OUTPUT_DIR: "./output/ic15"
MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
  BACKBONE:
    CONV_BODY: "R-50-FPN"
  RESNETS:
    BACKBONE_OUT_CHANNELS: 256
  RPN:
    USE_FPN: True
    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
    ASPECT_RATIOS: (0.25, 0.5, 1.0, 2.0, 4.0)
  ROI_HEADS:
    USE_FPN: True
    SCORE_THRESH: 0.52  # ic15
    NMS: 0.89
  ROI_BOX_HEAD:
    DEFORMABLE_POOLING: False
    POOLER_RESOLUTION: 7
    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
    POOLER_SAMPLING_RATIO: 2
    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
    PREDICTOR: "FPNPredictor"
    NUM_CLASSES: 2
    CLASS_WEIGHT: 1.0
  ## Boundary
  BOUNDARY_ON: True
  ROI_BOUNDARY_HEAD:
    DEFORMABLE_POOLING: False
    FEATURE_EXTRACTOR: "BoundaryRCNNFPNFeatureExtractor"
    POOLER_RESOLUTION: 14
    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
    POOLER_SAMPLING_RATIO: 2
    PREDICTOR: "BoundaryRCNNC4Predictor"
    RESOLUTION: 48
    SHARE_BOX_FEATURE_EXTRACTOR: False
    BO_WEIGHT: 0.1
    Loss_balance: 1.0

PROCESS:
  PNMS: True
  NMS_THRESH: 0.25
DATASETS:
  TRAIN: ("ic15_train",)
  TEST: ("ic15_test",)
  Test_Visual: True
DATALOADER:
  SIZE_DIVISIBILITY: 32
SOLVER:
  BASE_LR: 0.00025
  BIAS_LR_FACTOR: 2
  WEIGHT_DECAY: 0.0001
#  STEPS: (120000, 160000)
  STEPS: (5000, 10000)  # fine-tune
#  MAX_ITER: 180000
  MAX_ITER: 190500  # fine-tune
  IMS_PER_BATCH: 1
  CHECKPOINT_PERIOD: 5000
INPUT:

  MIN_SIZE_TRAIN: (400,600,720,1000,1200)
  MAX_SIZE_TRAIN: 2000
  MIN_SIZE_TEST: 1200
  MAX_SIZE_TEST: 2000

  CROP_PROB_TRAIN: 1.0
  ROTATE_PROB_TRAIN: 0.3  # fine-tune
#  ROTATE_PROB_TRAIN: 1.0
#  ROTATE_DEGREE: (0,30,60,90,210,150,180,210,240,270,300,330,360)
  ROTATE_DEGREE: (10,)  # fine-tune

TEST:
  IMS_PER_BATCH: 1