca_halonext26ts 93.550 93.560 6.450 6.440 98.710 98.680 1.290 1.320 25.58 10.76 224 256 0.875 0.940 bicubic
357 res2net101_26w_4s gluon_inception_v3 93.520 93.540 6.480 6.460 98.630 98.830 1.370 1.170 45.21 23.83 224 299 0.875 bilinear bicubic
358 dla102x gluon_resnet50_v1d 93.510 93.530 6.490 6.470 98.850 98.710 1.150 1.290 26.31 25.58 224 0.875 bilinear bicubic
359 dla102x 93.520 6.480 98.850 1.150 26.31 224 0.875 bilinear
360 res2net101_26w_4s 93.520 6.480 98.600 1.400 45.21 224 0.875 bilinear
361 coat_tiny 93.510 6.490 98.690 1.310 5.50 224 0.900 bicubic
362 gmlp_s16_224 93.510 6.490 98.780 1.220 19.42 224 0.875 bicubic
363 coat_tiny selecsls60b 93.500 6.500 98.680 98.840 1.320 1.160 5.50 32.77 224 0.900 0.875 bicubic
364 cait_xxs24_224 93.490 6.510 98.770 1.230 11.96 224 1.000 bicubic
365 coat_lite_mini xception41 93.490 93.480 6.510 6.520 98.780 98.750 1.220 1.250 11.01 26.97 224 299 0.900 0.903 bicubic
366 selecsls60b coat_lite_mini 93.480 93.460 6.520 6.540 98.840 98.780 1.160 1.220 32.77 11.01 224 0.875 0.900 bicubic
367 xception41 legacy_seresnet152 93.480 93.440 6.520 6.560 98.760 98.850 1.240 1.150 26.97 66.82 299 224 0.903 0.875 bicubic bilinear
368 legacy_seresnet152 resmlp_24_224 93.460 93.440 6.540 6.560 98.850 98.810 1.150 1.190 66.82 30.02 224 0.875 bilinear bicubic
369 lambda_resnet26rpt_256 botnet26t_256 93.440 93.430 6.560 6.570 98.880 98.660 1.120 1.340 10.99 12.49 256 0.940 0.950 bicubic
370 botnet26t_256 lambda_resnet26rpt_256 93.440 93.430 6.560 6.570 98.660 98.880 1.340 1.120 12.49 10.99 256 0.950 0.940 bicubic
371 res2net50_26w_8s legacy_seresnext50_32x4d 93.430 6.570 98.670 98.800 1.330 1.200 48.40 27.56 224 0.875 bilinear
372 resmlp_24_224 vit_tiny_patch16_384 93.430 6.570 98.810 98.830 1.190 1.170 30.02 5.79 224 384 0.875 1.000 bicubic
373 vit_tiny_patch16_384 res2net50_26w_8s 93.430 93.420 6.570 6.580 98.840 98.690 1.160 1.310 5.79 48.40 384 224 1.000 0.875 bicubic bilinear
374 hrnet_w30 repvgg_b1 93.410 6.590 98.830 98.790 1.170 1.210 37.71 57.42 224 0.875 bilinear
375 legacy_seresnext50_32x4d lambda_resnet26t 93.410 93.400 6.590 6.600 98.800 98.740 1.200 1.260 27.56 10.96 224 256 0.875 0.940 bilinear bicubic
376 repvgg_b1 hrnet_w30 93.410 93.390 6.590 6.610 98.780 98.830 1.220 1.170 57.42 37.71 224 0.875 bilinear
377 lambda_resnet26t dla60_res2net 93.400 93.380 6.600 6.620 98.760 98.860 1.240 1.140 10.96 20.85 256 224 0.940 0.875 bicubic bilinear
378 dla60_res2net eca_botnext26ts_256 93.380 93.360 6.620 6.640 98.830 98.700 1.170 1.300 20.85 10.59 224 256 0.875 0.950 bilinear bicubic
379 eca_botnext26ts_256 xcit_tiny_12_p16_224_dist 93.360 93.340 6.640 6.660 98.690 98.740 1.310 1.260 10.59 6.72 256 224 0.950 1.000 bicubic
380 xcit_tiny_12_p16_224_dist xcit_nano_12_p8_384_dist 93.340 93.280 6.660 6.720 98.750 98.850 1.250 1.150 6.72 3.05 224 384 1.000 bicubic
381 dla102 mixnet_l 93.290 93.270 6.710 6.730 98.780 98.700 1.220 1.300 33.27 7.33 224 0.875 bilinear bicubic
382 legacy_seresnet101 93.290 93.270 6.710 6.730 98.750 98.740 1.250 1.260 49.33 224 0.875 bilinear
383 mixnet_l dla102 93.290 93.260 6.710 6.740 98.710 98.770 1.290 1.230 7.33 33.27 224 0.875 bicubic bilinear
384 regnetx_032 93.270 93.250 6.730 6.750 98.740 98.730 1.260 1.270 15.30 224 0.875 bicubic
385 resnest26d pit_xs_distilled_224 93.260 93.240 6.740 6.760 98.840 98.830 1.160 1.170 17.07 11.00 224 0.875 0.900 bilinear bicubic
386 tv_resnet152 resnest26d 93.260 93.240 6.740 6.760 98.750 98.850 1.250 1.150 60.19 17.07 224 0.875 bilinear
387 xcit_nano_12_p8_384_dist tv_resnet152 93.250 93.240 6.750 6.760 98.850 98.750 1.150 1.250 3.05 60.19 384 224 1.000 0.875 bicubic bilinear
388 pit_xs_distilled_224 tf_inception_v3 93.230 93.200 6.770 6.800 98.820 98.480 1.180 1.520 11.00 23.83 224 299 0.900 0.875 bicubic
389 tf_inception_v3 dla60x 93.210 93.190 6.790 6.810 98.490 98.710 1.510 1.290 23.83 17.35 299 224 0.875 bicubic bilinear
dla60x 93.210 6.790 98.720 1.280 17.35 224 0.875 bilinear
tf_efficientnet_em 93.200 6.800 98.680 1.320 6.90 240 0.882 bicubic
390 res2net50_26w_4s 93.180 6.820 98.670 1.330 25.70 224 0.875 bilinear
391 eca_halonext26ts tf_efficientnet_em 93.140 93.180 6.860 6.820 98.690 98.670 1.310 1.330 10.76 6.90 256 240 0.940 0.882 bicubic
392 res2next50 93.110 93.140 6.890 6.860 98.660 98.650 1.340 1.350 24.67 224 0.875 bilinear
393 bat_resnext26ts 93.100 6.900 98.720 98.730 1.280 1.270 10.73 256 0.900 bicubic
394 tf_efficientnetv2_b0 93.060 6.940 98.700 1.300 7.14 224 0.875 bicubic
395 tf_mixnet_l levit_128 93.050 93.040 6.950 6.960 98.540 98.680 1.460 1.320 7.33 9.21 224 0.875 0.900 bicubic
396 levit_128 tf_mixnet_l 93.040 6.960 98.690 98.540 1.310 1.460 9.21 7.33 224 0.900 0.875 bicubic
397 repvgg_b1g4 93.040 93.030 6.960 6.970 98.820 1.180 39.97 224 0.875 bilinear
398 regnety_016 res2net50_14w_8s 93.030 6.970 98.690 98.700 1.310 1.300 11.20 25.06 224 0.875 bicubic bilinear
399 efficientnet_b1 93.020 93.030 6.980 6.970 98.710 1.290 7.79 256 1.000 bicubic
400 res2net50_14w_8s selecsls60 93.020 93.010 6.980 6.990 98.700 98.830 1.300 1.170 25.06 30.67 224 0.875 bilinear bicubic
401 selecsls60 adv_inception_v3 93.000 93.010 7.000 6.990 98.830 98.490 1.170 1.510 30.67 23.83 224 299 0.875 bicubic
402 adv_inception_v3 regnety_016 92.990 93.000 7.010 7.000 98.480 98.680 1.520 1.320 23.83 11.20 299 224 0.875 bicubic
403 hardcorenas_f 92.980 7.020 98.620 1.380 8.20 224 0.875 bilinear
404 hardcorenas_e efficientnet_b1_pruned 92.960 92.970 7.040 7.030 98.570 98.520 1.430 1.480 8.07 6.33 224 240 0.875 0.882 bilinear bicubic
405 efficientnet_b1_pruned hardcorenas_e 92.960 92.950 7.040 7.050 98.520 98.570 1.480 1.430 6.33 8.07 240 224 0.882 0.875 bicubic bilinear
406 hrnet_w32 92.950 7.050 98.840 1.160 41.23 224 0.875 bilinear
efficientnet_es 92.940 7.060 98.690 1.310 5.44 224 0.875 bicubic
407 gluon_resnet50_v1c 92.920 7.080 98.710 1.290 25.58 224 0.875 bicubic
408 tv_resnext50_32x4d efficientnet_es 92.910 92.920 7.090 7.080 98.730 98.690 1.270 1.310 25.03 5.44 224 0.875 bilinear bicubic
409 pit_xs_224 92.900 92.910 7.100 7.090 98.790 98.770 1.210 1.230 10.62 224 0.900 bicubic
410 inception_v3 tv_resnext50_32x4d 92.900 92.910 7.100 7.090 98.320 98.720 1.680 1.280 23.83 25.03 299 224 0.875 bicubic bilinear
411 inception_v3 92.900 7.100 98.330 1.670 23.83 299 0.875 bicubic
412 densenet161 92.890 7.110 98.810 1.190 28.68 224 0.875 bicubic
413 tv_resnet101 92.880 7.120 98.660 1.340 44.55 224 0.875 bilinear
414 densenet161 resmlp_12_distilled_224 92.880 92.870 7.120 7.130 98.810 98.620 1.190 1.380 28.68 15.35 224 0.875 bicubic
415 resmlp_12_distilled_224 tf_efficientnet_cc_b0_8e 92.870 7.130 98.630 98.460 1.370 1.540 15.35 24.01 224 0.875 bicubic
416 tf_efficientnet_cc_b0_8e coat_lite_tiny 92.850 92.860 7.150 7.140 98.460 98.630 1.540 1.370 24.01 5.72 224 0.875 0.900 bicubic
417 rexnet_100 92.840 7.160 98.620 1.380 4.80 224 0.875 bicubic
