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Adversarial Inception v3
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AdvProp (EfficientNet)
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Big Transfer (BiT)
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CSP-DarkNet
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CSP-ResNet
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CSP-ResNeXt
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DenseNet
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Deep Layer Aggregation
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Dual Path Network (DPN)
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ECA-ResNet
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EfficientNet (Knapsack Pruned)
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EfficientNet
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Ensemble Adversarial Inception ResNet v2
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ESE-VoVNet
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FBNet
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(Gluon) Inception v3
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<a href="../models/gloun-resnet/" class="md-nav__link">
(Gluon) ResNet
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(Gluon) ResNeXt
</a>
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(Gluon) SENet
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<a href="../models/gloun-seresnext/" class="md-nav__link">
(Gluon) SE-ResNeXt
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(Gluon) Xception
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HRNet
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Instagram ResNeXt WSL
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<a href="../models/inception-resnet-v2/" class="md-nav__link">
Inception ResNet v2
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Inception v3
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Inception v4
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(Legacy) SE-ResNet
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(Legacy) SE-ResNeXt
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(Legacy) SENet
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MixNet
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MnasNet
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MobileNet v2
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MobileNet v3
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NASNet
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Noisy Student (EfficientNet)
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PNASNet
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RegNetX
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RegNetY
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Res2Net
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Res2NeXt
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ResNeSt
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ResNet-D
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ResNet
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ResNeXt
</a>
</li>
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RexNet
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SE-ResNet
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<a href="../models/selecsls/" class="md-nav__link">
SelecSLS
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SE-ResNeXt
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SK-ResNet
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SK-ResNeXt
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SPNASNet
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SSL ResNet
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SSL ResNeXT
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<a href="../models/swsl-resnet/" class="md-nav__link">
SWSL ResNet
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SWSL ResNeXt
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</li>
<li class="md-nav__item">
<a href="../models/tf-efficientnet-condconv/" class="md-nav__link">
(Tensorflow) EfficientNet CondConv
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<a href="../models/tf-efficientnet-lite/" class="md-nav__link">
(Tensorflow) EfficientNet Lite
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<a href="../models/tf-efficientnet/" class="md-nav__link">
(Tensorflow) EfficientNet
</a>
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<li class="md-nav__item">
<a href="../models/tf-inception-v3/" class="md-nav__link">
(Tensorflow) Inception v3
</a>
</li>
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<a href="../models/tf-mixnet/" class="md-nav__link">
(Tensorflow) MixNet
</a>
</li>
<li class="md-nav__item">
<a href="../models/tf-mobilenet-v3/" class="md-nav__link">
(Tensorflow) MobileNet v3
</a>
</li>
<li class="md-nav__item">
<a href="../models/tresnet/" class="md-nav__link">
TResNet
</a>
</li>
<li class="md-nav__item">
<a href="../models/vision-transformer/" class="md-nav__link">
Vision Transformer (ViT)
