super-gradients/src/super_gradients/recipes/imagenet_vit_large.yaml

#  ViT Imagenet1K fine tuning from Imagenet21K classification training:
#  This example trains with batch_size = 32 * 8 GPUs, total 256.
#  Training time on 8 x GeForce RTX A5000 is 52min / epoch.
#  ViT Large : 85.64 (Final averaged model)
#
#  Log and tensorboard at s3://deci-pretrained-models/vit_large_cutmix_randaug_v2_lr=0.03/

# Instructions:
# running from the command line, set the PYTHONPATH environment variable: (Replace "YOUR_LOCAL_PATH" with the path to the downloaded repo):
#   export PYTHONPATH="YOUR_LOCAL_PATH"/super_gradients/
# Then:
#   python -m torch.distributed.launch --nproc_per_node=8 train_from_recipe_example/train_from_recipe.py --config-name=imagenet_vit_large


defaults:
  - imagenet_vit_base


dataset_params:
  batch_size: 32

training_hyperparams:
  initial_lr: 0.06
  average_best_models: True

architecture: vit_large

experiment_name: vit_large_imagenet1k