MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  MASK_ON: False
  BACKBONE:
    NAME: "build_resnet_fpn_backbone"
  RESNETS:
    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
    STRIDE_IN_1X1: False  # this is a C2 model
    NUM_GROUPS: 64
    WIDTH_PER_GROUP: 4
    DEPTH: 101
  FPN:
    IN_FEATURES: ["res2", "res3", "res4", "res5"]
    OUT_CHANNELS: 512
  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
    NMS_THRESH: 0.7
  ROI_HEADS:
    NAME: "StandardROIHeads"
    IN_FEATURES: ["p2", "p3", "p4", "p5"]
    NUM_CLASSES: 1601
    NMS_THRESH_TEST: 0.3
  ROI_BOX_HEAD:
    NAME: "FastRCNNConvFCHead"
    NUM_CONV: 0
    NUM_FC: 2
    FC_DIM: 2048
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 2
    POOLER_TYPE: "ROIAlign"
VERSION: 2