huggingface/pytorch-image-models v0.9.6

Release v0.9.6

on GitHub

Aug 28, 2023

• Add dynamic img size support to models in vision_transformer.py, vision_transformer_hybrid.py, deit.py, and eva.py w/o breaking backward compat.
  • Add dynamic_img_size=True to args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass).
  • Add dynamic_img_pad=True to allow image sizes that aren't divisible by patch size (pad bottom right to patch size each forward pass).
  • Enabling either dynamic mode will break FX tracing unless PatchEmbed module added as leaf.
  • Existing method of resizing position embedding by passing different img_size (interpolate pretrained embed weights once) on creation still works.
  • Existing method of changing patch_size (resize pretrained patch_embed weights once) on creation still works.
  • Example validation cmd python validate.py /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True

Aug 25, 2023

• Many new models since last release
  • FastViT - https://arxiv.org/abs/2303.14189
  • MobileOne - https://arxiv.org/abs/2206.04040
  • InceptionNeXt - https://arxiv.org/abs/2303.16900
  • RepGhostNet - https://arxiv.org/abs/2211.06088 (thanks https://github.com/ChengpengChen)
  • GhostNetV2 - https://arxiv.org/abs/2211.12905 (thanks https://github.com/yehuitang)
  • EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027 (thanks https://github.com/seefun)
  • EfficientViT (MIT) - https://arxiv.org/abs/2205.14756 (thanks https://github.com/seefun)
• Add --reparam arg to benchmark.py, onnx_export.py, and validate.py to trigger layer reparameterization / fusion for models with any one of reparameterize(), switch_to_deploy() or fuse()
  • Including FastViT, MobileOne, RepGhostNet, EfficientViT (MSRA), RepViT, RepVGG, and LeViT
• Preparing 0.9.6 'back to school' release

Aug 11, 2023

• Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights
• Example validation cmd to test w/ non-square resize python validate.py /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320