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# PyTorch Image Models, etc # PyTorch Image Models, etc
## What's New
### Nov 29, 2019
* Brought EfficientNet and MobileNetV3 up to date with my https://github.com/rwightman/gen-efficientnet-pytorch code. Torchscript and ONNX export compat excluded.
* AdvProp weights added
* Official TF MobileNetv3 weights added
* EfficientNet and MobileNetV3 hook based 'feature extraction' classes added. Will serve as basis for using models as backbones in obj detection/segmentation tasks. Lots more to be done here...
* HRNet classification models and weights added from https://github.com/HRNet/HRNet-Image-Classification
* Consistency in global pooling, `reset_classifer`, and `forward_features` across models
* `forward_features` always returns unpooled feature maps now
* Reasonable chance I broke something... let me know
### Nov 22, 2019
* Add ImageNet training RandAugment implementation alongside AutoAugment. PyTorch Transform compatible format, using PIL. Currently training two EfficientNet models from scratch with promising results... will update.
* `drop-connect` cmd line arg finally added to `train.py`, no need to hack model fns. Works for efficientnet/mobilenetv3 based models, ignored otherwise.
## Introduction ## Introduction
For each competition, personal, or freelance project involving images + Convolution Neural Networks, I build on top of an evolving collection of code and models. This repo contains a (somewhat) cleaned up and paired down iteration of that code. Hopefully it'll be of use to others. For each competition, personal, or freelance project involving images + Convolution Neural Networks, I build on top of an evolving collection of code and models. This repo contains a (somewhat) cleaned up and paired down iteration of that code. Hopefully it'll be of use to others.
@ -34,21 +50,24 @@ I've included a few of my favourite models, but this is not an exhaustive collec
* Squeeze-and-Excitation ResNet/ResNeXt (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) with some pretrained weight additions by myself) * Squeeze-and-Excitation ResNet/ResNeXt (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) 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) * 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-ResNet-V2 and Inception-V4 (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) ) * Inception-ResNet-V2 and Inception-V4 (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) )
* Xception (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch)) * Xception
* Original variant from [Cadene](https://github.com/Cadene/pretrained-models.pytorch)
* MXNet Gluon 'modified aligned' Xception-65 and 71 models from [Gluon ModelZoo](https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo)
* PNasNet & NASNet-A (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch)) * PNasNet & NASNet-A (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch))
* DPN (from [me](https://github.com/rwightman/pytorch-dpn-pretrained), weights hosted by Cadene) * DPN (from [me](https://github.com/rwightman/pytorch-dpn-pretrained), weights hosted by Cadene)
* DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107 * DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107
* Generic EfficientNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks * EfficientNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks
* EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- validated, compat with TF weights * EfficientNet AdvProp (B0-B8) (https://arxiv.org/abs/1911.09665) -- TF weights ported
* EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --validated w/ TF weights * EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- TF weights ported, B0-B2 finetuned PyTorch
* MixNet (https://arxiv.org/abs/1907.09595) -- validated, compat with TF weights * EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --TF weights ported
* MNASNet B1, A1 (Squeeze-Excite), and Small (https://arxiv.org/abs/1807.11626) * MixNet (https://arxiv.org/abs/1907.09595) -- TF weights ported, PyTorch finetuned (S, M, L) or trained models (XL)
* MobileNet-V1 (https://arxiv.org/abs/1704.04861) * MNASNet B1, A1 (Squeeze-Excite), and Small (https://arxiv.org/abs/1807.11626) -- trained in PyTorch
* MobileNet-V2 (https://arxiv.org/abs/1801.04381) * MobileNet-V2 (https://arxiv.org/abs/1801.04381)
* MobileNet-V3 (https://arxiv.org/abs/1905.02244) -- pretrained model good, still no official impl to verify against * FBNet-C (https://arxiv.org/abs/1812.03443) -- trained in PyTorch
* ChamNet (https://arxiv.org/abs/1812.08934) -- specific arch details hard to find, currently an educated guess
* FBNet-C (https://arxiv.org/abs/1812.03443) -- TODO A/B variants
* Single-Path NAS (https://arxiv.org/abs/1904.02877) -- pixel1 variant * Single-Path NAS (https://arxiv.org/abs/1904.02877) -- pixel1 variant
* MobileNet-V3 (https://arxiv.org/abs/1905.02244) -- pretrained PyTorch model, official TF weights ported
* HRNet
* code from https://github.com/HRNet/HRNet-Image-Classification, paper https://arxiv.org/abs/1908.07919
Use the `--model` arg to specify model for train, validation, inference scripts. Match the all lowercase Use the `--model` arg to specify model for train, validation, inference scripts. Match the all lowercase
creation fn for the model you'd like. creation fn for the model you'd like.