418 tf_efficientnet_cc_b0_4e 92.840 7.160 98.440 1.560 13.31 224 0.875 bicubic
419 coat_lite_tiny seresnext26t_32x4d 92.830 92.820 7.170 7.180 98.640 98.560 1.360 1.440 5.72 16.81 224 0.900 0.875 bicubic
420 res2net50_48w_2s seresnext26ts 92.800 92.810 7.200 7.190 98.470 98.600 1.530 1.400 25.29 10.39 224 256 0.875 0.900 bilinear bicubic
421 seresnext26ts res2net50_48w_2s 92.790 7.210 98.600 98.480 1.400 1.520 10.39 25.29 256 224 0.900 0.875 bicubic bilinear
422 seresnext26t_32x4d tinynet_a 92.770 92.790 7.230 7.210 98.550 98.560 1.450 1.440 16.81 6.19 224 192 0.875 bicubic
hrnet_w18 92.750 7.250 98.650 1.350 21.30 224 0.875 bilinear
423 crossvit_9_dagger_240 92.750 7.250 98.510 1.490 8.78 240 0.875 bicubic
424 dla60 hrnet_w18 92.690 92.750 7.310 7.250 98.630 98.660 1.370 1.340 22.04 21.30 224 0.875 bilinear
425 densenet201 92.690 7.310 98.660 98.650 1.340 1.350 20.01 224 0.875 bicubic
426 resnet26t gmixer_24_224 92.680 7.320 98.600 98.280 1.400 1.720 16.01 24.72 256 224 0.940 0.875 bicubic
427 gmixer_24_224 repvgg_a2 92.670 92.680 7.330 7.320 98.260 98.520 1.740 1.480 24.72 28.21 224 0.875 bicubic bilinear
428 legacy_seresnet50 dla60 92.670 7.330 98.660 98.630 1.340 1.370 28.09 22.04 224 0.875 bilinear
429 repvgg_a2 legacy_seresnet50 92.660 92.670 7.340 7.330 98.530 98.650 1.470 1.350 28.21 28.09 224 0.875 bilinear
430 resnet34d resnet26t 92.640 92.670 7.360 7.330 98.440 98.580 1.560 1.420 21.82 16.01 224 256 0.875 0.940 bicubic
431 mobilenetv2_120d resnet34d 92.610 92.640 7.390 7.360 98.510 98.420 1.490 1.580 5.83 21.82 224 0.875 bicubic
432 tf_efficientnet_b0_ap mobilenetv2_120d 92.600 92.610 7.400 7.390 98.370 98.500 1.630 1.500 5.29 5.83 224 0.875 bicubic
433 tf_efficientnet_b0_ap 92.610 7.390 98.370 1.630 5.29 224 0.875 bicubic
434 hardcorenas_d 92.600 7.400 98.430 1.570 7.50 224 0.875 bilinear
435 legacy_seresnext26_32x4d 92.590 92.580 7.410 7.420 98.420 98.410 1.580 1.590 16.79 224 0.875 bicubic
436 tf_efficientnet_lite2 92.570 92.580 7.430 7.420 98.550 1.450 6.09 260 0.890 bicubic
437 regnetx_016 skresnet34 92.560 92.570 7.440 7.430 98.550 98.520 1.450 1.480 9.19 22.28 224 0.875 bicubic
438 skresnet34 gluon_resnet50_v1b 92.560 7.440 98.510 98.550 1.490 1.450 22.28 25.56 224 0.875 bicubic
439 gluon_resnet50_v1b regnetx_016 92.550 92.540 7.450 7.460 98.550 1.450 25.56 9.19 224 0.875 bicubic
440 efficientnet_b0 92.480 7.520 98.680 1.320 5.29 224 0.875 bicubic
441 selecsls42b 92.480 7.520 98.440 1.560 32.46 224 0.875 bicubic
442 gcresnext26ts 92.470 7.530 98.500 98.490 1.500 1.510 10.48 256 0.900 bicubic
efficientnet_b0 92.470 7.530 98.680 1.320 5.29 224 0.875 bicubic
443 xcit_tiny_12_p16_224 92.460 7.540 98.630 1.370 6.72 224 1.000 bicubic
444 gernet_s 92.440 7.560 98.490 98.500 1.510 1.500 8.17 224 0.875 bilinear
445 seresnext26d_32x4d 92.430 7.570 98.540 1.460 16.81 224 0.875 bicubic
446 eca_resnext26ts xcit_nano_12_p8_224_dist 92.420 7.580 98.610 98.530 1.390 1.470 10.30 3.05 256 224 0.900 1.000 bicubic
447 xcit_nano_12_p8_224_dist eca_resnext26ts 92.410 7.590 98.510 98.620 1.490 1.380 3.05 10.30 224 256 1.000 0.900 bicubic
448 densenetblur121d 92.400 7.600 98.410 1.590 8.00 224 0.875 bicubic
449 tf_efficientnet_b0 92.400 7.600 98.470 1.530 5.29 224 0.875 bicubic
densenetblur121d 92.400 7.600 98.420 1.580 8.00 224 0.875 bicubic
450 convmixer_1024_20_ks9_p14 92.350 7.650 98.420 1.580 24.38 224 0.960 bicubic
451 hardcorenas_c 92.330 7.670 98.340 98.350 1.660 1.650 5.52 224 0.875 bilinear
452 tf_efficientnet_lite1 92.320 92.310 7.680 7.690 98.490 1.510 5.42 240 0.882 bicubic
453 densenet169 92.290 7.710 98.590 1.410 14.15 224 0.875 bicubic
454 mixnet_m 92.260 92.270 7.740 7.730 98.370 98.350 1.630 1.650 5.01 224 0.875 bicubic
455 dpn68 92.260 7.740 98.600 98.610 1.400 1.390 12.61 224 0.875 bicubic
resnet26d 92.250 7.750 98.470 1.530 16.01 224 0.875 bicubic
456 mobilenetv3_large_100_miil 92.250 7.750 98.250 1.750 5.48 224 0.875 bilinear
457 resnext26ts resnet26d 92.210 92.250 7.790 7.750 98.280 98.450 1.720 1.550 10.30 16.01 256 224 0.900 0.875 bicubic
458 tf_mixnet_m resnext26ts 92.170 92.220 7.830 7.780 98.420 98.250 1.580 1.750 5.01 10.30 224 256 0.875 0.900 bicubic
459 vit_small_patch32_224 tf_mixnet_m 92.150 92.200 7.850 7.800 98.510 98.420 1.490 1.580 22.88 5.01 224 0.900 0.875 bicubic
460 resmlp_12_224 vit_small_patch32_224 92.130 92.160 7.870 7.840 98.570 98.510 1.430 1.490 15.35 22.88 224 0.875 0.900 bicubic
461 tv_resnet50 resmlp_12_224 92.110 92.120 7.890 7.880 98.420 98.570 1.580 1.430 25.56 15.35 224 0.875 bilinear bicubic
462 xcit_nano_12_p16_384_dist tv_resnet50 92.100 92.120 7.900 7.880 98.520 98.420 1.480 1.580 3.05 25.56 384 224 1.000 0.875 bicubic bilinear
463 xcit_nano_12_p16_384_dist 92.110 7.890 98.520 1.480 3.05 384 1.000 bicubic
464 tf_efficientnet_es 92.100 7.900 98.430 1.570 5.44 224 0.875 bicubic
465 mobilenetv2_140 92.030 92.050 7.970 7.950 98.250 1.750 6.11 224 0.875 bicubic
466 ese_vovnet19b_dw 92.020 92.010 7.980 7.990 98.520 98.510 1.480 1.490 6.54 224 0.875 bicubic
hardcorenas_b 91.970 8.030 98.400 1.600 5.18 224 0.875 bilinear
467 densenet121 91.940 8.060 98.280 1.720 7.98 224 0.875 bicubic
468 vit_tiny_patch16_224 hardcorenas_b 91.930 8.070 98.330 98.400 1.670 1.600 5.72 5.18 224 0.900 0.875 bicubic bilinear
469 regnety_008 91.890 91.910 8.110 8.090 98.420 1.580 6.26 224 0.875 bicubic
470 vit_tiny_patch16_224 91.910 8.090 98.340 1.660 5.72 224 0.900 bicubic
471 mixnet_s 91.780 8.220 98.300 1.700 4.13 224 0.875 bicubic
472 vit_tiny_r_s16_p8_384 91.720 91.730 8.280 8.270 98.430 1.570 6.36 384 1.000 bicubic
473 efficientnet_es_pruned 91.710 8.290 98.400 98.410 1.600 1.590 5.44 224 0.875 bicubic
474 tf_mixnet_s repvgg_b0 91.690 91.680 8.310 8.320 98.240 98.450 1.760 1.550 4.13 15.82 224 0.875 bicubic bilinear
475 repvgg_b0 tf_mixnet_s 91.670 91.680 8.330 8.320 98.450 98.240 1.550 1.760 15.82 4.13 224 0.875 bilinear bicubic
476 semnasnet_100 91.660 8.340 98.260 98.270 1.740 1.730 3.89 224 0.875 bicubic
477 hardcorenas_a 91.610 91.620 8.390 8.380 98.170 1.830 5.26 224 0.875 bilinear
478 regnety_006 91.580 91.560 8.420 8.440 98.430 1.570 6.06 224 0.875 bicubic
479 mobilenetv3_rw 91.550 91.540 8.450 8.460 98.270 1.730 5.48 224 0.875 bicubic
480 levit_128s 91.500 8.500 98.400 1.600 7.78 224 0.900 bicubic
481 legacy_seresnet34 91.480 8.520 98.200 1.800 21.96 224 0.875 bilinear
482 mobilenetv3_large_100 91.480 8.520 98.330 98.320 1.670 1.680 5.48 224 0.875 bicubic
483 resnet26 91.460 91.450 8.540 8.550 98.270 1.730 16.00 224 0.875 bicubic
484 tf_mobilenetv3_large_100 91.410 91.420 8.590 8.580 98.250 98.260 1.750 1.740 5.48 224 0.875 bilinear
485 tv_densenet121 91.400 91.410 8.600 8.590 98.250 1.750 7.98 224 0.875 bicubic
486 mobilenetv2_110d 91.320 91.350 8.680 8.650 98.180 98.190 1.820 1.810 4.52 224 0.875 bicubic
