huggingface/pytorch-image-models v0.8.10dev0

v0.8.10dev0 Release

on GitHub

Feb 7, 2023

• New inference benchmark numbers added in results folder.
• Add convnext LAION CLIP trained weights and initial set of in1k fine-tunes
  • convnext_base.clip_laion2b_augreg_ft_in1k - 86.2% @ 256x256
  • convnext_base.clip_laiona_augreg_ft_in1k_384 - 86.5% @ 384x384
  • convnext_large_mlp.clip_laion2b_augreg_ft_in1k - 87.3% @ 256x256
  • convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384 - 87.9% @ 384x384
• Add DaViT models. Supports features_only=True. Adapted from https://github.com/dingmyu/davit by Fredo.
• Use a common NormMlpClassifierHead across MaxViT, ConvNeXt, DaViT
• Add EfficientFormer-V2 model, update EfficientFormer, and refactor LeViT (closely related architectures). Weights on HF hub.
  • New EfficientFormer-V2 arch, significant refactor from original at (https://github.com/snap-research/EfficientFormer). Supports features_only=True.
  • Minor updates to EfficientFormer.
  • Refactor LeViT models to stages, add features_only=True support to new conv variants, weight remap required.
• Move ImageNet meta-data (synsets, indices) from /results to timm/data/_info.
• Add ImageNetInfo / DatasetInfo classes to provide labelling for various ImageNet classifier layouts in timm
  • Update inference.py to use, try: python inference.py /folder/to/images --model convnext_small.in12k --label-type detail --topk 5
• Ready for 0.8.10 pypi pre-release (final testing).

Jan 20, 2023

• Add two convnext 12k -> 1k fine-tunes at 384x384

  • convnext_tiny.in12k_ft_in1k_384 - 85.1 @ 384
  • convnext_small.in12k_ft_in1k_384 - 86.2 @ 384

• Push all MaxxViT weights to HF hub, and add new ImageNet-12k -> 1k fine-tunes for rw base MaxViT and CoAtNet 1/2 models