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) Sept 7, 2022 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) Aug 29, 2022 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) Aug 26, 2022 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) Aug 15, 2022 ConvNeXt atto weights added convnext_atto - 75.7 @ 224, 77.0 @ 288 convnext_atto_ols - 75.9 @ 224, 77.2 @ 288 Aug 5, 2022 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) July 28, 2022 Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks Hugo Touvron