487 tf_efficientnet_lite0 91.280 91.300 8.720 8.700 98.090 1.910 4.65 224 0.875 bicubic
488 fbnetc_100 91.260 8.740 97.820 97.830 2.180 2.170 5.57 224 0.875 bilinear
dla34 91.250 8.750 98.180 1.820 15.74 224 0.875 bilinear
489 efficientnet_lite0 91.250 8.750 98.250 1.750 4.65 224 0.875 bicubic
490 mnasnet_100 dla34 91.200 91.230 8.800 8.770 98.040 98.180 1.960 1.820 4.38 15.74 224 0.875 bicubic bilinear
491 resnet34 mnasnet_100 91.190 91.200 8.810 8.800 98.230 98.050 1.770 1.950 21.80 4.38 224 0.875 bilinear bicubic
492 regnetx_008 resnet34 91.190 91.200 8.810 8.800 98.370 98.240 1.630 1.760 7.26 21.80 224 0.875 bicubic bilinear
493 hrnet_w18_small_v2 91.170 8.830 98.350 98.340 1.650 1.660 15.60 224 0.875 bilinear
494 resnest14d regnetx_008 91.150 91.160 8.850 8.840 98.350 98.380 1.650 1.620 10.61 7.26 224 0.875 bilinear bicubic
495 mixer_b16_224 91.130 91.140 8.870 8.860 97.410 97.400 2.590 2.600 59.88 224 0.875 bicubic
496 xcit_nano_12_p8_224 tinynet_b 91.100 91.130 8.900 8.870 98.240 98.070 1.760 1.930 3.05 3.73 224 188 1.000 0.875 bicubic
497 swsl_resnet18 resnest14d 91.090 91.120 8.910 8.880 98.210 98.330 1.790 1.670 11.69 10.61 224 0.875 bilinear
498 gluon_resnet34_v1b xcit_nano_12_p8_224 91.090 91.120 8.910 8.880 98.180 98.240 1.820 1.760 21.80 3.05 224 0.875 1.000 bicubic
499 deit_tiny_distilled_patch16_224 91.090 91.110 8.910 8.890 98.270 1.730 5.91 224 0.900 bicubic
500 crossvit_9_240 gluon_resnet34_v1b 91.070 91.100 8.930 8.900 98.310 98.180 1.690 1.820 8.55 21.80 240 224 0.875 bicubic
501 vgg19_bn swsl_resnet18 90.990 91.080 9.010 8.920 98.120 98.210 1.880 1.790 143.68 11.69 224 0.875 bilinear
502 pit_ti_distilled_224 crossvit_9_240 90.900 91.050 9.100 8.950 98.230 98.310 1.770 1.690 5.10 8.55 224 240 0.900 0.875 bicubic
503 regnetx_006 vgg19_bn 90.750 90.990 9.250 9.010 98.100 98.110 1.900 1.890 6.20 143.68 224 0.875 bicubic bilinear
504 regnety_004 pit_ti_distilled_224 90.750 90.900 9.250 9.100 98.080 98.220 1.920 1.780 4.34 5.10 224 0.875 0.900 bicubic
505 regnetx_006 90.770 9.230 98.100 1.900 6.20 224 0.875 bicubic
506 regnety_004 90.770 9.230 98.080 1.920 4.34 224 0.875 bicubic
507 ssl_resnet18 90.700 9.300 98.030 1.970 11.69 224 0.875 bilinear
508 spnasnet_100 90.610 90.600 9.390 9.400 97.950 2.050 4.42 224 0.875 bilinear
509 convit_tiny 90.550 9.450 98.210 98.220 1.790 1.780 5.71 224 0.875 bicubic
510 crossvit_tiny_240 90.530 90.540 9.470 9.460 97.950 2.050 7.01 240 0.875 bicubic
511 vgg16_bn 90.520 90.540 9.480 9.460 97.990 2.010 138.37 224 0.875 bilinear
512 ghostnet_100 90.440 9.560 97.840 97.830 2.160 2.170 5.18 224 0.875 bilinear
513 pit_ti_224 90.440 9.560 98.020 98.010 1.980 1.990 4.85 224 0.900 bicubic
514 tf_mobilenetv3_large_075 90.310 90.320 9.690 9.680 97.880 97.870 2.120 2.130 3.99 224 0.875 bilinear
515 tv_resnet34 90.310 90.290 9.690 9.710 97.970 97.980 2.030 2.020 21.80 224 0.875 bilinear
516 xcit_nano_12_p16_224_dist semnasnet_075 90.190 90.200 9.810 9.800 97.760 97.970 2.240 2.030 3.05 2.91 224 1.000 0.875 bicubic
517 skresnet18 90.170 9.830 97.780 2.220 11.96 224 0.875 bicubic
518 resnet18d xcit_nano_12_p16_224_dist 89.990 90.170 10.010 9.830 97.840 97.760 2.160 2.240 11.71 3.05 224 0.875 1.000 bicubic
519 hrnet_w18_small resnet18d 89.900 89.990 10.100 10.010 97.900 97.830 2.100 2.170 13.19 11.71 224 0.875 bilinear bicubic
520 vit_base_patch32_sam_224 hrnet_w18_small 89.870 89.880 10.130 10.120 97.600 97.900 2.400 2.100 88.22 13.19 224 0.900 0.875 bicubic bilinear
521 mobilenetv2_100 vit_base_patch32_224_sam 89.840 89.860 10.160 10.140 97.840 97.600 2.160 2.400 3.50 88.22 224 0.875 0.900 bicubic
522 vgg19 mobilenetv2_100 89.690 89.830 10.310 10.170 97.550 97.830 2.450 2.170 143.67 3.50 224 0.875 bilinear bicubic
523 deit_tiny_patch16_224 vgg19 89.600 89.680 10.400 10.320 97.960 97.550 2.040 2.450 5.72 143.67 224 0.900 0.875 bicubic bilinear
524 regnetx_004 deit_tiny_patch16_224 89.450 89.620 10.550 10.380 97.780 97.960 2.220 2.040 5.16 5.72 224 0.875 0.900 bicubic
525 vit_tiny_r_s16_p8_224 regnetx_004 89.400 89.470 10.600 10.530 97.690 97.770 2.310 2.230 6.34 5.16 224 0.900 0.875 bicubic
526 vgg16 89.380 89.360 10.620 10.640 97.520 2.480 138.36 224 0.875 bilinear
527 legacy_seresnet18 vit_tiny_r_s16_p8_224 89.250 89.350 10.750 10.650 97.690 97.700 2.310 2.300 11.78 6.34 224 0.875 0.900 bicubic
528 vgg13_bn legacy_seresnet18 89.210 89.260 10.790 10.740 97.540 97.680 2.460 2.320 133.05 11.78 224 0.875 bilinear bicubic
529 tf_mobilenetv3_large_minimal_100 vgg13_bn 89.160 89.200 10.840 10.800 97.320 97.520 2.680 2.480 3.92 133.05 224 0.875 bilinear
530 xcit_nano_12_p16_224 tf_mobilenetv3_large_minimal_100 88.960 89.170 11.040 10.830 97.410 97.320 2.590 2.680 3.05 3.92 224 1.000 0.875 bicubic bilinear
531 gluon_resnet18_v1b xcit_nano_12_p16_224 88.670 88.970 11.330 11.030 97.110 97.390 2.890 2.610 11.69 3.05 224 0.875 1.000 bicubic
532 vgg11_bn lcnet_100 88.390 88.950 11.610 11.050 97.280 97.380 2.720 2.620 132.87 2.95 224 0.875 bilinear bicubic
533 regnety_002 gluon_resnet18_v1b 88.210 88.660 11.790 11.340 97.420 97.100 2.580 2.900 3.16 11.69 224 0.875 bicubic
534 resnet18 tinynet_c 88.160 88.420 11.840 11.580 97.120 97.260 2.880 2.740 11.69 2.46 224 184 0.875 bilinear bicubic
535 vgg13 vgg11_bn 87.550 88.390 12.450 11.610 97.120 97.270 2.880 2.730 133.05 132.87 224 0.875 bilinear
536 regnetx_002 regnety_002 87.360 88.190 12.640 11.810 96.990 97.420 3.010 2.580 2.68 3.16 224 0.875 bicubic
537 vgg11 resnet18 87.330 88.150 12.670 11.850 97.110 97.120 2.890 2.880 132.86 11.69 224 0.875 bilinear
538 dla60x_c vgg13 87.080 87.570 12.920 12.430 97.140 97.120 2.860 2.880 1.32 133.05 224 0.875 bilinear
539 mixer_l16_224 regnetx_002 86.960 87.380 13.040 12.620 94.030 96.990 5.970 3.010 208.20 2.68 224 0.875 bicubic
540 tf_mobilenetv3_small_100 vgg11 85.990 87.340 14.010 12.660 96.410 97.110 3.590 2.890 2.54 132.86 224 0.875 bilinear
541 dla46x_c dla60x_c 85.470 87.110 14.530 12.890 96.450 97.140 3.550 2.860 1.07 1.32 224 0.875 bilinear
542 dla46_c mixer_l16_224 84.710 86.970 15.290 13.030 96.210 94.040 3.790 5.960 1.30 208.20 224 0.875 bilinear bicubic
543 lcnet_075 86.940 13.060 96.530 3.470 2.36 224 0.875 bicubic
544 tf_mobilenetv3_small_100 85.970 14.030 96.410 3.590 2.54 224 0.875 bilinear
545 dla46x_c 85.480 14.520 96.440 3.560 1.07 224 0.875 bilinear
546 tinynet_d 85.430 14.570 96.020 3.980 2.34 152 0.875 bicubic
547 mobilenetv2_050 84.990 15.010 95.620 4.380 1.97 224 0.875 bicubic
548 mnasnet_small 84.920 15.080 95.930 4.070 2.03 224 0.875 bicubic
549 dla46_c 84.670 15.330 96.200 3.800 1.30 224 0.875 bilinear
550 tf_mobilenetv3_small_075 84.530 15.470 95.890 4.110 2.04 224 0.875 bilinear
551 lcnet_050 83.000 17.000 95.020 4.980 1.88 224 0.875 bicubic
552 tf_mobilenetv3_small_minimal_100 82.680 17.320 95.010 4.990 2.04 224 0.875 bilinear
553 tinynet_e 79.800 20.200 93.980 6.020 2.04 106 0.875 bicubic

