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- MODEL:
- META_ARCHITECTURE: "GeneralizedRCNN"
- BACKBONE:
- NAME: "build_resnet_fpn_backbone"
- RESNETS:
- OUT_FEATURES: ["res2", "res3", "res4", "res5"]
- FPN:
- IN_FEATURES: ["res2", "res3", "res4", "res5"]
- ANCHOR_GENERATOR:
- SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
- ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
- RPN:
- IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
- PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
- PRE_NMS_TOPK_TEST: 1000 # Per FPN level
- # Detectron1 uses 2000 proposals per-batch,
- # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
- # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
- POST_NMS_TOPK_TRAIN: 1000
- POST_NMS_TOPK_TEST: 1000
- ROI_HEADS:
- NAME: "StandardROIHeads"
- IN_FEATURES: ["p2", "p3", "p4", "p5"]
- ROI_BOX_HEAD:
- NAME: "FastRCNNConvFCHead"
- NUM_FC: 2
- POOLER_RESOLUTION: 7
- ROI_MASK_HEAD:
- NAME: "MaskRCNNConvUpsampleHead"
- NUM_CONV: 4
- POOLER_RESOLUTION: 14
- DATASETS:
- TRAIN: ("coco_2017_train",)
- TEST: ("coco_2017_val",)
- SOLVER:
- IMS_PER_BATCH: 16
- BASE_LR: 0.02
- STEPS: (60000, 80000)
- MAX_ITER: 90000
- INPUT:
- MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
- VERSION: 2
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