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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
# WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# For better, more stable performance initialize from COCO
WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
MASK_ON: True
ROI_HEADS:
NUM_CLASSES: 8
# This is similar to the setting used in Mask R-CNN paper, Appendix A
# But there are some differences, e.g., we did not initialize the output
# layer using the corresponding classes from COCO
INPUT:
MIN_SIZE_TRAIN: (800, 832, 864, 896, 928, 960, 992, 1024)
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 1024
MAX_SIZE_TRAIN: 2048
MAX_SIZE_TEST: 2048
DATASETS:
TRAIN: ("cityscapes_fine_instance_seg_train",)
TEST: ("cityscapes_fine_instance_seg_val",)
SOLVER:
BASE_LR: 0.01
STEPS: (18000,)
MAX_ITER: 24000
IMS_PER_BATCH: 8
TEST:
EVAL_PERIOD: 8000
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