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@ -28,14 +28,16 @@ NON_STD_FILTERS = [
NUM_NON_STD = len(NON_STD_FILTERS)
# exclude models that cause specific test failures
if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
if 'GITHUB_ACTIONS' in os.environ:
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FILTERS = [
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*']
'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*']
NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*']
else:
EXCLUDE_FILTERS = []
NON_STD_EXCLUDE_FILTERS = ['vit_gi*']
TARGET_FWD_SIZE = MAX_FWD_SIZE = 384
TARGET_BWD_SIZE = 128
@ -168,11 +170,12 @@ def test_model_default_cfgs(model_name, batch_size):
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
# check classifier name matches default_cfg
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
if cfg.get('num_classes', None):
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
@ -184,7 +187,7 @@ def test_model_default_cfgs(model_name, batch_size):
@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS))
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs_non_std(model_name, batch_size):
"""Run a single forward pass with each model"""
@ -220,11 +223,12 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
assert outputs.shape[1] == model.num_features
# check classifier name matches default_cfg
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
if cfg.get('num_classes', None):
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
@ -255,7 +259,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
EXCLUDE_JIT_FILTERS = [
'*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable
'dla*', 'hrnet*', 'ghostnet*', # hopefully fix at some point
'vit_large_*', 'vit_huge_*',
'vit_large_*', 'vit_huge_*', 'vit_gi*',
]
@ -334,7 +338,7 @@ def _create_fx_model(model, train=False):
return fx_model
EXCLUDE_FX_FILTERS = []
EXCLUDE_FX_FILTERS = ['vit_gi*']
# not enough memory to run fx on more models than other tests
if 'GITHUB_ACTIONS' in os.environ:
EXCLUDE_FX_FILTERS += [

