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{"config":{"lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Getting Started Install The library can be installed with pip: pip install timm Conda Environment All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x and 3.7.x. To install timm in a conda environment: conda create -n torch-env conda activate torch-env conda install -c pytorch pytorch torchvision cudatoolkit=10.1 conda install pyyaml pip install timm Load Pretrained Model Pretrained models can be loaded using timm.create_model import timm m = timm . create_model ( 'mobilenetv3_100' , pretrained = True ) m . eval () To load a different model see the list of pretrained weights .","title":"Getting Started"},{"location":"#getting-started","text":"","title":"Getting Started"},{"location":"#install","text":"The library can be installed with pip: pip install timm Conda Environment All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x and 3.7.x. To install timm in a conda environment: conda create -n torch-env conda activate torch-env conda install -c pytorch pytorch torchvision cudatoolkit=10.1 conda install pyyaml pip install timm","title":"Install"},{"location":"#load-pretrained-model","text":"Pretrained models can be loaded using timm.create_model import timm m = timm . create_model ( 'mobilenetv3_100' , pretrained = True ) m . eval () To load a different model see the list of pretrained weights .","title":"Load Pretrained Model"},{"location":"changes/","text":"June 11, 2020 Bunch of changes: DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1 Activation factory added along with new activations: select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export) hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish context mgr for setting exportable/scriptable/no_jit states Norm + Activation combo layers added with initial trial support in DenseNet and VoVNet along with impl of EvoNorm and InplaceAbn wrapper that fit the interface Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call Prep for 0.1.28 pip release May 12, 2020 Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt , paper https://arxiv.org/abs/2004.08955 )) May 3, 2020 Pruned EfficientNet B1, B2, and B3 ( https://arxiv.org/abs/2002.08258 ) contributed by Yonathan Aflalo May 1, 2020 Merged a number of execellent contributions in the ResNet model family over the past month BlurPool2D and resnetblur models initiated by Chris Ha , I trained resnetblur50 to 79.3. TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by mrT23 ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per ( https://arxiv.org/abs/2002.08258 ) by Yonathan Aflalo 200 pretrained models in total now with updated results csv in results folder April 5, 2020 Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite 3.5M param MobileNet-V2 100 @ 73% 4.5M param MobileNet-V2 110d @ 75% 6.1M param MobileNet-V2 140 @ 76.5% 5.8M param MobileNet-V2 120d @ 77.3% March 18, 2020 Add EfficientNet-Lite models w/ weights ported from Tensorflow TPU Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by Andrew Lavin (see Training section for hparams) Feb 29, 2020 New MobileNet-V3 Large weights trained from stratch with this code to 75.77% top-1 IMPORTANT CHANGE - default weight init changed for all MobilenetV3 / EfficientNet / related models overall results similar to a bit better training from scratch on a few smaller models tried performance early in training seems consistently improved but less difference by end set fix_group_fanout=False in _init_weight_goog fn if you need to reproducte past behaviour Experimental LR noise feature added applies a random perturbation to LR each epoch in specified range of training Feb 18, 2020 Big refactor of model layers and addition of several attention mechanisms. Several additions motivated by 'Compounding the Performance Improvements...' ( https://arxiv.org/abs/2001.06268 ): Move layer/module impl into layers subfolder/module of models and organize in a more granular fashion ResNet downsample paths now properly support dilation (output stride != 32) for avg_pool ('D' variant) and 3x3 (SENets) networks Add Selective Kernel Nets on top of ResNet base, pretrained weights skresnet18 - 73% top-1 skresnet34 - 76.9% top-1 skresnext50_32x4d (equiv to SKNet50) - 80.2% top-1 ECA and CECA (circular padding) attention layer contributed by Chris Ha CBAM attention experiment (not the best results so far, may remove) Attention factory to allow dynamically selecting one of SE, ECA, CBAM in the .se position for all ResNets Add DropBlock and DropPath (formerly DropConnect for EfficientNet/MobileNetv3) support to all ResNet variants Full dataset results updated that incl NoisyStudent weights and 2 of the 3 SK weights Feb 12, 2020 Add EfficientNet-L2 and B0-B7 NoisyStudent weights ported from Tensorflow