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The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT)...

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It would be great if this repo started benchmarking detection models such as yolo family and Detr on COCO dataset. Big ask I know but it would be fantastic.

enhancement

See https://github.com/pytorch/pytorch/issues/71465 Slightly changes LayerNorm2d implementation, 1) currently when ln2d is called on a contiguous tensor, it accidentally turns most of the network into channels last mode, line 114 undoes...

Should Fix errors like RuntimeError: Error(s) in loading state_dict for EfficientDet: Missing key(s) in state_dict: "backbone.conv_stem.weight", "backbone.bn1.weight", ... Unexpected key(s) in state_dict: "model.model.backbone.conv_stem.weight", "model.model.backbone.bn1.weight", "model.model.backbone.bn1.bias", "model.model.backbone.bn1.running_mean", "model.model.backbone.bn1.running_var", "model.model.backbone.bn1.num_batches_tracked", "model.model.backbone.blocks.0.0.conv_dw.weight",

the code is released in [https://github.com/hustvl/TopFormer](url)

enhancement

Straight forward conversion of models trained with timm {all checkpoints; model_best; last; specific_model_file}

Is there a plan for adding [Squeezenet](https://pytorch.org/hub/pytorch_vision_squeezenet/) as part of `timm` library?

enhancement

I am using `timm` version `0.5.0`, and I plan to train a greyscale image, but the data loader is outputting RGB images. The model I intend to use works fine...

bug

Is your feature request related to a problem? Please describe. ViT GSAM checkpoint have been released in https://console.cloud.google.com/storage/browser/vit_models/gsam

enhancement