@ -103,67 +122,110 @@ I've leveraged the training scripts in this repository to train a few of the mod
| seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic | 224 | | seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic | 224 |
### Ported Weights ### 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 | Source | | Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size |
|---|---|---|---|---|---|---| |---|---|---|---|---|---|
| tf_efficientnet_b7 *tfp | 84.940 (15.060) | 97.214 (2.786) | 66.35 | bicubic | 600 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b8_ap *tfp | 85.436 (14.564) | 97.272 (2.728) | 87.4 | bicubic | 672 |
| tf_efficientnet_b7 | 84.932 (15.068) | 97.208 (2.792) | 66.35 | bicubic | 600 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b8_ap | 85.368 (14.632) | 97.294 (2.706) | 87.4 | bicubic | 672 |
| tf_efficientnet_b6 *tfp | 84.140 (15.860) | 96.852 (3.148) | 43.04 | bicubic | 528 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b7_ap *tfp | 85.154 (14.846) | 97.244 (2.756) | 66.35 | bicubic | 600 |
| tf_efficientnet_b6 | 84.110 (15.890) | 96.886 (3.114) | 43.04 | bicubic | 528 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b7_ap | 85.118 (14.882) | 97.252 (2.748) | 66.35 | bicubic | 600 |
| tf_efficientnet_b5 *tfp | 83.822 (16.178) | 96.756 (3.244) | 30.39 | bicubic | 456 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b7 *tfp | 84.940 (15.060) | 97.214 (2.786) | 66.35 | bicubic | 600 |
| tf_efficientnet_b5 | 83.812 (16.188) | 96.748 (3.252) | 30.39 | bicubic | 456 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b7 | 84.932 (15.068) | 97.208 (2.792) | 66.35 | bicubic | 600 |
| tf_efficientnet_b4 | 83.022 (16.978) | 96.300 (3.700) | 19.34 | bicubic | 380 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b6_ap | 84.786 (15.214) | 97.138 (2.862) | 43.04 | bicubic | 528 |
| tf_efficientnet_b4 *tfp | 82.948 (17.052) | 96.308 (3.692) | 19.34 | bicubic | 380 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b6_ap *tfp | 84.760 (15.240) | 97.124 (2.876) | 43.04 | bicubic | 528 |
| tf_efficientnet_b3 *tfp | 81.576 (18.424) | 95.662 (4.338) | 12.23 | bicubic | 300 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b5_ap *tfp | 84.276 (15.724) | 96.932 (3.068) | 30.39 | bicubic | 456 |
| tf_efficientnet_b3 | 81.636 (18.364) | 95.718 (4.282) | 12.23 | bicubic | 300 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b5_ap | 84.254 (15.746) | 96.976 (3.024) | 30.39 | bicubic | 456 |
| gluon_senet154 | 81.224 (18.776) | 95.356 (4.644) | 115.09 | bicubic | 224 | | | tf_efficientnet_b6 *tfp | 84.140 (15.860) | 96.852 (3.148) | 43.04 | bicubic | 528 |
| gluon_resnet152_v1s | 81.012 (18.988) | 95.416 (4.584) | 60.32 | bicubic | 224 | | | tf_efficientnet_b6 | 84.110 (15.890) | 96.886 (3.114) | 43.04 | bicubic | 528 |
| gluon_seresnext101_32x4d | 80.902 (19.098) | 95.294 (4.706) | 48.96 | bicubic | 224 | | | tf_efficientnet_b5 *tfp | 83.822 (16.178) | 96.756 (3.244) | 30.39 | bicubic | 456 |