@ -15,7 +15,7 @@ Papers:
RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math

@ -1,6 +1,6 @@
""" Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import torch.utils.data as data
import os

@ -1,3 +1,7 @@
""" Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, QMNIST, KMNIST, FashionMNIST, ImageNet, ImageFolder

@ -108,11 +108,11 @@ class RepeatAugSampler(Sampler):
indices = torch.arange(start=0, end=len(self.dataset))
# produce repeats e.g. [0, 0, 0, 1, 1, 1, 2, 2, 2....]
indices = torch.repeat_interleave(indices, repeats=self.num_repeats, dim=0)
indices = torch.repeat_interleave(indices, repeats=self.num_repeats, dim=0).tolist()
# add extra samples to make it evenly divisible
padding_size = self.total_size - len(indices)
if padding_size > 0:
indices = torch.cat([indices, indices[:padding_size]], dim=0)
indices += indices[:padding_size]
assert len(indices) == self.total_size
# subsample per rank

@ -3,7 +3,7 @@
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2021 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
from functools import partial

@ -8,7 +8,7 @@ CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Fea
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import numpy as np
import torch

@ -3,7 +3,7 @@
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math

@ -1,7 +1,7 @@
""" Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import math

@ -5,6 +5,7 @@ from .cait import *
from .coat import *
from .convit import *
from .convmixer import *
from .convnext import *
from .crossvit import *
from .cspnet import *
from .densenet import *

@ -4,6 +4,7 @@ Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
Original code and weights from https://github.com/facebookresearch/deit, copyright below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.

@ -9,6 +9,8 @@
Paper link: https://arxiv.org/abs/2103.10697
Original code: https://github.com/facebookresearch/convit, original copyright below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.

@ -0,0 +1,427 @@
""" ConvNeXt
Paper: `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
Original code and weights from https://github.com/facebookresearch/ConvNeXt, original copyright below
Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
"""
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the MIT license
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .fx_features import register_notrace_module
from .helpers import named_apply, build_model_with_cfg
from .layers import trunc_normal_, ClassifierHead, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp
from .registry import register_model
__all__ = ['ConvNeXt'] # model_registry will add each entrypoint fn to this
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = dict(
convnext_tiny=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth"),
convnext_small=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth"),
convnext_base=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"),
convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
convnext_tiny_hnf=_cfg(url=''),
convnext_base_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth'),
convnext_large_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth'),
convnext_xlarge_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth'),
convnext_base_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_large_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_xlarge_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_base_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841),
convnext_large_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841),
convnext_xlarge_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841),
)
def _is_contiguous(tensor: torch.Tensor) -> bool:
# jit is oh so lovely :/
# if torch.jit.is_tracing():
# return True
if torch.jit.is_scripting():
return tensor.is_contiguous()
else:
return tensor.is_contiguous(memory_format=torch.contiguous_format)
@register_notrace_module
class LayerNorm2d(nn.LayerNorm):
r""" LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
"""
def __init__(self, normalized_shape, eps=1e-6):
super().__init__(normalized_shape, eps=eps)
def forward(self, x) -> torch.Tensor:
if _is_contiguous(x):
return F.layer_norm(
x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
else:
s, u = torch.var_mean(x, dim=1, unbiased=False, keepdim=True)
x = (x - u) * torch.rsqrt(s + self.eps)
x = x * self.weight[:, None, None] + self.bias[:, None, None]
return x
class ConvNeXtBlock(nn.Module):
""" ConvNeXt Block
There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
Unlike the official impl, this one allows choice of 1 or 2, 1x1 conv can be faster with appropriate
choice of LayerNorm impl, however as model size increases the tradeoffs appear to change and nn.Linear
is a better choice. This was observed with PyTorch 1.10 on 3090 GPU, it could change over time & w/ different HW.
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(self, dim, drop_path=0., ls_init_value=1e-6, conv_mlp=False, mlp_ratio=4, norm_layer=None):
super().__init__()
if not norm_layer:
norm_layer = partial(LayerNorm2d, eps=1e-6) if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
mlp_layer = ConvMlp if conv_mlp else Mlp
self.use_conv_mlp = conv_mlp
self.conv_dw = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.norm = norm_layer(dim)
self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=nn.GELU)
self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.conv_dw(x)
if self.use_conv_mlp:
x = self.norm(x)
x = self.mlp(x)
else:
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.mlp(x)
x = x.permute(0, 3, 1, 2)
if self.gamma is not None:
x = x.mul(self.gamma.reshape(1, -1, 1, 1))
x = self.drop_path(x) + shortcut
return x
class ConvNeXtStage(nn.Module):
def __init__(
self, in_chs, out_chs, stride=2, depth=2, dp_rates=None, ls_init_value=1.0, conv_mlp=False,
norm_layer=None, cl_norm_layer=None, cross_stage=False):
super().__init__()
if in_chs != out_chs or stride > 1:
self.downsample = nn.Sequential(
norm_layer(in_chs),
nn.Conv2d(in_chs, out_chs, kernel_size=stride, stride=stride),
)
else:
self.downsample = nn.Identity()
dp_rates = dp_rates or [0.] * depth
self.blocks = nn.Sequential(*[ConvNeXtBlock(
dim=out_chs, drop_path=dp_rates[j], ls_init_value=ls_init_value, conv_mlp=conv_mlp,
norm_layer=norm_layer if conv_mlp else cl_norm_layer)
for j in range(depth)]
)
def forward(self, x):
x = self.downsample(x)
x = self.blocks(x)
return x
class ConvNeXt(nn.Module):
r""" ConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (tuple(int)): Feature dimension at each stage. Default: [96, 192, 384, 768]
drop_rate (float): Head dropout rate
drop_path_rate (float): Stochastic depth rate. Default: 0.
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
"""
def __init__(
self, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32, patch_size=4,
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), ls_init_value=1e-6, conv_mlp=False,
head_init_scale=1., head_norm_first=False, norm_layer=None, drop_rate=0., drop_path_rate=0.,
):
super().__init__()
assert output_stride == 32
if norm_layer is None:
norm_layer = partial(LayerNorm2d, eps=1e-6)
cl_norm_layer = norm_layer if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
else:
assert conv_mlp,\
'If a norm_layer is specified, conv MLP must be used so all norm expect rank-4, channels-first input'
cl_norm_layer = norm_layer
self.num_classes = num_classes
self.drop_rate = drop_rate
self.feature_info = []
# NOTE: this stem is a minimal form of ViT PatchEmbed, as used in SwinTransformer w/ patch_size = 4
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=patch_size, stride=patch_size),
norm_layer(dims[0])
)
self.stages = nn.Sequential()
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
curr_stride = patch_size
prev_chs = dims[0]
stages = []
# 4 feature resolution stages, each consisting of multiple residual blocks
for i in range(4):
stride = 2 if i > 0 else 1
# FIXME support dilation / output_stride
curr_stride *= stride
out_chs = dims[i]
stages.append(ConvNeXtStage(
prev_chs, out_chs, stride=stride,
depth=depths[i], dp_rates=dp_rates[i], ls_init_value=ls_init_value, conv_mlp=conv_mlp,
norm_layer=norm_layer, cl_norm_layer=cl_norm_layer)
)
prev_chs = out_chs
# NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.num_features = prev_chs
if head_norm_first:
# norm -> global pool -> fc ordering, like most other nets (not compat with FB weights)
self.norm_pre = norm_layer(self.num_features) # final norm layer, before pooling
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
else:
# pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
self.norm_pre = nn.Identity()
self.head = nn.Sequential(OrderedDict([
('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
('norm', norm_layer(self.num_features)),
('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
('drop', nn.Dropout(self.drop_rate)),
('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())
]))
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes=0, global_pool='avg'):
if isinstance(self.head, ClassifierHead):
# norm -> global pool -> fc
self.head = ClassifierHead(
self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
else:
# pool -> norm -> fc
self.head = nn.Sequential(OrderedDict([
('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
('norm', self.head.norm),
('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
('drop', nn.Dropout(self.drop_rate)),
('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())
]))
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm_pre(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
nn.init.constant_(module.bias, 0)
if name and 'head.' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def checkpoint_filter_fn(state_dict, model):
""" Remap FB checkpoints -> timm """
if 'model' in state_dict:
state_dict = state_dict['model']
out_dict = {}
import re
for k, v in state_dict.items():
k = k.replace('downsample_layers.0.', 'stem.')
k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k)
k = k.replace('dwconv', 'conv_dw')
k = k.replace('pwconv', 'mlp.fc')
k = k.replace('head.', 'head.fc.')
if k.startswith('norm.'):
k = k.replace('norm', 'head.norm')
if v.ndim == 2 and 'head' not in k:
model_shape = model.state_dict()[k].shape
v = v.reshape(model_shape)
out_dict[k] = v
return out_dict
def _create_convnext(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
ConvNeXt, variant, pretrained,
default_cfg=default_cfgs[variant],
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs)
return model
@register_model
def convnext_tiny(pretrained=False, **kwargs):
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
model = _create_convnext('convnext_tiny', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_hnf(pretrained=False, **kwargs):
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, **kwargs)
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_small(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
model = _create_convnext('convnext_small', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_in22k', pretrained=pretrained, **model_args)
return model