</a>
</li>
<li class="md-nav__item">
<a href="../models/wide-resnet/" class="md-nav__link">
Wide ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../models/xception/" class="md-nav__link">
Xception
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Results
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Scripts
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Training Examples
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Feature Extraction
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Recent Changes
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<a href="#march-7-2021" class="md-nav__link">
March 7, 2021
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Feb 18, 2021
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Feb 16, 2021
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Feb 12, 2021
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Feb 10, 2021
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Feb 8, 2021
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Jan 30, 2021
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Jan 25, 2021
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Jan 3, 2021
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Dec 18, 2020
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Dec 7, 2020
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Oct 30, 2020
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Oct 26, 2020
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Oct 21, 2020
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Oct 13, 2020
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Sept 18, 2020
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Sept 3, 2020
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Aug 12, 2020
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Aug 5, 2020
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June 11, 2020
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<h1 id="recent-changes">Recent Changes</h1>
<h3 id="march-7-2021">March 7, 2021</h3>
<ul>
<li>First 0.4.x PyPi release w/ NFNets (&amp; related), ByoB (GPU-Efficient, RepVGG, etc).</li>
<li>Change feature extraction for pre-activation nets (NFNets, ResNetV2) to return features before activation.</li>
</ul>
<h3 id="feb-18-2021">Feb 18, 2021</h3>
<ul>
<li>Add pretrained weights and model variants for NFNet-F* models from <a href="https://github.com/deepmind/deepmind-research/tree/master/nfnets">DeepMind Haiku impl</a>.<ul>
<li>Models are prefixed with <code>dm_</code>. They require SAME padding conv, skipinit enabled, and activation gains applied in act fn.</li>
<li>These models are big, expect to run out of GPU memory. With the GELU activiation + other options, they are roughly &frac12; the inference speed of my SiLU PyTorch optimized <code>s</code> variants.</li>
<li>Original model results are based on pre-processing that is not the same as all other models so you'll see different results in the results csv (once updated).</li>
<li>Matching the original pre-processing as closely as possible I get these results:<ul>
<li><code>dm_nfnet_f6</code> - 86.352</li>
<li><code>dm_nfnet_f5</code> - 86.100</li>
<li><code>dm_nfnet_f4</code> - 85.834</li>
<li><code>dm_nfnet_f3</code> - 85.676</li>
<li><code>dm_nfnet_f2</code> - 85.178</li>
<li><code>dm_nfnet_f1</code> - 84.696</li>
<li><code>dm_nfnet_f0</code> - 83.464</li>
</ul>
</li>
</ul>
</li>
</ul>
<h3 id="feb-16-2021">Feb 16, 2021</h3>
<ul>
<li>Add Adaptive Gradient Clipping (AGC) as per <a href="https://arxiv.org/abs/2102.06171">https://arxiv.org/abs/2102.06171</a>. Integrated w/ PyTorch gradient clipping via mode arg that defaults to prev 'norm' mode. For backward arg compat, clip-grad arg must be specified to enable when using train.py.<ul>
<li>AGC w/ default clipping factor <code>--clip-grad .01 --clip-mode agc</code></li>
<li>PyTorch global norm of 1.0 (old behaviour, always norm), <code>--clip-grad 1.0</code></li>
<li>PyTorch value clipping of 10, <code>--clip-grad 10. --clip-mode value</code></li>
<li>AGC performance is definitely sensitive to the clipping factor. More experimentation needed to determine good values for smaller batch sizes and optimizers besides those in paper. So far I've found .001-.005 is necessary for stable RMSProp training w/ NFNet/NF-ResNet.</li>
</ul>
</li>
</ul>
<h3 id="feb-12-2021">Feb 12, 2021</h3>
<ul>
<li>Update Normalization-Free nets to include new NFNet-F (<a href="https://arxiv.org/abs/2102.06171">https://arxiv.org/abs/2102.06171</a>) model defs</li>
</ul>
<h3 id="feb-10-2021">Feb 10, 2021</h3>
<ul>
<li>More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')<ul>
<li>GPU-Efficient-Networks (<a href="https://github.com/idstcv/GPU-Efficient-Networks">https://github.com/idstcv/GPU-Efficient-Networks</a>), impl in <code>byobnet.py</code></li>
<li>RepVGG (<a href="https://github.com/DingXiaoH/RepVGG">https://github.com/DingXiaoH/RepVGG</a>), impl in <code>byobnet.py</code></li>
<li>classic VGG (from torchvision, impl in <code>vgg</code>)</li>
</ul>
</li>