| gluon_seresnext101_64x4d | 80.890 (19.110) | 95.304 (4.696) | 88.23 | bicubic | 224 | | | tf_efficientnet_b5 | 83.812 (16.188) | 96.748 (3.252) | 30.39 | bicubic | 456 |
| gluon_resnext101_64x4d | 80.602 (19.398) | 94.994 (5.006) | 83.46 | bicubic | 224 | | | tf_efficientnet_b4_ap *tfp | 83.278 (16.722) | 96.376 (3.624) | 19.34 | bicubic | 380 |
| tf_efficientnet_el | 80.534 (19.466) | 95.190 (4.810) | 10.59 | bicubic | 300 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) | | tf_efficientnet_b4_ap | 83.248 (16.752) | 96.388 (3.612) | 19.34 | bicubic | 380 |
| tf_efficientnet_el *tfp | 80.476 (19.524) | 95.200 (4.800) | 10.59 | bicubic | 300 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) | | tf_efficientnet_b4 | 83.022 (16.978) | 96.300 (3.700) | 19.34 | bicubic | 380 |
| gluon_resnet152_v1d | 80.470 (19.530) | 95.206 (4.794) | 60.21 | bicubic | 224 | | | tf_efficientnet_b4 *tfp | 82.948 (17.052) | 96.308 (3.692) | 19.34 | bicubic | 380 |
| gluon_resnet101_v1d | 80.424 (19.576) | 95.020 (4.980) | 44.57 | bicubic | 224 | | | tf_efficientnet_b3_ap *tfp | 81.882 (18.118) | 95.662 (4.338) | 12.23 | bicubic | 300 |
| gluon_resnext101_32x4d | 80.334 (19.666) | 94.926 (5.074) | 44.18 | bicubic | 224 | | | tf_efficientnet_b3_ap | 81.828 (18.172) | 95.624 (4.376) | 12.23 | bicubic | 300 |
| gluon_resnet101_v1s | 80.300 (19.700) | 95.150 (4.850) | 44.67 | bicubic | 224 | | | tf_efficientnet_b3 | 81.636 (18.364) | 95.718 (4.282) | 12.23 | bicubic | 300 |
| tf_efficientnet_b2 *tfp | 80.188 (19.812) | 94.974 (5.026) | 9.11 | bicubic | 260 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b3 *tfp | 81.576 (18.424) | 95.662 (4.338) | 12.23 | bicubic | 300 |
| tf_efficientnet_b2 | 80.086 (19.914) | 94.908 (5.092) | 9.11 | bicubic | 260 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | gluon_senet154 | 81.224 (18.776) | 95.356 (4.644) | 115.09 | bicubic | 224 |
| gluon_resnet152_v1c | 79.916 (20.084) | 94.842 (5.158) | 60.21 | bicubic | 224 | | | gluon_resnet152_v1s | 81.012 (18.988) | 95.416 (4.584) | 60.32 | bicubic | 224 |
| gluon_seresnext50_32x4d | 79.912 (20.088) | 94.818 (5.182) | 27.56 | bicubic | 224 | | | gluon_seresnext101_32x4d | 80.902 (19.098) | 95.294 (4.706) | 48.96 | bicubic | 224 |
| gluon_resnet152_v1b | 79.692 (20.308) | 94.738 (5.262) | 60.19 | bicubic | 224 | | | gluon_seresnext101_64x4d | 80.890 (19.110) | 95.304 (4.696) | 88.23 | bicubic | 224 |
| gluon_xception65 | 79.604 (20.396) | 94.748 (5.252) | 39.92 | bicubic | 299 | | | gluon_resnext101_64x4d | 80.602 (19.398) | 94.994 (5.006) | 83.46 | bicubic | 224 |
| gluon_resnet101_v1c | 79.544 (20.456) | 94.586 (5.414) | 44.57 | bicubic | 224 | | | tf_efficientnet_el | 80.534 (19.466) | 95.190 (4.810) | 10.59 | bicubic | 300 |
| gluon_resnext50_32x4d | 79.356 (20.644) | 94.424 (5.576) | 25.03 | bicubic | 224 | | | tf_efficientnet_el *tfp | 80.476 (19.524) | 95.200 (4.800) | 10.59 | bicubic | 300 |
| gluon_resnet101_v1b | 79.304 (20.696) | 94.524 (5.476) | 44.55 | bicubic | 224 | | | gluon_resnet152_v1d | 80.470 (19.530) | 95.206 (4.794) | 60.21 | bicubic | 224 |
