Feature Maps, More Models, CutMix
Aug 12, 2020
- New/updated weights from training experiments
- EfficientNet-B3 - 82.1 top-1 (vs 81.6 for official with AA and 81.9 for AdvProp)
- RegNetY-3.2GF - 82.0 top-1 (78.9 from official ver)
- CSPResNet50 - 79.6 top-1 (76.6 from official ver)
- Add CutMix integrated w/ Mixup. See pull request for some usage examples
- Some fixes for using pretrained weights with
in_chans
!= 3 on several models.
Aug 5, 2020
Universal feature extraction, new models, new weights, new test sets.
- All models support the
features_only=True
argument forcreate_model
call to return a network that extracts feature maps from the deepest layer at each stride. - New models
- CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet
- ReXNet
- (Modified Aligned) Xception41/65/71 (a proper port of TF models)
- New trained weights
- SEResNet50 - 80.3 top-1
- CSPDarkNet53 - 80.1 top-1
- CSPResNeXt50 - 80.0 top-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
- Test set ranking/top-n diff script by @KushajveerSingh
- Train script and loader/transform tweaks to punch through more aug arguments
- README and documentation overhaul. See initial (WIP) documentation at https://rwightman.github.io/pytorch-image-models/
- adamp and sgdp optimizers added by @hellbell