TPU","title":"Changes"},{"location":"changes/#june-11-2020","text":"Bunch of changes: DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1 Activation factory added along with new activations: select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export) hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish context mgr for setting exportable/scriptable/no_jit states Norm + Activation combo layers added with initial trial support in DenseNet and VoVNet along with impl of EvoNorm and InplaceAbn wrapper that fit the interface Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call Prep for 0.1.28 pip release","title":"June 11, 2020"},{"location":"changes/#may-12-2020","text":"Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt , paper https://arxiv.org/abs/2004.08955 ))","title":"May 12, 2020"},{"location":"changes/#may-3-2020","text":"Pruned EfficientNet B1, B2, and B3 ( https://arxiv.org/abs/2002.08258 ) contributed by Yonathan Aflalo","title":"May 3, 2020"},{"location":"changes/#may-1-2020","text":"Merged a number of execellent contributions in the ResNet model family over the past month BlurPool2D and resnetblur models initiated by Chris Ha , I trained resnetblur50 to 79.3. TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by mrT23 ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per ( https://arxiv.org/abs/2002.08258 ) by Yonathan Aflalo 200 pretrained models in total now with updated results csv in results folder","title":"May 1, 2020"},{"location":"changes/#april-5-2020","text":"Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite 3.5M param MobileNet-V2 100 @ 73% 4.5M param MobileNet-V2 110d @ 75% 6.1M param MobileNet-V2 140 @ 76.5% 5.8M param MobileNet-V2 120d @ 77.3%","title":"April 5, 2020"},{"location":"changes/#march-18-2020","text":"Add EfficientNet-Lite models w/ weights ported from Tensorflow TPU Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by Andrew Lavin (see Training section for hparams)","title":"March 18, 2020"},{"location":"changes/#feb-29-2020","text":"New MobileNet-V3 Large weights trained from stratch with this code to 75.77% top-1 IMPORTANT CHANGE - default weight init changed for all MobilenetV3 / EfficientNet / related models overall results similar to a bit better training from scratch on a few smaller models tried performance early in training seems consistently improved but less difference by end set fix_group_fanout=False in _init_weight_goog fn if you need to reproducte past behaviour Experimental LR noise feature added applies a random perturbation to LR each epoch in specified range of training","title":"Feb 29, 2020"},{"location":"changes/#feb-18-2020","text":"Big refactor of model layers and addition of several attention mechanisms. Several additions motivated by 'Compounding the Performance Improvements...' ( https://arxiv.org/abs/2001.06268 ): Move layer/module impl into layers subfolder/module of models and organize in a more granular fashion ResNet downsample paths now properly support dilation (output stride != 32) for avg_pool ('D' variant) and 3x3 (SENets) networks Add Selective Kernel Nets on top of ResNet base, pretrained weights skresnet18 - 73% top-1 skresnet34 - 76.9% top-1 skresnext50_32x4d (equiv to SKNet50) - 80.2% top-1 ECA and CECA (circular padding) attention layer contributed by Chris Ha CBAM attention experiment (not the best results so far, may remove) Attention factory to allow dynamically selecting one of SE, ECA, CBAM in the .se position for all ResNets Add DropBlock and DropPath (formerly DropConnect for EfficientNet/MobileNetv3) support to all ResNet variants Full dataset results updated that incl NoisyStudent weights and 2 of the 3 SK weights","title":"Feb 18, 2020"},{"location":"changes/#feb-12-2020","text":"Add EfficientNet-L2 and B0-B7 NoisyStudent weights ported from Tensorflow TPU","title":"Feb 12, 2020"},{"location":"models/","text":"Architectures ResNet / ResNeXt (from torchvision with mods by myself) ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNeXt50 (32x4d), ResNeXt101 (32x4d and 64x4d) 'Bag of Tricks' / Gluon C, D, E, S variations ( https://arxiv.org/abs/1812.01187 ) Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from facebookresearch Res2Net ( https://github.com/gasvn/Res2Net , https://arxiv.org/abs/1904.01169 ) Selective Kernel (SK) Nets ( https://arxiv.org/abs/1903.06586 ) ResNeSt (code adapted from https://github.com/zhanghang1989/ResNeSt , paper https://arxiv.org/abs/2004.08955 ) DLA Original ( https://github.com/ucbdrive/dla , https://arxiv.org/abs/1707.06484 ) Res2Net ( https://github.com/gasvn/Res2Net , https://arxiv.org/abs/1904.01169 ) DenseNet (from torchvision ) DenseNet-121, DenseNet-169, DenseNet-201, DenseNet-161 Squeeze-and-Excitation ResNet/ResNeXt (from Cadene with some pretrained weight additions by myself) SENet-154, SE-ResNet-18, SE-ResNet-34, SE-ResNet-50, SE-ResNet-101, SE-ResNet-152, SE-ResNeXt-26 (32x4d), SE-ResNeXt50 (32x4d), SE-ResNeXt101 (32x4d) Inception-V3 (from torchvision ) Inception-ResNet-V2 and Inception-V4 (from Cadene ) Xception Original variant from Cadene MXNet Gluon 'modified aligned' Xception-65 and 71 models from Gluon ModelZoo PNasNet & NASNet-A (from Cadene ) DPN (from myself ) DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107 EfficientNet (from my standalone GenEfficientNet ) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks EfficientNet NoisyStudent (B0-B7, L2) ( https://arxiv.org/abs/1911.04252 ) EfficientNet AdvProp (B0-B8) ( https://arxiv.org/abs/1911.09665 ) EfficientNet (B0-B7) ( https://arxiv.org/abs/1905.11946 ) EfficientNet-EdgeTPU (S, M, L) ( https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html ) MixNet ( https://arxiv.org/abs/1907.09595 ) MNASNet B1, A1 (Squeeze-Excite), and Small ( https://arxiv.org/abs/1807.11626 ) MobileNet-V2 ( https://arxiv.org/abs/1801.04381 ) FBNet-C ( https://arxiv.org/abs/1812.03443 ) Single-Path NAS ( https://arxiv.org/abs/1904.02877 ) MobileNet-V3 ( https://arxiv.org/abs/1905.02244 ) HRNet code from https://github.com/HRNet/HRNet-Image-Classification , paper https://arxiv.org/abs/1908.07919 SelecSLS code from https://github.com/mehtadushy/SelecSLS-Pytorch , paper https://arxiv.org/abs/1907.00837 TResNet code from https://github.com/mrT23/TResNet , paper https://arxiv.org/abs/2003.13630 RegNet paper Designing Network Design Spaces - https://arxiv.org/abs/2003.13678 reference code at https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py VovNet V2 / V1 paper CenterMask : Real-Time Anchor-Free Instance Segmentation - https://arxiv.org/abs/1911.06667 reference code at https://github.com/youngwanLEE/vovnet-detectron2 Use the --model arg to specify model for train, validation, inference scripts. Match the all lowercase creation fn for the model you'd like. Pretrained Imagenet Weights Self-trained Weights Model Prec@1 (Err) Prec@5 (Err) Param # Image Scaling Image Size efficientnet_b3a 81.874 (18.126) 95.840 (4.160) 12.23M bicubic 320 (1.0 crop) efficientnet_b3 81.498 (18.502) 95.718 (4.282) 12.23M bicubic 300 skresnext50d_32x4d 81.278 (18.722) 95.366 (4.634) 27.5M bicubic 288 (1.0 crop) efficientnet_b2a 80.608 (19.392) 95.310 (4.690) 9.11M bicubic 288 (1.0 crop) mixnet_xl 80.478 (19.522) 94.932 (5.068) 11.90M bicubic 224 efficientnet_b2 80.402 (19.598) 95.076 (4.924) 9.11M bicubic 260 skresnext50d_32x4d 80.156 (19.844) 94.642 (5.358) 27.5M bicubic 224 resnext50_32x4d 79.762 (20.238) 94.600 (5.400) 25M bicubic 224 resnext50d_32x4d 79.674 (20.326) 94.868 (5.132) 25.1M bicubic 224 ese_vovnet39b 79.320 (20.680) 94.710 (5.290) 24.6M bicubic 224 resnetblur50 79.290 (20.710) 94.632 (5.368) 25.6M bicubic 224 resnet50 79.038 (20.962) 94.390 (5.610) 25.6M bicubic 224 mixnet_l 78.976 (21.024 94.184 (5.816) 7.33M bicubic 224 efficientnet_b1 78.692 (21.308) 94.086 (5.914) 7.79M bicubic 240 efficientnet_es 78.066 (21.934) 93.926 (6.074) 5.44M bicubic 224 seresnext26t_32x4d 77.998 (22.002) 93.708 (6.292) 16.8M bicubic 224 seresnext26tn_32x4d 77.986 (22.014) 93.746 (6.254) 16.8M bicubic 224 efficientnet_b0 77.698 (22.302) 93.532 (6.468) 5.29M bicubic 224 seresnext26d_32x4d 77.602 (22.398) 93.608 (6.392) 16.8M bicubic 224 mobilenetv2_120d 77.294 (22.706 93.502 (6.498) 5.8M bicubic 224 mixnet_m 77.256 (22.744) 93.418 (6.582) 5.01M bicubic 224 seresnext26_32x4d 77.104 (22.896) 93.316 (6.684) 16.8M bicubic 224 skresnet34 76.912 (23.088) 93.322 (6.678) 22.2M bicubic 224 ese_vovnet19b_dw 76.798 (23.202) 93.268 (6.732) 6.5M bicubic 224 resnet26d 76.68 (23.32) 93.166 (6.834) 16M bicubic 224 densenetblur121d 76.576 (23.424) 93.190 (6.810) 8.0M bicubic 224 mobilenetv2_140 76.524 (23.476) 92.990 (7.010) 6.1M bicubic 224 mixnet_s 75.988 (24.012) 92.794 (7.206) 4.13M bicubic 224 mobilenetv3_large_100 75.766 (24.234) 92.542 (7.458) 5.5M bicubic 224 mobilenetv3_rw 75.634 (24.366) 92.708 (7.292) 5.5M bicubic 224 mnasnet_a1 75.448 (24.552) 92.604 (7.396) 3.89M bicubic 224 resnet26 75.292 (24.708) 92.57 (7.43) 16M bicubic 224 fbnetc_100 75.124 (24.876) 92.386 (7.614) 5.6M bilinear 224 resnet34 75.110 (24.890) 92.284 (7.716) 22M bilinear 224 mobilenetv2_110d 75.052 (24.948) 92.180 (7.820) 4.5M bicubic 224 seresnet34 74.808 (25.192) 92.124 (7.876) 22M bilinear 224 mnasnet_b1 74.658 (25.342) 92.114 (7.886) 4.38M bicubic 224 spnasnet_100 74.084 (25.916) 91.818 (8.182) 4.42M bilinear 224 skresnet18 73.038 (26.962) 91.168 (8.832) 11.9M bicubic 224 mobilenetv2_100 72.978 (27.022) 91.016 (8.984) 3.5M bicubic 224 seresnet18 71.742 (28.258) 90.334 (9.666) 11.8M bicubic 224 Ported Weights For the models below, the model code and weight porting from Tensorflow or MXNet Gluon to Pytorch was done by myself. There are weights/models ported by others included in this repository, they are not listed below. Model