| tf_efficientnet_b1 *tfp | 79.172 (20.828) | 94.450 (5.550) | 7.79 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | gluon_resnet101_v1d | 80.424 (19.576) | 95.020 (4.980) | 44.57 | bicubic | 224 |
| gluon_resnet50_v1d | 79.074 (20.926) | 94.476 (5.524) | 25.58 | bicubic | 224 | | | tf_efficientnet_b2_ap *tfp | 80.420 (19.580) | 95.040 (4.960) | 9.11 | bicubic | 260 |
| tf_efficientnet_em *tfp | 78.958 (21.042) | 94.458 (5.542) | 6.90 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) | | gluon_resnext101_32x4d | 80.334 (19.666) | 94.926 (5.074) | 44.18 | bicubic | 224 |
| tf_mixnet_l *tfp | 78.846 (21.154) | 94.212 (5.788) | 7.33 | bilinear | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) | | tf_efficientnet_b2_ap | 80.306 (19.694) | 95.028 (4.972) | 9.11 | bicubic | 260 |
| tf_efficientnet_b1 | 78.826 (21.174) | 94.198 (5.802) | 7.79 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | gluon_resnet101_v1s | 80.300 (19.700) | 95.150 (4.850) | 44.67 | bicubic | 224 |
| gluon_inception_v3 | 78.804 (21.196) | 94.380 (5.620) | 27.16M | bicubic | 299 | [MxNet Gluon](https://gluon-cv.mxnet.io/model_zoo/classification.html) | | tf_efficientnet_b2 *tfp | 80.188 (19.812) | 94.974 (5.026) | 9.11 | bicubic | 260 |
| tf_mixnet_l | 78.770 (21.230) | 94.004 (5.996) | 7.33 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) | | tf_efficientnet_b2 | 80.086 (19.914) | 94.908 (5.092) | 9.11 | bicubic | 260 |
| tf_efficientnet_em | 78.742 (21.258) | 94.332 (5.668) | 6.90 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) | | gluon_resnet152_v1c | 79.916 (20.084) | 94.842 (5.158) | 60.21 | bicubic | 224 |
| gluon_resnet50_v1s | 78.712 (21.288) | 94.242 (5.758) | 25.68 | bicubic | 224 | | | gluon_seresnext50_32x4d | 79.912 (20.088) | 94.818 (5.182) | 27.56 | bicubic | 224 |
| gluon_resnet50_v1c | 78.010 (21.990) | 93.988 (6.012) | 25.58 | bicubic | 224 | | | gluon_resnet152_v1b | 79.692 (20.308) | 94.738 (5.262) | 60.19 | bicubic | 224 |
| tf_inception_v3 | 77.856 (22.144) | 93.644 (6.356) | 27.16M | bicubic | 299 | [Tensorflow Slim](https://github.com/tensorflow/models/tree/master/research/slim) | | gluon_xception65 | 79.604 (20.396) | 94.748 (5.252) | 39.92 | bicubic | 299 |
| tf_efficientnet_es *tfp | 77.616 (22.384) | 93.750 (6.250) | 5.44 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) | | gluon_resnet101_v1c | 79.544 (20.456) | 94.586 (5.414) | 44.57 | bicubic | 224 |
| gluon_resnet50_v1b | 77.578 (22.422) | 93.718 (6.282) | 25.56 | bicubic | 224 | | | tf_efficientnet_b1_ap *tfp | 79.532 (20.468) | 94.378 (5.622) | 7.79 | bicubic | 240 |
| adv_inception_v3 | 77.576 (22.424) | 93.724 (6.276) | 27.16M | bicubic | 299 | [Tensorflow Adv models](https://github.com/tensorflow/models/tree/master/research/adv_imagenet_models) | | tf_efficientnet_cc_b1_8e *tfp | 79.464 (20.536)| 94.492 (5.508) | 39.7 | bicubic | 240 |
| tf_efficientnet_es | 77.264 (22.736) | 93.600 (6.400) | 5.44 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) | | gluon_resnext50_32x4d | 79.356 (20.644) | 94.424 (5.576) | 25.03 | bicubic | 224 |
