Included yaml files are timm train script configs for training timm
SBB ViT exploration
See
- https://huggingface.co/blog/rwightman/vit-sbb-imagenet-full
- https://huggingface.co/collections/timm/searching-for-better-vit-baselines-663eb74f64f847d2f35a9c19
Note the # of GPUs, this needs to be taken into consideration for global batch size equivalence, and LR scaling.
Also note, some models have lr
set to a non null value, this LR is used directly if set. Otherwise, it falls back to lr_base
and the used rate is calculated based on lr_base_size
and a sqrt scaling according to the global batch size.
To easily use the .yaml file, use the --config argument for the timm train.py script. eg: train.py --config hparams.yaml --data-dir /where/my/data ... <other arg overrides>