Prec@1 (Err) Prec@5 (Err) Param # Image Scaling Image Size tf_efficientnet_l2_ns *tfp 88.352 (11.648) 98.652 (1.348) 480 bicubic 800 tf_efficientnet_l2_ns TBD TBD 480 bicubic 800 tf_efficientnet_l2_ns_475 88.234 (11.766) 98.546 (1.454)f 480 bicubic 475 tf_efficientnet_l2_ns_475 *tfp 88.172 (11.828) 98.566 (1.434) 480 bicubic 475 tf_efficientnet_b7_ns *tfp 86.844 (13.156) 98.084 (1.916) 66.35 bicubic 600 tf_efficientnet_b7_ns 86.840 (13.160) 98.094 (1.906) 66.35 bicubic 600 tf_efficientnet_b6_ns 86.452 (13.548) 97.882 (2.118) 43.04 bicubic 528 tf_efficientnet_b6_ns *tfp 86.444 (13.556) 97.880 (2.120) 43.04 bicubic 528 tf_efficientnet_b5_ns *tfp 86.064 (13.936) 97.746 (2.254) 30.39 bicubic 456 tf_efficientnet_b5_ns 86.088 (13.912) 97.752 (2.248) 30.39 bicubic 456 tf_efficientnet_b8_ap *tfp 85.436 (14.564) 97.272 (2.728) 87.4 bicubic 672 tf_efficientnet_b8 *tfp 85.384 (14.616) 97.394 (2.606) 87.4 bicubic 672 tf_efficientnet_b8 85.370 (14.630) 97.390 (2.610) 87.4 bicubic 672 tf_efficientnet_b8_ap 85.368 (14.632) 97.294 (2.706) 87.4 bicubic 672 tf_efficientnet_b4_ns *tfp 85.298 (14.702) 97.504 (2.496) 19.34 bicubic 380 tf_efficientnet_b4_ns 85.162 (14.838) 97.470 (2.530) 19.34 bicubic 380 tf_efficientnet_b7_ap *tfp 85.154 (14.846) 97.244 (2.756) 66.35 bicubic 600 tf_efficientnet_b7_ap 85.118 (14.882) 97.252 (2.748) 66.35 bicubic 600 tf_efficientnet_b7 *tfp 84.940 (15.060) 97.214 (2.786) 66.35 bicubic 600 tf_efficientnet_b7 84.932 (15.068) 97.208 (2.792) 66.35 bicubic 600 tf_efficientnet_b6_ap 84.786 (15.214) 97.138 (2.862) 43.04 bicubic 528 tf_efficientnet_b6_ap *tfp 84.760 (15.240) 97.124 (2.876) 43.04 bicubic 528 tf_efficientnet_b5_ap *tfp 84.276 (15.724) 96.932 (3.068) 30.39 bicubic 456 tf_efficientnet_b5_ap 84.254 (15.746) 96.976 (3.024) 30.39 bicubic 456 tf_efficientnet_b6 *tfp 84.140 (15.860) 96.852 (3.148) 43.04 bicubic 528 tf_efficientnet_b6 84.110 (15.890) 96.886 (3.114) 43.04 bicubic 528 tf_efficientnet_b3_ns *tfp 84.054 (15.946) 96.918 (3.082) 12.23 bicubic 300 tf_efficientnet_b3_ns 84.048 (15.952) 96.910 (3.090) 12.23 bicubic 300 tf_efficientnet_b5 *tfp 83.822 (16.178) 96.756 (3.244) 30.39 bicubic 456 tf_efficientnet_b5 83.812 (16.188) 96.748 (3.252) 30.39 bicubic 456 tf_efficientnet_b4_ap *tfp 83.278 (16.722) 96.376 (3.624) 19.34 bicubic 380 tf_efficientnet_b4_ap 83.248 (16.752) 96.388 (3.612) 19.34 bicubic 380 tf_efficientnet_b4 83.022 (16.978) 96.300 (3.700) 19.34 bicubic 380 tf_efficientnet_b4 *tfp 82.948 (17.052) 96.308 (3.692) 19.34 bicubic 380 tf_efficientnet_b2_ns *tfp 82.436 (17.564) 96.268 (3.732) 9.11 bicubic 260 tf_efficientnet_b2_ns 82.380 (17.620) 96.248 (3.752) 9.11 bicubic 260 tf_efficientnet_b3_ap *tfp 81.882 (18.118) 95.662 (4.338) 12.23 bicubic 300 tf_efficientnet_b3_ap 81.828 (18.172) 95.624 (4.376) 12.23 bicubic 300 tf_efficientnet_b3 81.636 (18.364) 95.718 (4.282) 12.23 bicubic 300 tf_efficientnet_b3 *tfp 81.576 (18.424) 95.662 (4.338) 12.23 bicubic 300 tf_efficientnet_lite4 81.528 (18.472) 95.668 (4.332) 13.00 bilinear 380 tf_efficientnet_b1_ns *tfp 81.514 (18.486) 95.776 (4.224) 7.79 bicubic 240 tf_efficientnet_lite4 *tfp 81.502 (18.498) 95.676 (4.324) 13.00 bilinear 380 tf_efficientnet_b1_ns 81.388 (18.612) 95.738 (4.262) 7.79 bicubic 240 gluon_senet154 81.224 (18.776) 95.356 (4.644) 115.09 bicubic 224 gluon_resnet152_v1s 81.012 (18.988) 95.416 (4.584) 60.32 bicubic 224 gluon_seresnext101_32x4d 80.902 (19.098) 95.294 (4.706) 48.96 bicubic 224 gluon_seresnext101_64x4d 80.890 (19.110) 95.304 (4.696) 88.23 bicubic 224 gluon_resnext101_64x4d 80.602 (19.398) 94.994 (5.006) 83.46 bicubic 224 tf_efficientnet_el 80.534 (19.466) 95.190 (4.810) 10.59 bicubic 300 tf_efficientnet_el *tfp 80.476 (19.524) 95.200 (4.800) 10.59 bicubic 300 gluon_resnet152_v1d 80.470 (19.530) 95.206 (4.794) 60.21 bicubic 224 gluon_resnet101_v1d 80.424 (19.576) 95.020 (4.980) 44.57 bicubic 224 tf_efficientnet_b2_ap *tfp 80.420 (19.580) 95.040 (4.960) 9.11 bicubic 260 gluon_resnext101_32x4d 80.334 (19.666) 94.926 (5.074) 44.18 bicubic 224 tf_efficientnet_b2_ap 80.306 (19.694) 95.028 (4.972) 9.11 bicubic 260 gluon_resnet101_v1s 80.300 (19.700) 95.150 (4.850) 44.67 bicubic 224 tf_efficientnet_b2 *tfp 80.188 (19.812) 94.974 (5.026) 9.11 bicubic 260 tf_efficientnet_b2 80.086 (19.914) 94.908 (5.092) 9.11 bicubic 260 gluon_resnet152_v1c 79.916 (20.084) 94.842 (5.158) 60.21 bicubic 224 gluon_seresnext50_32x4d 79.912 (20.088) 94.818 (5.182) 27.56 bicubic 224 tf_efficientnet_lite3 79.812 (20.188) 94.914 (5.086) 8.20 bilinear 300 tf_efficientnet_lite3 *tfp 79.734 (20.266) 94.838 (5.162) 8.20 bilinear 300 gluon_resnet152_v1b 79.692 (20.308) 94.738 (5.262) 60.19 bicubic 224 gluon_xception65 79.604 (20.396) 94.748 (5.252) 39.92 bicubic 299 gluon_resnet101_v1c 79.544 (20.456) 94.586 (5.414) 44.57 bicubic 224 tf_efficientnet_b1_ap *tfp 79.532 (20.468) 94.378 (5.622) 7.79 bicubic 240 tf_efficientnet_cc_b1_8e *tfp 79.464 (20.536) 94.492 (5.508) 39.7 bicubic 240 gluon_resnext50_32x4d 79.356 (20.644) 94.424 (5.576) 25.03 bicubic 224 gluon_resnet101_v1b 79.304 (20.696) 94.524 (5.476) 44.55 bicubic 224 tf_efficientnet_cc_b1_8e 79.298 (20.702) 94.364 (5.636) 39.7 bicubic 240 tf_efficientnet_b1_ap 79.278 (20.722) 94.308 (5.692) 7.79 bicubic 240 tf_efficientnet_b1 *tfp 79.172 (20.828) 94.450 (5.550) 7.79 bicubic 240 gluon_resnet50_v1d 79.074 (20.926) 94.476 (5.524) 25.58 bicubic 224 tf_efficientnet_em *tfp 78.958 (21.042) 94.458 (5.542) 6.90 bicubic 240 tf_mixnet_l *tfp 78.846 (21.154) 94.212 (5.788) 7.33 bilinear 224 tf_efficientnet_b1 78.826 (21.174) 94.198 (5.802) 7.79 bicubic 240 tf_efficientnet_b0_ns *tfp 78.806 (21.194) 94.496 (5.504) 5.29 bicubic 224 gluon_inception_v3 78.804 (21.196) 94.380 (5.620) 27.16M bicubic 299 tf_mixnet_l 78.770 (21.230) 94.004 (5.996) 7.33 bicubic 224 tf_efficientnet_em 78.742 (21.258) 94.332 (5.668) 6.90 bicubic 240 gluon_resnet50_v1s 78.712 (21.288) 94.242 (5.758) 25.68 bicubic 224 tf_efficientnet_b0_ns 78.658 (21.342) 94.376 (5.624) 5.29 bicubic 224 tf_efficientnet_cc_b0_8e *tfp 78.314 (21.686) 93.790 (6.210) 24.0 bicubic 224 gluon_resnet50_v1c 78.010 (21.990) 93.988 (6.012) 25.58 bicubic 224 tf_efficientnet_cc_b0_8e 77.908 (22.092) 93.656 (6.344) 24.0 bicubic 224 tf_inception_v3 77.856 (22.144) 93.644 (6.356) 27.16M bicubic 299 tf_efficientnet_cc_b0_4e *tfp 77.746 (22.254) 93.552 (6.448) 13.3 bicubic 224 tf_efficientnet_es *tfp 77.616 (22.384) 93.750 (6.250) 5.44 bicubic 224 gluon_resnet50_v1b 77.578 (22.422) 93.718 (6.282) 25.56 bicubic 224 adv_inception_v3 77.576 (22.424) 93.724 (6.276) 27.16M bicubic 299 tf_efficientnet_lite2 *tfp 77.544 (22.456) 93.800 (6.200) 6.09 bilinear 260 tf_efficientnet_lite2 77.460 (22.540) 93.746 (6.254) 6.09 bicubic 260 tf_efficientnet_b0_ap *tfp 77.514 (22.486) 93.576 (6.424) 5.29 bicubic 224 tf_efficientnet_cc_b0_4e 77.304 (22.696) 93.332 (6.668) 13.3 bicubic 224 tf_efficientnet_es 77.264 (22.736) 93.600 (6.400) 5.44 bicubic 224 tf_efficientnet_b0 *tfp 77.258 (22.742) 93.478 (6.522) 5.29 bicubic 224 tf_efficientnet_b0_ap 77.084 (22.916) 93.254 (6.746) 5.29 bicubic 224 tf_mixnet_m *tfp 77.072 (22.928) 93.368 (6.632) 5.01 bilinear 224 tf_mixnet_m 76.950 (23.050) 93.156 (6.844) 5.01 bicubic 224 tf_efficientnet_b0 76.848 (23.152) 93.228 (6.772) 5.29 bicubic 224 tf_efficientnet_lite1 *tfp 76.764 (23.236) 93.326 (6.674) 5.42 bilinear 240 tf_efficientnet_lite1 76.638 (23.362) 93.232 (6.768) 5.42 bicubic 240 tf_mixnet_s *tfp 75.800 (24.200) 92.788 (7.212) 4.13 bilinear 224 tf_mobilenetv3_large_100 *tfp 75.768 (24.232) 92.710 (7.290) 5.48 bilinear 224 tf_mixnet_s 75.648 (24.352) 92.636 (7.364) 4.13 bicubic 224 tf_mobilenetv3_large_100 75.516 (24.484) 92.600 (7.400) 5.48 bilinear 224 tf_efficientnet_lite0 *tfp 75.074 (24.926) 92.314 (7.686) 4.65 bilinear 224 tf_efficientnet_lite0 74.842 (25.158) 92.170 (7.830) 4.65 bicubic 224 gluon_resnet34_v1b 74.580 (25.420) 91.988 (8.012) 21.80 bicubic 224 tf_mobilenetv3_large_075 *tfp 73.730 (26.270) 91.616 (8.384) 3.99 bilinear 224 tf_mobilenetv3_large_075 73.442 (26.558) 91.352 (8.648) 3.99 bilinear 224 tf_mobilenetv3_large_minimal_100 *tfp 72.678 (27.322) 90.860 (9.140) 3.92 bilinear 224 tf_mobilenetv3_large_minimal_100 72.244 (27.756) 90.636 (9.364) 3.92 bilinear 224 tf_mobilenetv3_small_100 *tfp 67.918 (32.082) 87.958 (12.042 2.54 bilinear 224 tf_mobilenetv3_small_100 67.918 (32.082) 87.662 (12.338) 2.54 bilinear 224 tf_mobilenetv3_small_075 *tfp 66.142 (33.858) 86.498 (13.502) 2.04 bilinear 224 tf_mobilenetv3_small_075 65.718 (34.282) 86.136 (13.864) 2.04 bilinear 224 tf_mobilenetv3_small_minimal_100 *tfp 63.378 (36.622) 84.802 (15.198) 2.04 bilinear 224 tf_mobilenetv3_small_minimal_100 62.898 (37.102) 84.230 (15.770) 2.04 bilinear 224","title":"Models"},{"location":"models/#architectures","text":"","title":"Architectures"},{"location":"models/#resnet-resnext","text":"(from torchvision with mods by myself) ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNeXt50 (32x4d), ResNeXt101 (32x4d and 64x4d) 'Bag of Tricks' / Gluon C, D, E, S variations ( https://arxiv.org/abs/1812.01187 ) Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from facebookresearch Res2Net ( https://github.com/gasvn/Res2Net , https://arxiv.org/abs/1904.01169 ) Selective Kernel (SK) Nets ( https://arxiv.org/abs/1903.06586 ) ResNeSt (code adapted from https://github.com/zhanghang1989/ResNeSt , paper https://arxiv.org/abs/2004.08955 )","title":"ResNet / ResNeXt"},{"location":"models/#dla","text":"Original ( https://github.com/ucbdrive/dla , https://arxiv.org/abs/1707.06484 ) Res2Net ( https://github.com/gasvn/Res2Net , https://arxiv.org/abs/1904.01169 )","title":"DLA"},{"location":"models/#densenet","text":"(from torchvision ) DenseNet-121, DenseNet-169, DenseNet-201, DenseNet-161","title":"DenseNet"},{"location":"models/#squeeze-and-excitation-resnetresnext","text":"(from