huggingface/pytorch-image-models v0.6.11 on GitHub

Changes Since 0.6.7

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!