| tf_efficientnet_b0 *tfp | 77.258 (22.742) | 93.478 (6.522) | 5.29 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | gluon_resnet101_v1b | 79.304 (20.696) | 94.524 (5.476) | 44.55 | bicubic | 224 |
| tf_mixnet_m *tfp | 77.072 (22.928) | 93.368 (6.632) | 5.01 | bilinear | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) | | tf_efficientnet_cc_b1_8e | 79.298 (20.702) | 94.364 (5.636) | 39.7 | bicubic | 240 |
| tf_mixnet_m | 76.950 (23.050) | 93.156 (6.844) | 5.01 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) | | tf_efficientnet_b1_ap | 79.278 (20.722) | 94.308 (5.692) | 7.79 | bicubic | 240 |
| tf_efficientnet_b0 | 76.848 (23.152) | 93.228 (6.772) | 5.29 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | | tf_efficientnet_b1 *tfp | 79.172 (20.828) | 94.450 (5.550) | 7.79 | bicubic | 240 |
| tf_mixnet_s *tfp | 75.800 (24.200) | 92.788 (7.212) | 4.13 | bilinear | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) | | gluon_resnet50_v1d | 79.074 (20.926) | 94.476 (5.524) | 25.58 | bicubic | 224 |
| tf_mixnet_s | 75.648 (24.352) | 92.636 (7.364) | 4.13 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) | | tf_efficientnet_em *tfp | 78.958 (21.042) | 94.458 (5.542) | 6.90 | bicubic | 240 |
| gluon_resnet34_v1b | 74.580 (25.420) | 91.988 (8.012) | 21.80 | bicubic | 224 | | | tf_mixnet_l *tfp | 78.846 (21.154) | 94.212 (5.788) | 7.33 | bilinear | 224 |
| gluon_resnet18_v1b | 70.830 (29.170) | 89.756 (10.244) | 11.69 | bicubic | 224 | | | tf_efficientnet_b1 | 78.826 (21.174) | 94.198 (5.802) | 7.79 | bicubic | 240 |
| 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_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_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_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 |
| 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 |
Models with `*tfp` next to them were scored with `--tf-preprocessing` flag. Models with `*tfp` next to them were scored with `--tf-preprocessing` flag.
The `tf_efficientnet`, `tf_mixnet` models require an equivalent for 'SAME' padding as their arch results in asymmetric padding. I've added this in the model creation wrapper, but it does come with a performance penalty. The `tf_efficientnet`, `tf_mixnet` models require an equivalent for 'SAME' padding as their arch results in asymmetric padding. I've added this in the model creation wrapper, but it does come with a performance penalty.
Sources for original weights:
* `tf_efficientnet*`: [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet)
* `tf_efficientnet_e*`: [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu)
* `tf_mixnet*`: [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet)
* `tf_inception*`: [Tensorflow Slim](https://github.com/tensorflow/models/tree/master/research/slim)
* `gluon_*`: [MxNet Gluon](https://gluon-cv.mxnet.io/model_zoo/classification.html)
## Usage ## Usage
### Environment ### Environment

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