First official release in a long while (since 0.5.4). All change log since 0.5.4 below, July 8, 2022 More models, more fixes Official research models (w/ weights) added: EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt) MobileViT-V2 from (https://github.com/apple/ml-cvnets) DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit) My own models: Small ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14) CspNet refactored with dataclass config, simplified CrossStage3 (cs3) option. These are closer to YOLO-v5+ backbone defs. More relative position vit fiddling. Two srelpos (shared relative position) models trained, and a medium w/ class token. Add an alternate downsample mode to EdgeNeXt and train a small model. Better than original small, but not their new USI trained weights. My own model weight results (all ImageNet-1k training) resnet10t - 66.5 @ 176, 68.3 @ 224 resnet14t - 71.3 @ 176, 72.3 @ 224 resnetaa50 - 80.6 @ 224 , 81.6 @ 288 darknet53 - 80.0 @ 256, 80.5 @ 288 cs3darknet_m - 77.0 @ 256, 77.6 @ 288 cs3darknet_focus_m - 76.7 @ 256, 77.3 @ 288 cs3darknet_l - 80.4 @ 256, 80.9 @ 288 cs3darknet_focus_l - 80.3 @ 256, 80.9 @ 288 vit_srelpos_small_patch16_224 - 81.1 @ 224, 82.1 @ 320 vit_srelpos_medium_patch16_224 - 82.3 @ 224, 83.1 @ 320 vit_relpos_small_patch16_cls_224 - 82.6 @ 224, 83.6 @ 320 edgnext_small_rw - 79.6 @ 224, 80.4 @ 320 cs3, darknet, and vit_*relpos weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs. Hugging Face Hub support fixes verified, demo notebook TBA Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation. Add support to change image extensions scanned by timm datasets/parsers. See (https://github.com/rwightman/pytorch-image-models/pull/1274#issuecomment-1178303103) Default ConvNeXt LayerNorm impl to use F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2) via LayerNorm2d in all cases. a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges. previous impl exists as LayerNormExp2d in models/layers/norm.py Numerous bug fixes Currently testing for imminent PyPi 0.6.x release LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)? ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ... May 13, 2022 Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript. Some refactoring for existing timm Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects. More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program) vit_relpos_small_patch16_224 - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool vit_relpos_medium_patch16_rpn_224 - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool vit_relpos_medium_patch16_224 - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool vit_relpos_base_patch16_gapcls_224 - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake) Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020) Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg) May 2, 2022 Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (vision_transformer_relpos.py) and Residual Post-Norm branches (from Swin-V2) (vision_transformer*.py) vit_relpos_base_patch32_plus_rpn_256 - 79.5 @ 256, 80.6 @ 320 -- rel pos + extended width + res-post-norm, no class token, avg pool vit_relpos_base_patch16_224 - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool vit_base_patch16_rpn_224 - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie How to Train Your ViT) vit_* models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae). April 22, 2022 timm models are now officially supported in fast.ai! Just in time for the new Practical Deep Learning course. timmdocs documentation link updated to timm.fast.ai. Two more model weights added in the TPU trained series. Some In22k pretrain still in progress. seresnext101d_32x8d - 83.69 @ 224, 84.35 @ 288 seresnextaa101d_32x8d (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288 March 23, 2022 Add ParallelBlock and LayerScale option to base vit models to support model configs in Three things everyone should know about ViT convnext_tiny_hnf (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs. March 21, 2022 Merge norm_norm_norm. IMPORTANT this update for a coming 0.6.x release will likely de-stabilize the master branch for a while. Branch 0.5.x or a previous 0.5.x release can be used if stability is required. Significant weights update (all TPU trained) as described in this release regnety_040 - 82.3 @ 224, 82.96 @ 288 regnety_064 - 83.0 @ 224, 83.65 @ 288 regnety_080 - 83.17 @ 224, 83.86 @ 288 regnetv_040 - 82.44 @ 224, 83.18 @ 288 (timm pre-act) regnetv_064 - 83.1 @ 224, 83.71 @ 288 (timm pre-act) regnetz_040 - 83.67 @ 256, 84.25 @ 320 regnetz_040h - 83.77 @ 256, 84.5 @ 320 (w/ extra fc in head) resnetv2_50d_gn - 80.8 @ 224, 81.96 @ 288 (pre-act GroupNorm) resnetv2_50d_evos 80.77 @ 224, 82.04 @ 288 (pre-act EvoNormS) regnetz_c16_evos - 81.9 @ 256, 82.64 @ 320 (EvoNormS) regnetz_d8_evos - 83.42 @ 256, 84.04 @ 320 (EvoNormS) xception41p - 82 @ 299 (timm pre-act) xception65 - 83.17 @ 299 xception65p - 83.14 @ 299 (timm pre-act) resnext101_64x4d - 82.46 @ 224, 83.16 @ 288 seresnext101_32x8d - 83.57 @ 224, 84.270 @ 288 resnetrs200 - 83.85 @ 256, 84.44 @ 320 HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon) SwinTransformer-V2 implementation added. Submitted by Christoph Reich. Training experiments and model changes by myself are ongoing so expect compat breaks. Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2 MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer VOLO models w/ weights adapted from https://github.com/sail-sg/volo Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc Enhance support for alternate norm + act ('NormAct') layers added to a number of models, esp EfficientNet/MobileNetV3, RegNet, and aligned Xception Grouped conv support added to EfficientNet family Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler Gradient checkpointing support added to many models forward_head(x, pre_logits=False) fn added to all models to allow separate calls of forward_features + forward_head All vision transformer and vision MLP models update to return non-pooled / non-token selected features from foward_features, for consistency with CNN models, token selection or pooling now applied in forward_head Feb 2, 2022 Chris Hughes posted an exhaustive run through of timm on his blog yesterday. Well worth a read. Getting Started with PyTorch Image Models (timm): A Practitioner's Guide I'm currently prepping to merge the norm_norm_norm branch back to master (ver 0.6.x) in next week or so. The changes are more extensive than usual and may destabilize and break some model API use (aiming for full backwards compat). So, beware pip install git+https://github.com/rwightman/pytorch-image-models installs! 0.5.x releases and a 0.5.x branch will remain stable with a cherry pick or two until dust clears. Recommend sticking to pypi install for a bit if you want stable.