@ -12,6 +12,8 @@ Paper link: https://arxiv.org/abs/2103.14899
Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
NOTE: model names have been renamed from originals to represent actual input res all *_224 -> *_240 and *_384 -> *_408
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright IBM All Rights Reserved.

@ -411,9 +411,12 @@ class CspNet(nn.Module):
def _create_cspnet(variant, pretrained=False, **kwargs):
cfg_variant = variant.split('_')[0]
# NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4))
return build_model_with_cfg(
CspNet, variant, pretrained,
feature_cfg=dict(flatten_sequential=True), model_cfg=model_cfgs[cfg_variant],
model_cfg=model_cfgs[cfg_variant],
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)

@ -33,7 +33,7 @@ The majority of the above models (EfficientNet*, MixNet, MnasNet) and original w
by Mingxing Tan, Quoc Le, and other members of their Google Brain team. Thanks for consistently releasing
the models and weights open source!
Hacked together by / Copyright 2021 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import List
@ -73,16 +73,20 @@ default_cfgs = {
'mnasnet_140': _cfg(url=''),
'semnasnet_050': _cfg(url=''),
'semnasnet_075': _cfg(url=''),
'semnasnet_075': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/semnasnet_075-18710866.pth'),
'semnasnet_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'),
'semnasnet_140': _cfg(url=''),
'mnasnet_small': _cfg(url=''),
'mnasnet_small': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_small_lamb-aff75073.pth'),
'mobilenetv2_035': _cfg(
url=''),
'mobilenetv2_050': _cfg(
url=''),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth',
interpolation='bicubic',
),
'mobilenetv2_075': _cfg(
url=''),
'mobilenetv2_100': _cfg(
@ -726,7 +730,7 @@ def _gen_mobilenet_v2(
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
num_features=1280 if fix_stem_head else round_chs_fn(1280),
num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)),
stem_size=32,
fix_stem=fix_stem_head,
round_chs_fn=round_chs_fn,
@ -1474,7 +1478,7 @@ def efficientnet_b0_g16_evos(pretrained=False, **kwargs):
""" EfficientNet-B0 w/ group 16 conv + EvoNorm"""
model = _gen_efficientnet(
'efficientnet_b0_g16_evos', group_size=16, channel_divisor=16,
pretrained=pretrained, **kwargs) #norm_layer=partial(EvoNorm2dS0, group_size=16),
pretrained=pretrained, **kwargs) #norm_layer=partial(EvoNorm2dS0, group_size=16),
return model

@ -1,6 +1,6 @@
""" EfficientNet, MobileNetV3, etc Blocks
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import math

@ -3,7 +3,7 @@
Assembles EfficieNet and related network feature blocks from string definitions.
Handles stride, dilation calculations, and selects feature extraction points.
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging

@ -281,8 +281,8 @@ def load_pretrained(
if num_classes != pretrained_cfg['num_classes']:
for classifier_name in classifiers:
# completely discard fully connected if model num_classes doesn't match pretrained weights
del state_dict[classifier_name + '.weight']
del state_dict[classifier_name + '.bias']
state_dict.pop(classifier_name + '.weight', None)
state_dict.pop(classifier_name + '.bias', None)
strict = False
elif label_offset > 0:
for classifier_name in classifiers:

@ -23,7 +23,7 @@ from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible
from .inplace_abn import InplaceAbn
from .linear import Linear
from .mixed_conv2d import MixedConv2d
from .mlp import Mlp, GluMlp, GatedMlp
from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .norm import GroupNorm, LayerNorm2d
from .norm_act import BatchNormAct2d, GroupNormAct

@ -157,7 +157,6 @@ def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: b
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None, scale_by_keep=True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob

@ -14,7 +14,7 @@ Adapted from official impl at https://github.com/facebookresearch/LeViT, origina
This version combines both conv/linear models and fixes torchscript compatibility.
Modifications by/coyright Copyright 2021 Ross Wightman
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright (c) 2015-present, Facebook, Inc.

@ -1,11 +1,10 @@
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2021 Ross Wightman
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import List
@ -48,9 +47,15 @@ default_cfgs = {
interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_in21k_miil.pth', num_classes=11221),
'mobilenetv3_small_050': _cfg(url=''),
'mobilenetv3_small_075': _cfg(url=''),
'mobilenetv3_small_100': _cfg(url=''),
'mobilenetv3_small_050': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth',
interpolation='bicubic'),
'mobilenetv3_small_075': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_075_lambc-384766db.pth',
interpolation='bicubic'),
'mobilenetv3_small_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_100_lamb-266a294c.pth',
interpolation='bicubic'),
'mobilenetv3_rw': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
@ -75,14 +80,29 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'fbnetv3_b': _cfg(),
'fbnetv3_d': _cfg(),
'fbnetv3_g': _cfg(),
'fbnetv3_b': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_b_224-ead5d2a1.pth',
test_input_size=(3, 256, 256), crop_pct=0.95),
'fbnetv3_d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_d_224-c98bce42.pth',
test_input_size=(3, 256, 256), crop_pct=0.95),
'fbnetv3_g': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_g_240-0b1df83b.pth',
input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95),
"lcnet_035": _cfg(),
"lcnet_050": _cfg(),
"lcnet_075": _cfg(),
"lcnet_100": _cfg(),
"lcnet_050": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth',
interpolation='bicubic',
),
"lcnet_075": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth',
interpolation='bicubic',
),
"lcnet_100": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth',
interpolation='bicubic',
),
"lcnet_150": _cfg(),
}
@ -102,9 +122,10 @@ class MobileNetV3(nn.Module):
* LCNet - https://arxiv.org/abs/2109.15099
"""
def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True,
round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'):
def __init__(
self, block_args, num_classes=1000, in_chans=3, stem_size=16, fix_stem=False, num_features=1280,
head_bias=True, pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True,
round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'):
super(MobileNetV3, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
@ -115,7 +136,8 @@ class MobileNetV3(nn.Module):
self.drop_rate = drop_rate
# Stem
stem_size = round_chs_fn(stem_size)
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_act_layer(stem_size, inplace=True)
@ -179,8 +201,8 @@ class MobileNetV3Features(nn.Module):
"""
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3,
stem_size=16, output_stride=32, pad_type='', round_chs_fn=round_channels, se_from_exp=True,
act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
stem_size=16, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels,
se_from_exp=True, act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
super(MobileNetV3Features, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
@ -188,7 +210,8 @@ class MobileNetV3Features(nn.Module):
self.drop_rate = drop_rate
# Stem
stem_size = round_chs_fn(stem_size)
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
@ -371,6 +394,7 @@ def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwarg
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=16,
fix_stem=channel_multiplier < 0.75,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
@ -480,6 +504,44 @@ def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
return model
def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
""" LCNet
Essentially a MobileNet-V3 crossed with a MobileNet-V1
Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arxiv.org/abs/2109.15099
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['dsa_r1_k3_s1_c32'],
# stage 1, 112x112 in
['dsa_r2_k3_s2_c64'],
# stage 2, 56x56 in
['dsa_r2_k3_s2_c128'],
# stage 3, 28x28 in
['dsa_r1_k3_s2_c256', 'dsa_r1_k5_s1_c256'],
# stage 4, 14x14in
['dsa_r4_k5_s1_c256'],
# stage 5, 14x14in
['dsa_r2_k5_s2_c512_se0.25'],
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU),
num_features=1280,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
@register_model
def mobilenetv3_large_075(pretrained=False, **kwargs):
""" MobileNet V3 """

