super-gradients/src/super_gradients/recipes/dataset_params/imagenet_dataset_params.yaml

# Base recipe for ImageNet Datasets amd Dataloaders.
img_mean: [0.485, 0.456, 0.406] # mean for normalization
img_std: [0.229, 0.224, 0.225]  # std  for normalization

train_dataset_params:
  root: /data/Imagenet/train
  transforms:
    - RandomResizedCropAndInterpolation:
        size: 224
        interpolation: default
    - RandomHorizontalFlip
    - ToTensor
    - Normalize:
        mean: ${dataset_params.img_mean}
        std: ${dataset_params.img_std}

val_dataset_params:
  root: /data/Imagenet/val
  transforms:
    - Resize:
        size: 256
    - CenterCrop:
        size: 224
    - ToTensor
    - Normalize:
        mean: ${dataset_params.img_mean}
        std: ${dataset_params.img_std}

train_dataloader_params:
  batch_size: 64
  num_workers: 8
  drop_last: False
  pin_memory: True

val_dataloader_params:
  batch_size: 200
  num_workers: 8
  drop_last: False
  pin_memory: True

_convert_: all