MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  RPN:
    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
    PRE_NMS_TOPK_TEST: 6000
    POST_NMS_TOPK_TEST: 1000
  ROI_HEADS:
    #    NUM_CLASSES: 81 # 0-79 Known class; 80 -> Unknown; 81 -> Background.
    NUM_CLASSES: 81
    IN_FEATURES: ["p2", "p3", "p4", "p5"]
    NAME: "Res5ROIHeads"
  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
  MASK_ON: False
  RESNETS:
    DEPTH: 50
    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
  ROI_BOX_HEAD:
    NAME: "FastRCNNConvFCHead"
    NUM_FC: 2
    POOLER_RESOLUTION: 7
  ROI_MASK_HEAD:
    NAME: "MaskRCNNConvUpsampleHead"
    NUM_CONV: 4
    POOLER_RESOLUTION: 14
INPUT:
  MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
  MIN_SIZE_TEST: 800
DATASETS:
  TRAIN: ("coco_2017_train",)
  TEST: ("coco_2017_val",)
SOLVER:
  IMS_PER_BATCH: 16
  BASE_LR: 0.02
  STEPS: (60000, 80000)
  MAX_ITER: 90000
VERSION: 2
OWOD:
  ENABLE_THRESHOLD_AUTOLABEL_UNK: True
  NUM_UNK_PER_IMAGE: 1
  ENABLE_UNCERTAINITY_AUTOLABEL_UNK: False
  ENABLE_CLUSTERING: True
  FEATURE_STORE_SAVE_PATH: 'feature_store'
  CLUSTERING:
    ITEMS_PER_CLASS: 20
    START_ITER: 0 #1000
    UPDATE_MU_ITER: 3000
    MOMENTUM: 0.99
    Z_DIMENSION: 128