resnet50_cutmix.yml

EXP_NAME: "ResNet50_CutMix"

RESUME_WEIGHT: ~

WEIGHT:
  NAME: "resnet50.pth"
  SAVE_DIR: "/mnt/sdb/data/wangxinran/weight/fgvclib/"
  
LOGGER: 
  NAME: "txt_logger"

DATASET:
  NAME: "CUB_200_2011"
  ROOT: "/mnt/sdb/data/wangxinran/dataset/"
  TRAIN: 
    BATCH_SIZE: 64
    POSITIVE: 0
    PIN_MEMORY: True
    SHUFFLE: True
    NUM_WORKERS: 4
  TEST: 
    BATCH_SIZE: 64
    POSITIVE: 0
    PIN_MEMORY: False
    SHUFFLE: False
    NUM_WORKERS: 4

MODEL:
  NAME: "ResNet50_CutMix"
  CLASS_NUM: 200
  CRITERIONS: 
    - name: "cross_entropy_loss"
      args: []
      w: 1.0
  BACKBONE:
    NAME: "resnet50"
    ARGS:
      - pretrained: True
      - del_keys: []
  ENCODER:
    NAME: "global_avg_pooling"
  NECKS:
    NAME: ~
  HEADS:
    NAME: "classifier_1fc"
    ARGS:
      - in_dim: 
        - 2048

TRANSFORMS: 
  TRAIN: 
    - name: "resize"
      size: 
        - 600
        - 600
    - name: "random_crop"
      size: 448
      padding: 8
    - name: "random_horizontal_flip"
      prob: 0.5
    - name: "to_tensor"
    - name: "normalize"
      mean: 
         - 0.5
         - 0.5
         - 0.5
      std: 
        - 0.5
        - 0.5
        - 0.5
  TEST:
    - name: "resize"
      size: 
        - 600
        - 600
    - name: "center_crop"
      size: 448
    - name: "to_tensor"
    - name: "normalize"
      mean: 
         - 0.5
         - 0.5
         - 0.5
      std: 
        - 0.5
        - 0.5
        - 0.5

OPTIMIZER:
  NAME: "SGD"
  ARGS: 
    - momentum: 0.9
    - weight_decay: 0.0005
  LR:
    base: 0.0002 
    backbone: 0.0002
    encoder: 0.002
    necks: 0.002
    heads: 0.002

ITERATION_NUM: ~
EPOCH_NUM: 1
START_EPOCH: 0
UPDATE_STRATEGY: "general_updating"

# Validation details
PER_ITERATION: ~
PER_EPOCH: ~
METRICS: 
  - name: "accuracy(topk=1)"
    metric: "accuracy"
    top_k: 1
    threshold: ~
  - name: "accuracy(topk=5)"
    metric: "accuracy"
    top_k: 5
    threshold: ~
  - name: "recall(threshold=0.5)"
    metric: "recall"
    top_k: ~
    threshold: 0.5
  - name: "precision(threshold=0.5)"
    metric: "precision"
    top_k: ~
    threshold: 0.5

INTERPRETER:
  NAME: "cam"
  METHOD: "gradcam"
  TARGET_LAYERS: 
    - "backbone.layer4"