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pytorch-image-models/docs/changes.md

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Changes

Aug 1, 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
    • (Aligned) Xception41/65/71 (a proper port of TF models)
  • New trained weights
    • SEResNet50 - 80.3
    • CSPDarkNet53 - 80.1 top-1
    • CSPResNeXt50 - 80.0 to-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

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)