<li>Refinements to normalizer layer arg handling and normalizer+act layer handling in some models</li>
<li>Default AMP mode changed to native PyTorch AMP instead of APEX. Issues not being fixed with APEX. Native works with <code>--channels-last</code> and <code>--torchscript</code> model training, APEX does not.</li>
<li>Fix a few bugs introduced since last pypi release</li>
</ul>
<h3 id="feb-8-2021">Feb 8, 2021</h3>
<ul>
<li>Add several ResNet weights with ECA attention. 26t &amp; 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352.<ul>
<li><code>ecaresnet26t</code> - 79.88 top-1 @ 320x320, 79.08 @ 256x256</li>
<li><code>ecaresnet50t</code> - 82.35 top-1 @ 320x320, 81.52 @ 256x256</li>
<li><code>ecaresnet269d</code> - 84.93 top-1 @ 352x352, 84.87 @ 320x320</li>
</ul>
</li>
<li>Remove separate tiered (<code>t</code>) vs tiered_narrow (<code>tn</code>) ResNet model defs, all <code>tn</code> changed to <code>t</code> and <code>t</code> models removed (<code>seresnext26t_32x4d</code> only model w/ weights that was removed).</li>
<li>Support model default_cfgs with separate train vs test resolution <code>test_input_size</code> and remove extra <code>_320</code> suffix ResNet model defs that were just for test.</li>
</ul>
<h3 id="jan-30-2021">Jan 30, 2021</h3>
<ul>
<li>Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based on <a href="https://arxiv.org/abs/2101.08692">paper</a></li>
</ul>
<h3 id="jan-25-2021">Jan 25, 2021</h3>
<ul>
<li>Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights from <a href="https://github.com/google-research/big_transfer">https://github.com/google-research/big_transfer</a></li>
<li>Add official R50+ViT-B/16 hybrid models + weights from <a href="https://github.com/google-research/vision_transformer">https://github.com/google-research/vision_transformer</a></li>
<li>ImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) support<ul>
<li>NOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learning</li>
</ul>
</li>
<li>Add model defs and weights for DeiT Vision Transformer models from <a href="https://github.com/facebookresearch/deit">https://github.com/facebookresearch/deit</a></li>
<li>Refactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classes</li>
<li>Add Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train script<ul>
<li>Ex: <code>train.py /data/tfds --dataset tfds/oxford_iiit_pet --val-split test --model resnet50 -b 256 --amp --num-classes 37 --opt adamw --lr 3e-4 --weight-decay .001 --pretrained -j 2</code></li>
</ul>
</li>
<li>Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tar<ul>
<li>Run validation on full ImageNet-21k directly from tar w/ BiT model: <code>validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --amp</code></li>
</ul>
</li>
<li>Models in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handling</li>
</ul>
<h3 id="jan-3-2021">Jan 3, 2021</h3>
<ul>
<li>Add SE-ResNet-152D weights<ul>
<li>256x256 val, 0.94 crop top-1 - 83.75</li>
<li>320x320 val, 1.0 crop - 84.36</li>
</ul>
</li>
<li>Update results files</li>
</ul>
<h3 id="dec-18-2020">Dec 18, 2020</h3>
<ul>
<li>Add ResNet-101D, ResNet-152D, and ResNet-200D weights trained @ 256x256<ul>
<li>256x256 val, 0.94 crop (top-1) - 101D (82.33), 152D (83.08), 200D (83.25)</li>
<li>288x288 val, 1.0 crop - 101D (82.64), 152D (83.48), 200D (83.76)</li>
<li>320x320 val, 1.0 crop - 101D (83.00), 152D (83.66), 200D (84.01)</li>
</ul>
</li>
</ul>
<h3 id="dec-7-2020">Dec 7, 2020</h3>
<ul>
<li>Simplify EMA module (ModelEmaV2), compatible with fully torchscripted models</li>
<li>Misc fixes for SiLU ONNX export, default_cfg missing from Feature extraction models, Linear layer w/ AMP + torchscript</li>
<li>PyPi release @ 0.3.2 (needed by EfficientDet)</li>
</ul>
<h3 id="oct-30-2020">Oct 30, 2020</h3>
<ul>
<li>Test with PyTorch 1.7 and fix a small top-n metric view vs reshape issue.</li>
<li>Convert newly added 224x224 Vision Transformer weights from official JAX repo. 81.8 top-1 for B/16, 83.1 L/16.</li>
<li>Support PyTorch 1.7 optimized, native SiLU (aka Swish) activation. Add mapping to 'silu' name, custom swish will eventually be deprecated.</li>
<li>Fix regression for loading pretrained classifier via direct model entrypoint functions. Didn't impact create_model() factory usage.</li>
<li>PyPi release @ 0.3.0 version!</li>
</ul>
<h3 id="oct-26-2020">Oct 26, 2020</h3>
<ul>
<li>Update Vision Transformer models to be compatible with official code release at <a href="https://github.com/google-research/vision_transformer">https://github.com/google-research/vision_transformer</a></li>
<li>Add Vision Transformer weights (ImageNet-21k pretrain) for 384x384 base and large models converted from official jax impl<ul>
<li>ViT-B/16 - 84.2</li>
<li>ViT-B/32 - 81.7</li>
<li>ViT-L/16 - 85.2</li>
<li>ViT-L/32 - 81.5</li>
</ul>
</li>
</ul>
<h3 id="oct-21-2020">Oct 21, 2020</h3>
<ul>
<li>Weights added for Vision Transformer (ViT) models. 77.86 top-1 for 'small' and 79.35 for 'base'. Thanks to <a href="https://www.kaggle.com/christofhenkel">Christof</a> for training the base model w/ lots of GPUs.</li>
</ul>
<h3 id="oct-13-2020">Oct 13, 2020</h3>