@ -4,7 +4,8 @@ This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-C
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2020 Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
from functools import partial
@ -250,6 +251,21 @@ default_cfgs = {
'resnetblur50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth',
interpolation='bicubic'),
'resnetblur50d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnetblur101d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnetaa50d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'resnetaa101d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
'seresnetaa50d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0'),
# ResNet-RS models
'resnetrs50': _cfg(
@ -288,6 +304,12 @@ def get_padding(kernel_size, stride, dilation=1):
return padding
def create_aa(aa_layer, channels, stride=2, enable=True):
if not aa_layer or not enable:
return nn.Identity()
return aa_layer(stride) if issubclass(aa_layer, nn.AvgPool2d) else aa_layer(channels=channels, stride=stride)
class BasicBlock(nn.Module):
expansion = 1
@ -309,7 +331,7 @@ class BasicBlock(nn.Module):
self.bn1 = norm_layer(first_planes)
self.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act1 = act_layer(inplace=True)
self.aa = aa_layer(channels=first_planes, stride=stride) if use_aa else nn.Identity()
self.aa = create_aa(aa_layer, channels=first_planes, stride=stride, enable=use_aa)
self.conv2 = nn.Conv2d(
first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
@ -376,7 +398,7 @@ class Bottleneck(nn.Module):
self.bn2 = norm_layer(width)
self.drop_block = drop_block() if drop_block is not None else nn.Identity()
self.act2 = act_layer(inplace=True)
self.aa = aa_layer(channels=width, stride=stride) if use_aa else nn.Identity()
self.aa = create_aa(aa_layer, channels=width, stride=stride, enable=use_aa)
self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
@ -606,19 +628,22 @@ class ResNet(nn.Module):
self.act1 = act_layer(inplace=True)
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
# Stem Pooling
# Stem pooling. The name 'maxpool' remains for weight compatibility.
if replace_stem_pool:
self.maxpool = nn.Sequential(*filter(None, [
nn.Conv2d(inplanes, inplanes, 3, stride=1 if aa_layer else 2, padding=1, bias=False),
aa_layer(channels=inplanes, stride=2) if aa_layer else None,
create_aa(aa_layer, channels=inplanes, stride=2),
norm_layer(inplanes),
act_layer(inplace=True)
]))
else:
if aa_layer is not None:
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=inplanes, stride=2)])
if issubclass(aa_layer, nn.AvgPool2d):
self.maxpool = aa_layer(2)
else:
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=inplanes, stride=2)])
else:
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@ -1328,6 +1353,56 @@ def resnetblur50(pretrained=False, **kwargs):
return _create_resnet('resnetblur50', pretrained, **model_args)
@register_model
def resnetblur50d(pretrained=False, **kwargs):
"""Constructs a ResNet-50-D model with blur anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnetblur50d', pretrained, **model_args)
@register_model
def resnetblur101d(pretrained=False, **kwargs):
"""Constructs a ResNet-101-D model with blur anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=BlurPool2d,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnetblur101d', pretrained, **model_args)
@register_model
def resnetaa50d(pretrained=False, **kwargs):
"""Constructs a ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnetaa50d', pretrained, **model_args)
@register_model
def resnetaa101d(pretrained=False, **kwargs):
"""Constructs a ResNet-101-D model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=nn.AvgPool2d,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnetaa101d', pretrained, **model_args)
@register_model
def seresnetaa50d(pretrained=False, **kwargs):
"""Constructs a SE=ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d,
stem_width=32, stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('seresnetaa50d', pretrained, **model_args)
@register_model
def seresnet18(pretrained=False, **kwargs):
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='se'), **kwargs)

@ -4,6 +4,7 @@ A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shi
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer

@ -179,8 +179,8 @@ def _filter_fn(state_dict):
def _create_vgg(variant: str, pretrained: bool, **kwargs: Any) -> VGG:
cfg = variant.split('_')[0]
# NOTE: VGG is one of the only models with stride==1 features, so indices are offset from other models
out_indices = kwargs.get('out_indices', (0, 1, 2, 3, 4, 5))
# NOTE: VGG is one of few models with stride==1 features w/ 6 out_indices [0..5]
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5))
model = build_model_with_cfg(
VGG, variant, pretrained,
model_cfg=cfgs[cfg],

@ -4,6 +4,7 @@ Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.1
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
from copy import deepcopy
@ -23,7 +24,7 @@ __all__ = ['Visformer']
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head',

@ -20,7 +20,7 @@ for some einops/einsum fun
* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
Hacked together by / Copyright 2021 Ross Wightman
Hacked together by / Copyright 2020, Ross Wightman
"""
import math
import logging
@ -105,6 +105,12 @@ default_cfgs = {
'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_huge_patch14_224': _cfg(url=''),
'vit_giant_patch14_224': _cfg(url=''),
'vit_gigantic_patch14_224': _cfg(url=''),
'vit_base2_patch32_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
# patch models, imagenet21k (weights from official Google JAX impl)
'vit_tiny_patch16_224_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
@ -136,11 +142,25 @@ default_cfgs = {
num_classes=21843),
# SAM trained models (https://arxiv.org/abs/2106.01548)
'vit_base_patch32_sam_224': _cfg(
'vit_base_patch32_224_sam': _cfg(
url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
'vit_base_patch16_sam_224': _cfg(
'vit_base_patch16_224_sam': _cfg(
url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),
# DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
'vit_small_patch16_224_dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_small_patch8_224_dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch16_224_dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch8_224_dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
# deit models (FB weights)
'deit_tiny_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth',
@ -184,6 +204,7 @@ default_cfgs = {
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
@ -616,6 +637,16 @@ def vit_base_patch32_224(pretrained=False, **kwargs):
return model
@register_model
def vit_base2_patch32_256(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/32)
# FIXME experiment
"""
model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, **kwargs)
model = _create_vision_transformer('vit_base2_patch32_256', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_patch32_384(pretrained=False, **kwargs):
""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
@ -696,22 +727,29 @@ def vit_large_patch16_384(pretrained=False, **kwargs):
@register_model
def vit_base_patch16_sam_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
def vit_huge_patch14_224(pretrained=False, **kwargs):
""" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
"""
# NOTE original SAM weights release worked with representation_size=768
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
model = _create_vision_transformer('vit_base_patch16_sam_224', pretrained=pretrained, **model_kwargs)
model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_patch32_sam_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
def vit_giant_patch14_224(pretrained=False, **kwargs):
""" ViT-Giant model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
"""
# NOTE original SAM weights release worked with representation_size=768
model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
model = _create_vision_transformer('vit_base_patch32_sam_224', pretrained=pretrained, **model_kwargs)
model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_gigantic_patch14_224(pretrained=False, **kwargs):
""" ViT-Gigantic model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
"""
model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
return model
@ -820,6 +858,62 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
return model
@register_model
def vit_base_patch16_224_sam(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
"""
# NOTE original SAM weights release worked with representation_size=768
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch16_224_sam', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_patch32_224_sam(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
"""
# NOTE original SAM weights release worked with representation_size=768
model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch32_224_sam', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_patch16_224_dino(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch16_224_dino', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_patch8_224_dino(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch8_224_dino', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_patch16_224_dino(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch16_224_dino', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_patch8_224_dino(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
"""
model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch8_224_dino', pretrained=pretrained, **model_kwargs)
return model
@register_model
def deit_tiny_patch16_224(pretrained=False, **kwargs):
""" DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).

