_BASE_: "Base-RCNN-FPN.yaml"
MODEL:
  WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
  PIXEL_STD: [57.375, 57.120, 58.395]
  MASK_ON: True
  RESNETS:
    STRIDE_IN_1X1: False  # this is a C2 model
    NUM_GROUPS: 32
    WIDTH_PER_GROUP: 8
    DEPTH: 101
  ROI_HEADS:
    NUM_CLASSES: 1
    SCORE_THRESH_TEST: 0.001
    NMS_THRESH_TEST: .01
INPUT:
  MIN_SIZE_TRAIN: (496,)
  MIN_SIZE_TEST: 496
SOLVER:
  BASE_LR: 0.02
  #GAMMA: 0.05
  #STEPS: (3000, 7000, 11000, 15000)
  #MAX_ITER: 18000
  GAMMA: 0.1
  STEPS: (3000, 4500)
  MAX_ITER: 6000
  CHECKPOINT_PERIOD: 300
  IMS_PER_BATCH: 14
TEST:
  DETECTIONS_PER_IMAGE: 30  # LVIS allows up to 300
  EVAL_PERIOD: 300
DATALOADER:
  SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
  REPEAT_THRESHOLD: 0.001
  NUM_WORKERS: 4
# DATASETS:
#   TRAIN: ("fold1","fold2","fold3","fold4",)
#   TEST: ("fold5",)
# OUTPUT_DIR: "./output_valid_fold5"
# DATASETS:
#   TRAIN: ("fold2","fold3","fold4","fold5",)
#   TEST: ("fold1",)
# OUTPUT_DIR: "./output_valid_fold1"
# DATASETS:
#   TRAIN: ("fold3","fold4","fold5","fold1",)
#   TEST: ("fold2",)
# OUTPUT_DIR: "./output_valid_fold2"
# DATASETS:
#   TRAIN: ("fold4","fold5","fold1","fold2",)
#   TEST: ("fold3",)
# OUTPUT_DIR: "./output_valid_fold3"
# DATASETS:
#   TRAIN: ("fold5","fold1","fold2","fold3",)
#   TEST: ("fold4",)
# OUTPUT_DIR: "./output_valid_fold4"

#modifiying to commit again