<ul>
<li>Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train...</li>
<li>Adafactor and AdaHessian (FP32 only, no AMP) optimizers</li>
<li>EdgeTPU-M (<code>efficientnet_em</code>) model trained in PyTorch, 79.3 top-1</li>
<li>Pip release, doc updates pending a few more changes...</li>
</ul>
<h3 id="sept-18-2020">Sept 18, 2020</h3>
<ul>
<li>New ResNet 'D' weights. 72.7 (top-1) ResNet-18-D, 77.1 ResNet-34-D, 80.5 ResNet-50-D</li>
<li>Added a few untrained defs for other ResNet models (66D, 101D, 152D, 200/200D)</li>
</ul>
<h3 id="sept-3-2020">Sept 3, 2020</h3>
<ul>
<li>New weights<ul>
<li>Wide-ResNet50 - 81.5 top-1 (vs 78.5 torchvision)</li>
<li>SEResNeXt50-32x4d - 81.3 top-1 (vs 79.1 cadene)</li>
</ul>
</li>
<li>Support for native Torch AMP and channels_last memory format added to train/validate scripts (<code>--channels-last</code>, <code>--native-amp</code> vs <code>--apex-amp</code>)</li>
<li>Models tested with channels_last on latest NGC 20.08 container. AdaptiveAvgPool in attn layers changed to mean((2,3)) to work around bug with NHWC kernel.</li>
</ul>
<h3 id="aug-12-2020">Aug 12, 2020</h3>
<ul>
<li>New/updated weights from training experiments<ul>
<li>EfficientNet-B3 - 82.1 top-1 (vs 81.6 for official with AA and 81.9 for AdvProp)</li>
<li>RegNetY-3.2GF - 82.0 top-1 (78.9 from official ver)</li>
<li>CSPResNet50 - 79.6 top-1 (76.6 from official ver)</li>
</ul>
</li>
<li>Add CutMix integrated w/ Mixup. See <a href="https://github.com/rwightman/pytorch-image-models/pull/218">pull request</a> for some usage examples</li>
<li>Some fixes for using pretrained weights with <code>in_chans</code> != 3 on several models.</li>
</ul>
<h3 id="aug-5-2020">Aug 5, 2020</h3>
<p>Universal feature extraction, new models, new weights, new test sets.</p>
<ul>
<li>All models support the <code>features_only=True</code> argument for <code>create_model</code> call to return a network that extracts features from the deepest layer at each stride.</li>
<li>New models<ul>
<li>CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet</li>
<li>ReXNet</li>
<li>(Modified Aligned) Xception41/65/71 (a proper port of TF models)</li>
</ul>
</li>
<li>New trained weights<ul>
<li>SEResNet50 - 80.3 top-1</li>
<li>CSPDarkNet53 - 80.1 top-1</li>
<li>CSPResNeXt50 - 80.0 top-1</li>
<li>DPN68b - 79.2 top-1</li>
<li>EfficientNet-Lite0 (non-TF ver) - 75.5 (submitted by <a href="https://github.com/hal-314">@hal-314</a>)</li>
</ul>
</li>
<li>Add 'real' labels for ImageNet and ImageNet-Renditions test set, see <a href="results/README.md"><code>results/README.md</code></a></li>
<li>Test set ranking/top-n diff script by <a href="https://github.com/KushajveerSingh">@KushajveerSingh</a></li>
<li>Train script and loader/transform tweaks to punch through more aug arguments</li>
<li>README and documentation overhaul. See initial (WIP) documentation at <a href="https://rwightman.github.io/pytorch-image-models/">https://rwightman.github.io/pytorch-image-models/</a></li>
<li>adamp and sgdp optimizers added by <a href="https://github.com/hellbell">@hellbell</a></li>
</ul>
<h3 id="june-11-2020">June 11, 2020</h3>
<p>Bunch of changes:</p>
<ul>
<li>DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions</li>
<li>VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1</li>
<li>Activation factory added along with new activations:<ul>
<li>select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export)</li>
<li>hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish</li>
<li>context mgr for setting exportable/scriptable/no_jit states</li>
</ul>
</li>
<li>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</li>
<li>Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this</li>
<li>Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call</li>
<li>Prep for 0.1.28 pip release</li>
</ul>
<h3 id="may-12-2020">May 12, 2020</h3>
<ul>
<li>Add ResNeSt models (code adapted from <a href="https://github.com/zhanghang1989/ResNeSt">https://github.com/zhanghang1989/ResNeSt</a>, paper <a href="https://arxiv.org/abs/2004.08955">https://arxiv.org/abs/2004.08955</a>))</li>
</ul>
<h3 id="may-3-2020">May 3, 2020</h3>
<ul>
<li>Pruned EfficientNet B1, B2, and B3 (<a href="https://arxiv.org/abs/2002.08258">https://arxiv.org/abs/2002.08258</a>) contributed by <a href="https://github.com/yoniaflalo">Yonathan Aflalo</a></li>
</ul>
<h3 id="may-1-2020">May 1, 2020</h3>
<ul>
<li>Merged a number of execellent contributions in the ResNet model family over the past month<ul>
<li>BlurPool2D and resnetblur models initiated by <a href="https://github.com/VRandme">Chris Ha</a>, I trained resnetblur50 to 79.3.</li>
<li>TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by <a href="https://github.com/mrT23">mrT23</a></li>
<li>ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (<a href="https://arxiv.org/abs/2002.08258">https://arxiv.org/abs/2002.08258</a>) by <a href="https://github.com/yoniaflalo">Yonathan Aflalo</a></li>
</ul>
</li>
<li>200 pretrained models in total now with updated results csv in results folder</li>
</ul>
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