pytorch-image-models/docs/changes.md

Recent Changes

Feb 10, 2021

• More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')
  • GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl in byobnet.py
  • RepVGG (https://github.com/DingXiaoH/RepVGG), impl in byobnet.py
  • classic VGG (from torchvision, impl in vgg)
• Refinements to normalizer layer arg handling and normalizer+act layer handling in some models
• Default AMP mode changed to native PyTorch AMP instead of APEX. Issues not being fixed with APEX. Native works with --channels-last and --torchscript model training, APEX does not.
• Fix a few bugs introduced since last pypi release

Feb 8, 2021

• Add several ResNet weights with ECA attention. 26t & 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352.
  • ecaresnet26t - 79.88 top-1 @ 320x320, 79.08 @ 256x256
  • ecaresnet50t - 82.35 top-1 @ 320x320, 81.52 @ 256x256
  • ecaresnet269d - 84.93 top-1 @ 352x352, 84.87 @ 320x320
• Remove separate tiered (t) vs tiered_narrow (tn) ResNet model defs, all tn changed to t and t models removed (seresnext26t_32x4d only model w/ weights that was removed).
• Support model default_cfgs with separate train vs test resolution test_input_size and remove extra _320 suffix ResNet model defs that were just for test.

Jan 30, 2021

• Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based on paper

Jan 25, 2021

• Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights from https://github.com/google-research/big_transfer
• Add official R50+ViT-B/16 hybrid models + weights from https://github.com/google-research/vision_transformer
• ImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) support
  • NOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learning
• Add model defs and weights for DeiT Vision Transformer models from https://github.com/facebookresearch/deit
• Refactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classes
• Add Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train script
  • Ex: 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
• Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tar
  • Run validation on full ImageNet-21k directly from tar w/ BiT model: validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --amp
• Models in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handling

Jan 3, 2021

• Add SE-ResNet-152D weights
  • 256x256 val, 0.94 crop top-1 - 83.75
  • 320x320 val, 1.0 crop - 84.36
• Update results files

Dec 18, 2020

• Add ResNet-101D, ResNet-152D, and ResNet-200D weights trained @ 256x256
  • 256x256 val, 0.94 crop (top-1) - 101D (82.33), 152D (83.08), 200D (83.25)
  • 288x288 val, 1.0 crop - 101D (82.64), 152D (83.48), 200D (83.76)
  • 320x320 val, 1.0 crop - 101D (83.00), 152D (83.66), 200D (84.01)

Dec 7, 2020

• Simplify EMA module (ModelEmaV2), compatible with fully torchscripted models
• Misc fixes for SiLU ONNX export, default_cfg missing from Feature extraction models, Linear layer w/ AMP + torchscript
• PyPi release @ 0.3.2 (needed by EfficientDet)

Oct 30, 2020

• 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!

Oct 26, 2020

• 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

Oct 21, 2020

• 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.

Oct 13, 2020

• 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...

Sept 18, 2020

• 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)

Sept 3, 2020

• 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.

Aug 12, 2020

• 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.

Aug 5, 2020

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

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

• Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt, paper https://arxiv.org/abs/2004.08955))

May 3, 2020

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

May 1, 2020

• 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