paa_R_101_FPN_2x.yaml

MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-101"
  RPN_ONLY: True
  PAA_ON: True
  BACKBONE:
    CONV_BODY: "R-101-FPN-RETINANET"
  RESNETS:
    BACKBONE_OUT_CHANNELS: 256
  RETINANET:
    USE_C5: False
  PAA:
    ANCHOR_SIZES: (64, 128, 256, 512, 1024) # 8S
    ASPECT_RATIOS: (1.0,)
    SCALES_PER_OCTAVE: 1
    USE_DCN_IN_TOWER: False
    TOPK: 9 # topk for selecting candidate positive samples from each level
    IOU_THRESHOLD: 0.1
    REG_LOSS_WEIGHT: 1.3
    USE_IOU_PRED: True
    IOU_LOSS_WEIGHT: 0.5
    INFERENCE_SCORE_VOTING: True
DATASETS:
  TRAIN: ("coco_2017_train",)
  TEST: ("coco_2017_val",)
INPUT:
  MIN_SIZE_RANGE_TRAIN: (640, 800)
  MAX_SIZE_TRAIN: 1333
  MIN_SIZE_TEST: 800
  MAX_SIZE_TEST: 1333
DATALOADER:
  SIZE_DIVISIBILITY: 32
SOLVER:
  BASE_LR: 0.01
  WEIGHT_DECAY: 0.0001
  STEPS: (120000, 160000)
  MAX_ITER: 180000
  IMS_PER_BATCH: 16
  WARMUP_METHOD: "constant"