RANDOM_SEED: 0
# Don't need AMP to train a tiny linear layer.
AMP: false
CUDNN_BENCHMARK: true
CUDNN_DETERMINISTIC: false

DATA:
  ROOT: "datasets/imagenet"
  IMAGE_TRANSFORM_TRAIN:
    - "random_resized_crop::{'scale': (0.08, 1.0)}"
    - "horizontal_flip"
    - "normalize"
  IMAGE_TRANSFORM_VAL:
    - "smallest_resize"
    - "center_crop"
    - "normalize"

MODEL:
  VISUAL:
    FROZEN: true

OPTIM:
  BATCH_SIZE: 256
  SGD_MOMENTUM: 0.9
  WEIGHT_DECAY: 0.0
  NO_DECAY: "none"
  LOOKAHEAD:
    USE: false

  LR: 0.3
  WARMUP_STEPS: 0
  LR_DECAY_NAME: "cosine"
  NUM_ITERATIONS: 500500  # 100 epochs