Recent 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
- 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
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)