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< h1 id = "recent-changes" > Recent Changes< / h1 >
< h3 id = "aug-1-2020" > Aug 1, 2020< / h3 >
< p > Universal feature extraction, new models, new weights, new test sets.< / p >
< ul >
< li > All models support the < code > features_only=True< / code > argument for < code > create_model< / code > call to return a network that extracts features from the deepest layer at each stride.< / li >
< li > New models< ul >
< li > CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet< / li >
< li > ReXNet< / li >
< li > (Aligned) Xception41/65/71 (a proper port of TF models)< / li >
< / ul >
< / li >
< li > New trained weights< ul >
< li > SEResNet50 - 80.3< / li >
< li > CSPDarkNet53 - 80.1 top-1< / li >
< li > CSPResNeXt50 - 80.0 to-1< / li >
< li > DPN68b - 79.2 top-1< / li >
< li > EfficientNet-Lite0 (non-TF ver) - 75.5 (submitted by @hal-314)< / li >
< / ul >
< / li >
< li > Add 'real' labels for ImageNet and ImageNet-Renditions test set, see < a href = "results/README.md" > < code > results/README.md< / code > < / a > < / li >
< / ul >
< h3 id = "june-11-2020" > June 11, 2020< / h3 >
< p > Bunch of changes:< / p >
< ul >
< li > DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions< / li >
< li > VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1< / li >
< li > Activation factory added along with new activations:< ul >
< li > select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export)< / li >
< li > hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish< / li >
< li > context mgr for setting exportable/scriptable/no_jit states< / li >
< / ul >
< / li >
< li > 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< / li >
< li > Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this< / li >
< li > Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call< / li >
< li > Prep for 0.1.28 pip release< / li >
< / ul >
< h3 id = "may-12-2020" > May 12, 2020< / h3 >
< ul >
< li > Add ResNeSt models (code adapted from < a href = "https://github.com/zhanghang1989/ResNeSt" > https://github.com/zhanghang1989/ResNeSt< / a > , paper < a href = "https://arxiv.org/abs/2004.08955" > https://arxiv.org/abs/2004.08955< / a > ))< / li >
< / ul >
< h3 id = "may-3-2020" > May 3, 2020< / h3 >
< ul >
< li > Pruned EfficientNet B1, B2, and B3 (< a href = "https://arxiv.org/abs/2002.08258" > https://arxiv.org/abs/2002.08258< / a > ) contributed by < a href = "https://github.com/yoniaflalo" > Yonathan Aflalo< / a > < / li >
< / ul >
< h3 id = "may-1-2020" > May 1, 2020< / h3 >
< ul >
< li > Merged a number of execellent contributions in the ResNet model family over the past month< ul >
< li > BlurPool2D and resnetblur models initiated by < a href = "https://github.com/VRandme" > Chris Ha< / a > , I trained resnetblur50 to 79.3.< / li >
< li > TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by < a href = "https://github.com/mrT23" > mrT23< / a > < / li >
< li > ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (< a href = "https://arxiv.org/abs/2002.08258" > https://arxiv.org/abs/2002.08258< / a > ) by < a href = "https://github.com/yoniaflalo" > Yonathan Aflalo< / a > < / li >
< / ul >
< / li >
< li > 200 pretrained models in total now with updated results csv in results folder< / li >
< / ul >
< h3 id = "april-5-2020" > April 5, 2020< / h3 >
< ul >
< li > Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite< ul >
< li > 3.5M param MobileNet-V2 100 @ 73%< / li >
< li > 4.5M param MobileNet-V2 110d @ 75%< / li >
< li > 6.1M param MobileNet-V2 140 @ 76.5%< / li >
< li > 5.8M param MobileNet-V2 120d @ 77.3%< / li >
< / ul >
< / li >
< / ul >
< h3 id = "march-18-2020" > March 18, 2020< / h3 >
< ul >
< li > Add EfficientNet-Lite models w/ weights ported from < a href = "https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite" > Tensorflow TPU< / a > < / li >
< li > Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by < a href = "https://github.com/andravin" > Andrew Lavin< / a > (see Training section for hparams)< / li >
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< script src = "https://cdnjs.cloudflare.com/ajax/libs/tablesort/5.2.1/tablesort.min.js" > < / script >
< script src = "../javascripts/tables.js" > < / script >
< / body >
< / html >
