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68 lines
4.1 KiB
68 lines
4.1 KiB
# Recent Changes
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### Aug 12, 2020
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* New/updated weights from training experiments
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* EfficientNet-B3 - 82.1 top-1 (vs 81.6 for official with AA and 81.9 for AdvProp)
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* RegNetY-3.2GF - 82.0 top-1 (78.9 from official ver)
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* CSPResNet50 - 79.6 top-1 (76.6 from official ver)
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* Add CutMix integrated w/ Mixup. See [pull request](https://github.com/rwightman/pytorch-image-models/pull/218) for some usage examples
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* Some fixes for using pretrained weights with `in_chans` != 3 on several models.
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### Aug 5, 2020
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Universal feature extraction, new models, new weights, new test sets.
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* 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.
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* New models
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* CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet
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* ReXNet
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* (Modified Aligned) Xception41/65/71 (a proper port of TF models)
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* New trained weights
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* SEResNet50 - 80.3 top-1
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* CSPDarkNet53 - 80.1 top-1
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* CSPResNeXt50 - 80.0 top-1
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* DPN68b - 79.2 top-1
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* EfficientNet-Lite0 (non-TF ver) - 75.5 (submitted by [@hal-314](https://github.com/hal-314))
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* Add 'real' labels for ImageNet and ImageNet-Renditions test set, see [`results/README.md`](results/README.md)
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* Test set ranking/top-n diff script by [@KushajveerSingh](https://github.com/KushajveerSingh)
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* Train script and loader/transform tweaks to punch through more aug arguments
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* README and documentation overhaul. See initial (WIP) documentation at https://rwightman.github.io/pytorch-image-models/
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* adamp and sgdp optimizers added by [@hellbell](https://github.com/hellbell)
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### June 11, 2020
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Bunch of changes:
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* DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions
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* VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1
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* Activation factory added along with new activations:
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* select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export)
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* hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish
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* context mgr for setting exportable/scriptable/no_jit states
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* 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
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* Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this
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* Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call
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* Prep for 0.1.28 pip release
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### May 12, 2020
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* Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt, paper https://arxiv.org/abs/2004.08955))
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### May 3, 2020
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* Pruned EfficientNet B1, B2, and B3 (https://arxiv.org/abs/2002.08258) contributed by [Yonathan Aflalo](https://github.com/yoniaflalo)
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### May 1, 2020
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* Merged a number of execellent contributions in the ResNet model family over the past month
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* BlurPool2D and resnetblur models initiated by [Chris Ha](https://github.com/VRandme), I trained resnetblur50 to 79.3.
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* TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by [mrT23](https://github.com/mrT23)
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* ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (https://arxiv.org/abs/2002.08258) by [Yonathan Aflalo](https://github.com/yoniaflalo)
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* 200 pretrained models in total now with updated results csv in results folder
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### April 5, 2020
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* Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite
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* 3.5M param MobileNet-V2 100 @ 73%
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* 4.5M param MobileNet-V2 110d @ 75%
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* 6.1M param MobileNet-V2 140 @ 76.5%
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* 5.8M param MobileNet-V2 120d @ 77.3%
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### March 18, 2020
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* Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite)
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* Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by [Andrew Lavin](https://github.com/andravin) (see Training section for hparams)
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