Cadene with some pretrained weight additions by myself) SENet-154, SE-ResNet-18, SE-ResNet-34, SE-ResNet-50, SE-ResNet-101, SE-ResNet-152, SE-ResNeXt-26 (32x4d), SE-ResNeXt50 (32x4d), SE-ResNeXt101 (32x4d)","title":"Squeeze-and-Excitation ResNet/ResNeXt"},{"location":"models/#inception-v3","text":"(from torchvision )","title":"Inception-V3"},{"location":"models/#inception-resnet-v2-and-inception-v4","text":"(from Cadene )","title":"Inception-ResNet-V2 and Inception-V4"},{"location":"models/#xception","text":"Original variant from Cadene MXNet Gluon 'modified aligned' Xception-65 and 71 models from Gluon ModelZoo","title":"Xception"},{"location":"models/#pnasnet-nasnet-a","text":"(from Cadene )","title":"PNasNet & NASNet-A"},{"location":"models/#dpn","text":"(from myself ) DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107","title":"DPN"},{"location":"models/#efficientnet","text":"(from my standalone GenEfficientNet ) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks EfficientNet NoisyStudent (B0-B7, L2) ( https://arxiv.org/abs/1911.04252 ) EfficientNet AdvProp (B0-B8) ( https://arxiv.org/abs/1911.09665 ) EfficientNet (B0-B7) ( https://arxiv.org/abs/1905.11946 ) EfficientNet-EdgeTPU (S, M, L) ( https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html ) MixNet ( https://arxiv.org/abs/1907.09595 ) MNASNet B1, A1 (Squeeze-Excite), and Small ( https://arxiv.org/abs/1807.11626 ) MobileNet-V2 ( https://arxiv.org/abs/1801.04381 ) FBNet-C ( https://arxiv.org/abs/1812.03443 ) Single-Path NAS ( https://arxiv.org/abs/1904.02877 )","title":"EfficientNet"},{"location":"models/#mobilenet-v3","text":"( https://arxiv.org/abs/1905.02244 )","title":"MobileNet-V3"},{"location":"models/#hrnet","text":"code from https://github.com/HRNet/HRNet-Image-Classification , paper https://arxiv.org/abs/1908.07919","title":"HRNet"},{"location":"models/#selecsls","text":"code from https://github.com/mehtadushy/SelecSLS-Pytorch , paper https://arxiv.org/abs/1907.00837","title":"SelecSLS"},{"location":"models/#tresnet","text":"code from https://github.com/mrT23/TResNet , paper https://arxiv.org/abs/2003.13630","title":"TResNet"},{"location":"models/#regnet","text":"paper Designing Network Design Spaces - https://arxiv.org/abs/2003.13678 reference code at https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py","title":"RegNet"},{"location":"models/#vovnet-v2-v1","text":"paper CenterMask : Real-Time Anchor-Free Instance Segmentation - https://arxiv.org/abs/1911.06667 reference code at https://github.com/youngwanLEE/vovnet-detectron2 Use the --model arg to specify model for train, validation, inference scripts. Match the all lowercase creation fn for the model you'd like.","title":"VovNet V2 / V1"},{"location":"models/#pretrained-imagenet-weights","text":"","title":"Pretrained Imagenet Weights"},{"location":"models/#self-trained-weights","text":"Model Prec@1 (Err) Prec@5 (Err) Param # Image Scaling Image Size efficientnet_b3a 81.874 (18.126) 95.840 (4.160) 12.23M bicubic 320 (1.0 crop) efficientnet_b3 81.498 (18.502) 95.718 (4.282) 12.23M bicubic 300 skresnext50d_32x4d 81.278 (18.722) 95.366 (4.634) 27.5M bicubic 288 (1.0 crop) efficientnet_b2a 80.608 (19.392) 95.310 (4.690) 9.11M bicubic 288 (1.0 crop) mixnet_xl 80.478 (19.522) 94.932 (5.068) 11.90M bicubic 224 efficientnet_b2 80.402 (19.598) 95.076 (4.924) 9.11M bicubic 260 skresnext50d_32x4d 80.156 (19.844) 94.642 (5.358) 27.5M bicubic 224 resnext50_32x4d 79.762 (20.238) 94.600 (5.400) 25M bicubic 224 resnext50d_32x4d 79.674 (20.326) 94.868 (5.132) 25.1M bicubic 224 ese_vovnet39b 79.320 (20.680) 94.710 (5.290) 24.6M bicubic 224 resnetblur50 79.290 (20.710) 94.632 (5.368) 25.6M bicubic 224 resnet50 79.038 (20.962) 94.390 (5.610) 25.6M bicubic 224 mixnet_l 78.976 (21.024 94.184 (5.816) 7.33M bicubic 224 efficientnet_b1 78.692 (21.308) 94.086 (5.914) 7.79M bicubic 240 efficientnet_es 78.066 (21.934) 93.926 (6.074) 5.44M bicubic 224 seresnext26t_32x4d 77.998 (22.002) 93.708 (6.292) 16.8M bicubic 224 seresnext26tn_32x4d 77.986 (22.014) 93.746 (6.254) 16.8M bicubic 224 efficientnet_b0 77.698 (22.302) 93.532 (6.468) 5.29M bicubic 224 seresnext26d_32x4d 77.602 (22.398) 93.608 (6.392) 16.8M bicubic 224 mobilenetv2_120d 77.294 (22.706 93.502 (6.498) 5.8M bicubic 224 mixnet_m 77.256 (22.744) 93.418 (6.582) 5.01M bicubic 224 seresnext26_32x4d 77.104 (22.896) 93.316 (6.684) 16.8M bicubic 224 skresnet34 76.912 (23.088) 93.322 (6.678) 22.2M bicubic 224 ese_vovnet19b_dw 76.798 (23.202) 93.268 (6.732) 6.5M bicubic 224 resnet26d 76.68 (23.32) 93.166 (6.834) 16M bicubic 224 densenetblur121d 76.576 (23.424) 93.190 (6.810) 8.0M bicubic 224 mobilenetv2_140 76.524 (23.476) 92.990 (7.010) 6.1M bicubic 224 mixnet_s 75.988 (24.012) 92.794 (7.206) 4.13M bicubic 224 mobilenetv3_large_100 75.766 (24.234) 92.542 (7.458) 