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PyTorch Image Models

Employ timm for ImageNet evaluation.

Training

Different from standard timm scripts, we separate the root directory of train and eval data, as the images are reconstructed in the quantization process. Besides, you can change the select_indices parameter to specify the sample-level quantized sample indices. Multiple indices can be specified here. We use the ResNet50 model as the template here. For the other models, you can refer to the timm documentation and conduct the above modifications.

sh distributed_train.sh 9 [TRAIN_ROOT] [EVAL_ROOT] --select-indices [INDICES1] [INDICES2] --output [OUTPUT_DIR] --model resnet50 --sched cosine --epochs 260 --lr 0.6 --reprob 0.6 --remode pixel --batch-size 128 --amp --aug-splits 3 -aa rand-m9-mstd0.5-inc1 --resplit --split-bn --jsd --dist-bn reduce

Getting Started (Documentation)

Current documentation for timm covers the basics.

Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide by Chris Hughes is an extensive blog post covering many aspects of timm in detail.

timmdocs is quickly becoming a much more comprehensive set of documentation for timm. A big thanks to Aman Arora for his efforts creating timmdocs.

paperswithcode is a good resource for browsing the models within timm.