43 lines
1.3 KiB
YAML
43 lines
1.3 KiB
YAML
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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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BACKBONE:
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NAME: "build_resnet_fpn_backbone"
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RESNETS:
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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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RPN:
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IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
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PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
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PRE_NMS_TOPK_TEST: 1000 # Per FPN level
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# Detectron1 uses 2000 proposals per-batch,
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# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
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# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
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POST_NMS_TOPK_TRAIN: 1000
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POST_NMS_TOPK_TEST: 1000
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ROI_HEADS:
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NAME: "StandardROIHeads"
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IN_FEATURES: ["p2", "p3", "p4", "p5"]
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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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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: 2
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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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INPUT:
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MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
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VERSION: 2
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