5.5M bicubic 224 mobilenetv3_rw 75.634 (24.366) 92.708 (7.292) 5.5M bicubic 224 mnasnet_a1 75.448 (24.552) 92.604 (7.396) 3.89M bicubic 224 resnet26 75.292 (24.708) 92.57 (7.43) 16M bicubic 224 fbnetc_100 75.124 (24.876) 92.386 (7.614) 5.6M bilinear 224 resnet34 75.110 (24.890) 92.284 (7.716) 22M bilinear 224 mobilenetv2_110d 75.052 (24.948) 92.180 (7.820) 4.5M bicubic 224 seresnet34 74.808 (25.192) 92.124 (7.876) 22M bilinear 224 mnasnet_b1 74.658 (25.342) 92.114 (7.886) 4.38M bicubic 224 spnasnet_100 74.084 (25.916) 91.818 (8.182) 4.42M bilinear 224 skresnet18 73.038 (26.962) 91.168 (8.832) 11.9M bicubic 224 mobilenetv2_100 72.978 (27.022) 91.016 (8.984) 3.5M bicubic 224 seresnet18 71.742 (28.258) 90.334 (9.666) 11.8M bicubic 224","title":"Self-trained Weights"},{"location":"models/#ported-weights","text":"For the models below, the model code and weight porting from Tensorflow or MXNet Gluon to Pytorch was done by myself. There are weights/models ported by others included in this repository, they are not listed below. Model Prec@1 (Err) Prec@5 (Err) Param # Image Scaling Image Size tf_efficientnet_l2_ns *tfp 88.352 (11.648) 98.652 (1.348) 480 bicubic 800 tf_efficientnet_l2_ns TBD TBD 480 bicubic 800 tf_efficientnet_l2_ns_475 88.234 (11.766) 98.546 (1.454)f 480 bicubic 475 tf_efficientnet_l2_ns_475 *tfp 88.172 (11.828) 98.566 (1.434) 480 bicubic 475 tf_efficientnet_b7_ns *tfp 86.844 (13.156) 98.084 (1.916) 66.35 bicubic 600 tf_efficientnet_b7_ns 86.840 (13.160) 98.094 (1.906) 66.35 bicubic 600 tf_efficientnet_b6_ns 86.452 (13.548) 97.882 (2.118) 43.04 bicubic 528 tf_efficientnet_b6_ns *tfp 86.444 (13.556) 97.880 (2.120) 43.04 bicubic 528 tf_efficientnet_b5_ns *tfp 86.064 (13.936) 97.746 (2.254) 30.39 bicubic 456 tf_efficientnet_b5_ns 86.088 (13.912) 97.752 (2.248) 30.39 bicubic 456 tf_efficientnet_b8_ap *tfp 85.436 (14.564) 97.272 (2.728) 87.4 bicubic 672 tf_efficientnet_b8 *tfp 85.384 (14.616) 97.394 (2.606) 87.4 bicubic 672 tf_efficientnet_b8 85.370 (14.630) 97.390 (2.610) 87.4 bicubic 672 tf_efficientnet_b8_ap 85.368 (14.632) 97.294 (2.706) 87.4 bicubic 672 tf_efficientnet_b4_ns *tfp 85.298 (14.702) 97.504 (2.496) 19.34 bicubic 380 tf_efficientnet_b4_ns 85.162 (14.838) 97.470 (2.530) 19.34 bicubic 380 tf_efficientnet_b7_ap *tfp 85.154 (14.846) 97.244 (2.756) 66.35 bicubic 600 tf_efficientnet_b7_ap 85.118 (14.882) 97.252 (2.748) 66.35 bicubic 600 tf_efficientnet_b7 *tfp 84.940 (15.060) 97.214 (2.786) 66.35 bicubic 600 tf_efficientnet_b7 84.932 (15.068) 97.208 (2.792) 66.35 bicubic 600 tf_efficientnet_b6_ap 84.786 (15.214) 97.138 (2.862) 43.04 bicubic 528 tf_efficientnet_b6_ap *tfp 84.760 (15.240) 97.124 (2.876) 43.04 bicubic 528 tf_efficientnet_b5_ap *tfp 84.276 (15.724) 96.932 (3.068) 30.39 bicubic 456 tf_efficientnet_b5_ap 84.254 (15.746) 96.976 (3.024) 30.39 bicubic 456 tf_efficientnet_b6 *tfp 84.140 (15.860) 96.852 (3.148) 43.04 bicubic 528 tf_efficientnet_b6 84.110 (15.890) 96.886 (3.114) 43.04 bicubic 528 tf_efficientnet_b3_ns *tfp 84.054 (15.946) 96.918 (3.082) 12.23 bicubic 300 tf_efficientnet_b3_ns 84.048 (15.952) 96.910 (3.090) 12.23 bicubic 300 tf_efficientnet_b5 *tfp 83.822 (16.178) 96.756 (3.244) 30.39 bicubic 456 tf_efficientnet_b5 83.812 (16.188) 96.748 (3.252) 30.39 bicubic 456 tf_efficientnet_b4_ap *tfp 83.278 (16.722) 96.376 (3.624) 19.34 bicubic 380 tf_efficientnet_b4_ap 83.248 (16.752) 96.388 (3.612) 19.34 bicubic 380 tf_efficientnet_b4 83.022 (16.978) 96.300 (3.700) 19.34 bicubic 380 tf_efficientnet_b4 *tfp 82.948 (17.052) 96.308 (3.692) 19.34 bicubic 380 tf_efficientnet_b2_ns *tfp 82.436 (17.564) 96.268 (3.732) 9.11 bicubic 260 tf_efficientnet_b2_ns 82.380 (17.620) 96.248 (3.752) 9.11 bicubic 260 tf_efficientnet_b3_ap *tfp 81.882 (18.118) 95.662 (4.338) 12.23 bicubic 300 tf_efficientnet_b3_ap 81.828 (18.172) 95.624 (4.376) 12.23 bicubic 300 tf_efficientnet_b3 81.636 (18.364) 95.718 (4.282) 12.23 bicubic 300 tf_efficientnet_b3 *tfp 81.576 (18.424) 95.662 (4.338) 12.23 bicubic 300 tf_efficientnet_lite4 81.528 (18.472) 95.668 (4.332) 13.00 bilinear 380 tf_efficientnet_b1_ns *tfp 81.514 (18.486) 95.776 (4.224) 7.79 bicubic 240 tf_efficientnet_lite4 *tfp 81.502 (18.498) 95.676 (4.324) 13.00 bilinear 380 tf_efficientnet_b1_ns 81.388 (18.612) 95.738 (4.262) 7.79 bicubic 240 gluon_senet154 81.224 (18.776) 95.356 (4.644) 115.09 bicubic 224 gluon_resnet152_v1s 81.012 (18.988) 95.416 (4.584) 60.32 bicubic 224 gluon_seresnext101_32x4d 80.902 (19.098) 95.294 (4.706) 48.96 bicubic 224 gluon_seresnext101_64x4d 80.890 (19.110) 95.304 (4.696) 88.23 bicubic 224 gluon_resnext101_64x4d 80.602 (19.398) 94.994 (5.006) 83.46 bicubic 224 tf_efficientnet_el 80.534 (19.466) 95.190 (4.810) 10.59 bicubic 300 tf_efficientnet_el *tfp 80.476 (19.524) 95.200 (4.800) 10.59 bicubic 300 gluon_resnet152_v1d 80.470 (19.530) 95.206 (4.794) 60.21 bicubic 224 gluon_resnet101_v1d 80.424 (19.576) 