rwightman/pytorch-image-models: v0.6.11 Release

Changes Since 0.6.7 Sept 23, 2022

• CLIP LAION-2B pretrained B/32, L/14, H/14, and g/14 image tower weights as vit models (for fine-tune)

• Hugging Face timm docs home now exists, look for more here in the future
• Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2
• Add more weights in maxxvit series incl a pico (7.5M params, 1.9 GMACs), two tiny variants:
  • maxvit_rmlp_pico_rw_256 - 80.5 @ 256, 81.3 @ 320 (T)
  • maxvit_tiny_rw_224 - 83.5 @ 224 (G)
  • maxvit_rmlp_tiny_rw_256 - 84.2 @ 256, 84.8 @ 320 (T)

• MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this:
  • maxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.6 @ 320 (T)

• CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) timm original models
  • both found in maxxvit.py model def, contains numerous experiments outside scope of original papers
  • an unfinished Tensorflow version from MaxVit authors can be found https://github.com/google-research/maxvit
• Initial CoAtNet and MaxVit timm pretrained weights (working on more):
  • coatnet_nano_rw_224 - 81.7 @ 224 (T)
  • coatnet_rmlp_nano_rw_224 - 82.0 @ 224, 82.8 @ 320 (T)
  • coatnet_0_rw_224 - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks
  • coatnet_bn_0_rw_224 - 82.4 (T)
  • maxvit_nano_rw_256 - 82.9 @ 256 (T)
  • coatnet_rmlp_1_rw_224 - 83.4 @ 224, 84 @ 320 (T)
  • coatnet_1_rw_224 - 83.6 @ 224 (G)
  • (T) = TPU trained with bits_and_tpu branch training code, (G) = GPU trained
• GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% timm re-write for license purposes)
• MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit)
• EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer)
• PyramidVisionTransformer-V2 (adapted from https://github.com/whai362/PVT)
• 'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost)

• ConvNeXt atto weights added
  • convnext_atto - 75.7 @ 224, 77.0 @ 288
  • convnext_atto_ols - 75.9 @ 224, 77.2 @ 288

• More custom ConvNeXt smaller model defs with weights
  • convnext_femto - 77.5 @ 224, 78.7 @ 288
  • convnext_femto_ols - 77.9 @ 224, 78.9 @ 288
  • convnext_pico - 79.5 @ 224, 80.4 @ 288
  • convnext_pico_ols - 79.5 @ 224, 80.5 @ 288
  • convnext_nano_ols - 80.9 @ 224, 81.6 @ 288
• Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt)

• Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks Hugo Touvron!