mirror of https://github.com/RE-OWOD/RE-OWOD
54 lines
1.5 KiB
YAML
54 lines
1.5 KiB
YAML
MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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RPN:
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IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
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PRE_NMS_TOPK_TEST: 6000
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POST_NMS_TOPK_TEST: 1000
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ROI_HEADS:
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# NUM_CLASSES: 81 # 0-79 Known class; 80 -> Unknown; 81 -> Background.
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NUM_CLASSES: 81
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IN_FEATURES: ["p2", "p3", "p4", "p5"]
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NAME: "Res5ROIHeads"
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WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
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MASK_ON: False
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RESNETS:
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DEPTH: 50
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OUT_FEATURES: ["res2", "res3", "res4", "res5"]
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FPN:
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IN_FEATURES: ["res2", "res3", "res4", "res5"]
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ANCHOR_GENERATOR:
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SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
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ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps
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ROI_BOX_HEAD:
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NAME: "FastRCNNConvFCHead"
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NUM_FC: 2
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POOLER_RESOLUTION: 7
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ROI_MASK_HEAD:
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NAME: "MaskRCNNConvUpsampleHead"
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NUM_CONV: 4
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POOLER_RESOLUTION: 14
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INPUT:
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MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
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MIN_SIZE_TEST: 800
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DATASETS:
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TRAIN: ("coco_2017_train",)
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TEST: ("coco_2017_val",)
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SOLVER:
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IMS_PER_BATCH: 16
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BASE_LR: 0.02
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STEPS: (60000, 80000)
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MAX_ITER: 90000
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VERSION: 2
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OWOD:
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ENABLE_THRESHOLD_AUTOLABEL_UNK: True
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NUM_UNK_PER_IMAGE: 1
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ENABLE_UNCERTAINITY_AUTOLABEL_UNK: False
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ENABLE_CLUSTERING: True
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FEATURE_STORE_SAVE_PATH: 'feature_store'
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CLUSTERING:
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ITEMS_PER_CLASS: 20
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START_ITER: 0 #1000
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UPDATE_MU_ITER: 3000
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MOMENTUM: 0.99
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Z_DIMENSION: 128
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