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142 lines
8.9 KiB
142 lines
8.9 KiB
# Recent Changes
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### Feb 10, 2021
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* More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')
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* GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl in `byobnet.py`
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* RepVGG (https://github.com/DingXiaoH/RepVGG), impl in `byobnet.py`
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* classic VGG (from torchvision, impl in `vgg`)
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* Refinements to normalizer layer arg handling and normalizer+act layer handling in some models
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* 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.
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* Fix a few bugs introduced since last pypi release
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### Feb 8, 2021
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* Add several ResNet weights with ECA attention. 26t & 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352.
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* `ecaresnet26t` - 79.88 top-1 @ 320x320, 79.08 @ 256x256
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* `ecaresnet50t` - 82.35 top-1 @ 320x320, 81.52 @ 256x256
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* `ecaresnet269d` - 84.93 top-1 @ 352x352, 84.87 @ 320x320
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* 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).
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* 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.
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### Jan 30, 2021
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* Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based on [paper](https://arxiv.org/abs/2101.08692)
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### Jan 25, 2021
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* Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights from https://github.com/google-research/big_transfer
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* Add official R50+ViT-B/16 hybrid models + weights from https://github.com/google-research/vision_transformer
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* ImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) support
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* NOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learning
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* Add model defs and weights for DeiT Vision Transformer models from https://github.com/facebookresearch/deit
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* Refactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classes
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* Add Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train script
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* 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`
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* Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tar
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* Run validation on full ImageNet-21k directly from tar w/ BiT model: `validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --amp`
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* Models in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handling
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### Jan 3, 2021
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* Add SE-ResNet-152D weights
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* 256x256 val, 0.94 crop top-1 - 83.75
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* 320x320 val, 1.0 crop - 84.36
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* Update results files
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### Dec 18, 2020
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* Add ResNet-101D, ResNet-152D, and ResNet-200D weights trained @ 256x256
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* 256x256 val, 0.94 crop (top-1) - 101D (82.33), 152D (83.08), 200D (83.25)
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* 288x288 val, 1.0 crop - 101D (82.64), 152D (83.48), 200D (83.76)
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* 320x320 val, 1.0 crop - 101D (83.00), 152D (83.66), 200D (84.01)
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### Dec 7, 2020
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* Simplify EMA module (ModelEmaV2), compatible with fully torchscripted models
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* Misc fixes for SiLU ONNX export, default_cfg missing from Feature extraction models, Linear layer w/ AMP + torchscript
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* PyPi release @ 0.3.2 (needed by EfficientDet)
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### Oct 30, 2020
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* Test with PyTorch 1.7 and fix a small top-n metric view vs reshape issue.
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* Convert newly added 224x224 Vision Transformer weights from official JAX repo. 81.8 top-1 for B/16, 83.1 L/16.
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* Support PyTorch 1.7 optimized, native SiLU (aka Swish) activation. Add mapping to 'silu' name, custom swish will eventually be deprecated.
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* Fix regression for loading pretrained classifier via direct model entrypoint functions. Didn't impact create_model() factory usage.
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* PyPi release @ 0.3.0 version!
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### Oct 26, 2020
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* Update Vision Transformer models to be compatible with official code release at https://github.com/google-research/vision_transformer
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* Add Vision Transformer weights (ImageNet-21k pretrain) for 384x384 base and large models converted from official jax impl
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* ViT-B/16 - 84.2
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* ViT-B/32 - 81.7
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* ViT-L/16 - 85.2
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* ViT-L/32 - 81.5
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### Oct 21, 2020
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* Weights added for Vision Transformer (ViT) models. 77.86 top-1 for 'small' and 79.35 for 'base'. Thanks to [Christof](https://www.kaggle.com/christofhenkel) for training the base model w/ lots of GPUs.
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### Oct 13, 2020
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* Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train...
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* Adafactor and AdaHessian (FP32 only, no AMP) optimizers
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* EdgeTPU-M (`efficientnet_em`) model trained in PyTorch, 79.3 top-1
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* Pip release, doc updates pending a few more changes...
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### Sept 18, 2020
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* New ResNet 'D' weights. 72.7 (top-1) ResNet-18-D, 77.1 ResNet-34-D, 80.5 ResNet-50-D
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* Added a few untrained defs for other ResNet models (66D, 101D, 152D, 200/200D)
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### Sept 3, 2020
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* New weights
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* Wide-ResNet50 - 81.5 top-1 (vs 78.5 torchvision)
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* SEResNeXt50-32x4d - 81.3 top-1 (vs 79.1 cadene)
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* Support for native Torch AMP and channels_last memory format added to train/validate scripts (`--channels-last`, `--native-amp` vs `--apex-amp`)
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* 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.
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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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