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PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
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README.md

PyTorch Image Models, etc

What's New

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

May 3, 2020

May 1, 2020

  • Merged a number of execellent contributions in the ResNet model family over the past month
  • 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

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.

The work of many others is present here. I've tried to make sure all source material is acknowledged:

Models

I've included a few of my favourite models, but this is not an exhaustive collection. You can't do better than Cadene's collection in that regard. Most models do have pretrained weights from their respective sources or original authors.

Included models:

Use the --model arg to specify model for train, validation, inference scripts. Match the all lowercase creation fn for the model you'd like.

Features

Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:

  • All models have a common default configuration interface and API for
    • accessing/changing the classifier - get_classifier and reset_classifier
    • doing a forward pass on just the features - forward_features
    • these makes it easy to write consistent network wrappers that work with any of the models
  • All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired
  • The train script works in several process/GPU modes:
    • NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional)
    • PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
    • PyTorch w/ single GPU single process (AMP optional)
  • A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights.
  • A 'Test Time Pool' wrapper that can wrap any of the included models and usually provide improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
  • Training schedules and techniques that provide competitive results (Cosine LR, Random Erasing, Label Smoothing, etc)
  • Mixup (as in https://arxiv.org/abs/1710.09412) - currently implementing/testing
  • An inference script that dumps output to CSV is provided as an example
  • AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py)
  • AugMix w/ JSD loss (https://arxiv.org/abs/1912.02781), JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well
  • SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data
  • DropBlock (https://arxiv.org/abs/1810.12890)
  • Efficient Channel Attention - ECA (https://arxiv.org/abs/1910.03151)

Results

A CSV file containing an ImageNet-1K validation results summary for all included models with pretrained weights and default configurations is located here

Self-trained Weights

I've leveraged the training scripts in this repository to train a few of the models with missing weights to good levels of performance. These numbers are all for 224x224 training and validation image sizing with the usual 87.5% validation crop.

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

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.

Sources for original weights:

Training Hyperparameters

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

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

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:

./distributed_train.sh 2 /imagenet/ --model seresnext26t_32x4d --lr 0.1 --warmup-epochs 5 --epochs 160 --weight-decay 1e-4 --sched cosine --reprob 0.4 --remode pixel -b 112

EfficientNet-B3 with RandAugment - 81.5 top-1, 95.7 top-5

The training of this model started with the same command line as EfficientNet-B2 w/ RA above. After almost three weeks of training the process crashed. The results weren't looking amazing so I resumed the training several times with tweaks to a few params (increase RE prob, decrease rand-aug, increase ema-decay). Nothing looked great. I ended up averaging the best checkpoints from all restarts. The result is mediocre at default res/crop but oddly performs much better with a full image test crop of 1.0.

EfficientNet-B0 with RandAugment - 77.7 top-1, 95.3 top-5

Michael Klachko 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

ResNet50 with JSD loss and RandAugment (clean + 2x RA augs) - 79.04 top-1, 94.39 top-5

Trained on two older 1080Ti cards, this took a while. Only slightly, non statistically better ImageNet validation result than my first good AugMix training of 78.99. However, these weights are more robust on tests with ImageNetV2, ImageNet-Sketch, etc. Unlike my first AugMix runs, I've enabled SplitBatchNorm, disabled random erasing on the clean split, and cranked up random erasing prob on the 2 augmented paths.

./distributed_train.sh 2 /imagenet -b 64 --model resnet50 --sched cosine --epochs 200 --lr 0.05 --amp --remode pixel --reprob 0.6 --aug-splits 3 --aa rand-m9-mstd0.5-inc1 --resplit --split-bn --jsd --dist-bn reduce

EfficientNet-ES (EdgeTPU-Small) with RandAugment - 78.066 top-1, 93.926 top-5

Trained by Andrew Lavin 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

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

ResNeXt-50 32x4d w/ RandAugment - 79.762 top-1, 94.60 top-5

These params will also work well for SE-ResNeXt-50 and SK-ResNeXt-50 and likely 101. I used them for the SK-ResNeXt-50 32x4d that I trained with 2 GPU using a slightly higher LR per effective batch size (lr=0.18, b=192 per GPU). The cmd line below are tuned for 8 GPU training.

./distributed_train.sh 8 /imagenet --model resnext50_32x4d --lr 0.6 --warmup-epochs 5 --epochs 240 --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 192 -j 6 --amp --dist-bn reduce

TODO dig up some more

Usage

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. Little to no care has been taken to be Python 2.x friendly and I don't plan to support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment.

PyTorch versions 1.2, 1.3.1, and 1.4 have been tested with this code.

I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda:

conda create -n torch-env
conda activate torch-env
conda install -c pytorch pytorch torchvision cudatoolkit=10.1
conda install pyyaml

Pip

This package can be installed via pip. Currently, the model factory (timm.create_model) is the most useful component to use via a pip install.

Install (after conda env/install):

pip install timm

Use:

>>> import timm
>>> m = timm.create_model('mobilenetv3_100', pretrained=True)
>>> m.eval()

Scripts

A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release.

Training

The variety of training args is large and not all combinations of options (or even options) have been fully tested. For the training dataset folder, specify the folder to the base that contains a train and validation folder.

To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value:

./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 -j 4

NOTE: NVIDIA APEX should be installed to run in per-process distributed via DDP or to enable AMP mixed precision with the --amp flag

Validation / Inference

Validation and inference scripts are similar in usage. One outputs metrics on a validation set and the other outputs topk class ids in a csv. Specify the folder containing validation images, not the base as in training script.

To validate with the model's pretrained weights (if they exist):

python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained

To run inference from a checkpoint:

python inference.py /imagenet/validation/ --model mobilenetv3_100 --checkpoint ./output/model_best.pth.tar

TODO

A number of additions planned in the future for various projects, incl

  • Do a model performance (speed + accuracy) benchmarking across all models (make runable as script)
  • Complete feature map extraction across all model types and build obj detection/segmentation models and scripts (or integrate backbones with mmdetection, detectron2)