Recent Changes

Test with PyTorch 1.7 and fix a small top-n metric view vs reshape issue.
Convert newly added 224x224 Vision Transformer weights from official JAX repo. 81.8 top-1 for B/16, 83.1 L/16.
Support PyTorch 1.7 optimized, native SiLU (aka Swish) activation. Add mapping to 'silu' name, custom swish will eventually be deprecated.
Fix regression for loading pretrained classifier via direct model entrypoint functions. Didn't impact create_model() factory usage.
PyPi release @ 0.3.0 version!

Update Vision Transformer models to be compatible with official code release at https://github.com/google-research/vision_transformer
Add Vision Transformer weights (ImageNet-21k pretrain) for 384x384 base and large models converted from official jax impl
- ViT-B/16 - 84.2
- ViT-B/32 - 81.7
- ViT-L/16 - 85.2
- ViT-L/32 - 81.5

Weights added for Vision Transformer (ViT) models. 77.86 top-1 for 'small' and 79.35 for 'base'. Thanks to Christof for training the base model w/ lots of GPUs.

Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train...
Adafactor and AdaHessian (FP32 only, no AMP) optimizers
EdgeTPU-M (efficientnet_em) model trained in PyTorch, 79.3 top-1
Pip release, doc updates pending a few more changes...

New ResNet 'D' weights. 72.7 (top-1) ResNet-18-D, 77.1 ResNet-34-D, 80.5 ResNet-50-D
Added a few untrained defs for other ResNet models (66D, 101D, 152D, 200/200D)

New weights
- Wide-ResNet50 - 81.5 top-1 (vs 78.5 torchvision)
- SEResNeXt50-32x4d - 81.3 top-1 (vs 79.1 cadene)
Support for native Torch AMP and channels_last memory format added to train/validate scripts (--channels-last, --native-amp vs --apex-amp)
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.

New/updated weights from training experiments
- EfficientNet-B3 - 82.1 top-1 (vs 81.6 for official with AA and 81.9 for AdvProp)
- RegNetY-3.2GF - 82.0 top-1 (78.9 from official ver)
- CSPResNet50 - 79.6 top-1 (76.6 from official ver)
Add CutMix integrated w/ Mixup. See pull request for some usage examples
Some fixes for using pretrained weights with in_chans != 3 on several models.

Universal feature extraction, new models, new weights, new test sets.

All models support the features_only=True argument for create_model call to return a network that extracts features from the deepest layer at each stride.
New models
- CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet
- ReXNet
- (Modified Aligned) Xception41/65/71 (a proper port of TF models)
New trained weights
- SEResNet50 - 80.3 top-1
- CSPDarkNet53 - 80.1 top-1
- CSPResNeXt50 - 80.0 top-1
- DPN68b - 79.2 top-1
- EfficientNet-Lite0 (non-TF ver) - 75.5 (submitted by @hal-314)
Add 'real' labels for ImageNet and ImageNet-Renditions test set, see results/README.md
Test set ranking/top-n diff script by @KushajveerSingh
Train script and loader/transform tweaks to punch through more aug arguments
README and documentation overhaul. See initial (WIP) documentation at https://rwightman.github.io/pytorch-image-models/
adamp and sgdp optimizers added by @hellbell

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

Pruned EfficientNet B1, B2, and B3 (https://arxiv.org/abs/2002.08258) contributed by Yonathan Aflalo

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