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_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
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