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