95.020 (4.980) 44.57 bicubic 224 tf_efficientnet_b2_ap *tfp 80.420 (19.580) 95.040 (4.960) 9.11 bicubic 260 gluon_resnext101_32x4d 80.334 (19.666) 94.926 (5.074) 44.18 bicubic 224 tf_efficientnet_b2_ap 80.306 (19.694) 95.028 (4.972) 9.11 bicubic 260 gluon_resnet101_v1s 80.300 (19.700) 95.150 (4.850) 44.67 bicubic 224 tf_efficientnet_b2 *tfp 80.188 (19.812) 94.974 (5.026) 9.11 bicubic 260 tf_efficientnet_b2 80.086 (19.914) 94.908 (5.092) 9.11 bicubic 260 gluon_resnet152_v1c 79.916 (20.084) 94.842 (5.158) 60.21 bicubic 224 gluon_seresnext50_32x4d 79.912 (20.088) 94.818 (5.182) 27.56 bicubic 224 tf_efficientnet_lite3 79.812 (20.188) 94.914 (5.086) 8.20 bilinear 300 tf_efficientnet_lite3 *tfp 79.734 (20.266) 94.838 (5.162) 8.20 bilinear 300 gluon_resnet152_v1b 79.692 (20.308) 94.738 (5.262) 60.19 bicubic 224 gluon_xception65 79.604 (20.396) 94.748 (5.252) 39.92 bicubic 299 gluon_resnet101_v1c 79.544 (20.456) 94.586 (5.414) 44.57 bicubic 224 tf_efficientnet_b1_ap *tfp 79.532 (20.468) 94.378 (5.622) 7.79 bicubic 240 tf_efficientnet_cc_b1_8e *tfp 79.464 (20.536) 94.492 (5.508) 39.7 bicubic 240 gluon_resnext50_32x4d 79.356 (20.644) 94.424 (5.576) 25.03 bicubic 224 gluon_resnet101_v1b 79.304 (20.696) 94.524 (5.476) 44.55 bicubic 224 tf_efficientnet_cc_b1_8e 79.298 (20.702) 94.364 (5.636) 39.7 bicubic 240 tf_efficientnet_b1_ap 79.278 (20.722) 94.308 (5.692) 7.79 bicubic 240 tf_efficientnet_b1 *tfp 79.172 (20.828) 94.450 (5.550) 7.79 bicubic 240 gluon_resnet50_v1d 79.074 (20.926) 94.476 (5.524) 25.58 bicubic 224 tf_efficientnet_em *tfp 78.958 (21.042) 94.458 (5.542) 6.90 bicubic 240 tf_mixnet_l *tfp 78.846 (21.154) 94.212 (5.788) 7.33 bilinear 224 tf_efficientnet_b1 78.826 (21.174) 94.198 (5.802) 7.79 bicubic 240 tf_efficientnet_b0_ns *tfp 78.806 (21.194) 94.496 (5.504) 5.29 bicubic 224 gluon_inception_v3 78.804 (21.196) 94.380 (5.620) 27.16M bicubic 299 tf_mixnet_l 78.770 (21.230) 94.004 (5.996) 7.33 bicubic 224 tf_efficientnet_em 78.742 (21.258) 94.332 (5.668) 6.90 bicubic 240 gluon_resnet50_v1s 78.712 (21.288) 94.242 (5.758) 25.68 bicubic 224 tf_efficientnet_b0_ns 78.658 (21.342) 94.376 (5.624) 5.29 bicubic 224 tf_efficientnet_cc_b0_8e *tfp 78.314 (21.686) 93.790 (6.210) 24.0 bicubic 224 gluon_resnet50_v1c 78.010 (21.990) 93.988 (6.012) 25.58 bicubic 224 tf_efficientnet_cc_b0_8e 77.908 (22.092) 93.656 (6.344) 24.0 bicubic 224 tf_inception_v3 77.856 (22.144) 93.644 (6.356) 27.16M bicubic 299 tf_efficientnet_cc_b0_4e *tfp 77.746 (22.254) 93.552 (6.448) 13.3 bicubic 224 tf_efficientnet_es *tfp 77.616 (22.384) 93.750 (6.250) 5.44 bicubic 224 gluon_resnet50_v1b 77.578 (22.422) 93.718 (6.282) 25.56 bicubic 224 adv_inception_v3 77.576 (22.424) 93.724 (6.276) 27.16M bicubic 299 tf_efficientnet_lite2 *tfp 77.544 (22.456) 93.800 (6.200) 6.09 bilinear 260 tf_efficientnet_lite2 77.460 (22.540) 93.746 (6.254) 6.09 bicubic 260 tf_efficientnet_b0_ap *tfp 77.514 (22.486) 93.576 (6.424) 5.29 bicubic 224 tf_efficientnet_cc_b0_4e 77.304 (22.696) 93.332 (6.668) 13.3 bicubic 224 tf_efficientnet_es 77.264 (22.736) 93.600 (6.400) 5.44 bicubic 224 tf_efficientnet_b0 *tfp 77.258 (22.742) 93.478 (6.522) 5.29 bicubic 224 tf_efficientnet_b0_ap 77.084 (22.916) 93.254 (6.746) 5.29 bicubic 224 tf_mixnet_m *tfp 77.072 (22.928) 93.368 (6.632) 5.01 bilinear 224 tf_mixnet_m 76.950 (23.050) 93.156 (6.844) 5.01 bicubic 224 tf_efficientnet_b0 76.848 (23.152) 93.228 (6.772) 5.29 bicubic 224 tf_efficientnet_lite1 *tfp 76.764 (23.236) 93.326 (6.674) 5.42 bilinear 240 tf_efficientnet_lite1 76.638 (23.362) 93.232 (6.768) 5.42 bicubic 240 tf_mixnet_s *tfp 75.800 (24.200) 92.788 (7.212) 4.13 bilinear 224 tf_mobilenetv3_large_100 *tfp 75.768 (24.232) 92.710 (7.290) 5.48 bilinear 224 tf_mixnet_s 75.648 (24.352) 92.636 (7.364) 4.13 bicubic 224 tf_mobilenetv3_large_100 75.516 (24.484) 92.600 (7.400) 5.48 bilinear 224 tf_efficientnet_lite0 *tfp 75.074 (24.926) 92.314 (7.686) 4.65 bilinear 224 tf_efficientnet_lite0 74.842 (25.158) 92.170 (7.830) 4.65 bicubic 224 gluon_resnet34_v1b 74.580 (25.420) 91.988 (8.012) 21.80 bicubic 224 tf_mobilenetv3_large_075 *tfp 73.730 (26.270) 91.616 (8.384) 3.99 bilinear 224 tf_mobilenetv3_large_075 73.442 (26.558) 91.352 (8.648) 3.99 bilinear 224 tf_mobilenetv3_large_minimal_100 *tfp 72.678 (27.322) 90.860 (9.140) 3.92 bilinear 224 tf_mobilenetv3_large_minimal_100 72.244 (27.756) 90.636 (9.364) 3.92 bilinear 224 tf_mobilenetv3_small_100 *tfp 67.918 (32.082) 87.958 (12.042 2.54 bilinear 224 tf_mobilenetv3_small_100 67.918 (32.082) 87.662 (12.338) 2.54 bilinear 224 tf_mobilenetv3_small_075 *tfp 66.142 (33.858) 86.498 (13.502) 2.04 bilinear 224 tf_mobilenetv3_small_075 65.718 (34.282) 86.136 (13.864) 2.04 bilinear 224 tf_mobilenetv3_small_minimal_100 *tfp 63.378 (36.622) 84.802 (15.198) 2.04 bilinear 224 tf_mobilenetv3_small_minimal_100 62.898 (37.102) 84.230 (15.770) 2.04 bilinear 224","title":"Ported Weights"}]} |