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< h1 id = "recent-changes" > Recent Changes< / h1 >
< 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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