@ -11,7 +11,7 @@ A PyTorch implement of the Hybrid Vision Transformers as described in:
NOTE These hybrid model definitions depend on code in vision_transformer.py.
They were moved here to keep file sizes sane.
Hacked together by / Copyright 2021 Ross Wightman
Hacked together by / Copyright 2020, Ross Wightman
"""
from copy import deepcopy
from functools import partial

@ -1,10 +1,12 @@
""" Cross-Covariance Image Transformer (XCiT) in PyTorch
Same as the official implementation, with some minor adaptations.
- https://github.com/facebookresearch/xcit/blob/master/xcit.py
Paper:
- https://arxiv.org/abs/2106.09681
Same as the official implementation, with some minor adaptations, original copyright below
- https://github.com/facebookresearch/xcit/blob/master/xcit.py
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.

@ -114,7 +114,7 @@ class Lars(Optimizer):
)
if group['trust_clip']:
trust_ratio = torch.minimum(trust_ratio / group['lr'], one_tensor)
grad.add(p, alpha=weight_decay)
grad.add_(p, alpha=weight_decay)
grad.mul_(trust_ratio)
# apply SGD update https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100

@ -3,7 +3,7 @@ from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .distributed import distribute_bn, reduce_tensor
from .jit import set_jit_legacy
from .jit import set_jit_legacy, set_jit_fuser
from .log import setup_default_logging, FormatterNoInfo
from .metrics import AverageMeter, accuracy
from .misc import natural_key, add_bool_arg

@ -2,6 +2,8 @@
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
@ -16,3 +18,33 @@ def set_jit_legacy():
torch._C._jit_set_profiling_mode(False)
torch._C._jit_override_can_fuse_on_gpu(True)
#torch._C._jit_set_texpr_fuser_enabled(True)
def set_jit_fuser(fuser):
if fuser == "te":
# default fuser should be == 'te'
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(True)
elif fuser == "old" or fuser == "legacy":
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(False)
elif fuser == "nvfuser" or fuser == "nvf":
os.environ['PYTORCH_CUDA_FUSER_DISABLE_FALLBACK'] = '1'
os.environ['PYTORCH_CUDA_FUSER_DISABLE_FMA'] = '1'
os.environ['PYTORCH_CUDA_FUSER_JIT_OPT_LEVEL'] = '0'
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_can_fuse_on_cpu()
torch._C._jit_can_fuse_on_gpu()
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_nvfuser_guard_mode(True)
torch._C._jit_set_nvfuser_enabled(True)
else:
assert False, f"Invalid jit fuser ({fuser})"

@ -1 +1 @@
__version__ = '0.5.0'
__version__ = '0.5.5'

@ -295,6 +295,8 @@ parser.add_argument('--use-multi-epochs-loader', action='store_true', default=Fa
help='use the multi-epochs-loader to save time at the beginning of every epoch')
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
help='convert model torchscript for inference')
parser.add_argument('--fuser', default='', type=str,
help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
parser.add_argument('--log-wandb', action='store_true', default=False,
help='log training and validation metrics to wandb')
@ -364,6 +366,9 @@ def main():
random_seed(args.seed, args.rank)
if args.fuser:
set_jit_fuser(args.fuser)
model = create_model(
args.model,
pretrained=args.pretrained,

@ -11,6 +11,7 @@ import argparse
import os
import csv
import glob
import json
import time
import logging
import torch
@ -21,7 +22,7 @@ from contextlib import suppress
from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models
from timm.data import create_dataset, create_loader, resolve_data_config, RealLabelsImagenet
from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_legacy
from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_fuser
has_apex = False
try:
@ -102,8 +103,8 @@ parser.add_argument('--use-ema', dest='use_ema', action='store_true',
help='use ema version of weights if present')
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
help='convert model torchscript for inference')
parser.add_argument('--legacy-jit', dest='legacy_jit', action='store_true',
help='use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance')
parser.add_argument('--fuser', default='', type=str,
help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
help='Output csv file for validation results (summary)')
parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
@ -133,8 +134,8 @@ def validate(args):
else:
_logger.info('Validating in float32. AMP not enabled.')
if args.legacy_jit:
set_jit_legacy()
if args.fuser:
set_jit_fuser(args.fuser)
# create model
model = create_model(
@ -265,6 +266,7 @@ def validate(args):
else:
top1a, top5a = top1.avg, top5.avg
results = OrderedDict(
model=args.model,
top1=round(top1a, 4), top1_err=round(100 - top1a, 4),
top5=round(top5a, 4), top5_err=round(100 - top5a, 4),
param_count=round(param_count / 1e6, 2),
@ -278,6 +280,27 @@ def validate(args):
return results
def _try_run(args, initial_batch_size):
batch_size = initial_batch_size
results = OrderedDict()
error_str = 'Unknown'
while batch_size >= 1:
args.batch_size = batch_size
torch.cuda.empty_cache()
try:
results = validate(args)
return results
except RuntimeError as e:
error_str = str(e)
if 'channels_last' in error_str:
break
_logger.warning(f'"{error_str}" while running validation. Reducing batch size to {batch_size} for retry.')
batch_size = batch_size // 2
results['error'] = error_str
_logger.error(f'{args.model} failed to validate ({error_str}).')
return results
def main():
setup_default_logging()
args = parser.parse_args()
@ -293,7 +316,7 @@ def main():
if args.model == 'all':
# validate all models in a list of names with pretrained checkpoints
args.pretrained = True
model_names = list_models(pretrained=True, exclude_filters=['*_in21k', '*_in22k'])
model_names = list_models(pretrained=True, exclude_filters=['*_in21k', '*_in22k', '*_dino'])
model_cfgs = [(n, '') for n in model_names]
elif not is_model(args.model):
# model name doesn't exist, try as wildcard filter
@ -310,36 +333,25 @@ def main():
_logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
results = []
try:
start_batch_size = args.batch_size
initial_batch_size = args.batch_size
for m, c in model_cfgs:
batch_size = start_batch_size
args.model = m
args.checkpoint = c
result = OrderedDict(model=args.model)
r = {}
while not r and batch_size >= args.num_gpu:
torch.cuda.empty_cache()
try:
args.batch_size = batch_size
print('Validating with batch size: %d' % args.batch_size)
r = validate(args)
except RuntimeError as e:
if batch_size <= args.num_gpu:
print("Validation failed with no ability to reduce batch size. Exiting.")
raise e
batch_size = max(batch_size // 2, args.num_gpu)
print("Validation failed, reducing batch size by 50%")
result.update(r)
r = _try_run(args, initial_batch_size)
if 'error' in r:
continue
if args.checkpoint:
result['checkpoint'] = args.checkpoint
results.append(result)
r['checkpoint'] = args.checkpoint
results.append(r)
except KeyboardInterrupt as e:
pass
results = sorted(results, key=lambda x: x['top1'], reverse=True)
if len(results):
write_results(results_file, results)
else:
validate(args)
results = validate(args)
# output results in JSON to stdout w/ delimiter for runner script
print(f'--result\n{json.dumps(results, indent=4)}')
def write_results(results_file, results):

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