fcos_imprv_dcnv2_X_101_64x4d_FPN_2x.yaml

MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "https://cloudstor.aarnet.edu.au/plus/s/k3ys35075jmU1RP/download#X-101-64x4d.pkl"
  RPN_ONLY: True
  FCOS_ON: True
  BACKBONE:
    CONV_BODY: "R-101-FPN-RETINANET"
  RESNETS:
    STRIDE_IN_1X1: False
    BACKBONE_OUT_CHANNELS: 256
    NUM_GROUPS: 64
    WIDTH_PER_GROUP: 4
    STAGE_WITH_DCN: (False, False, True, True)
    WITH_MODULATED_DCN: True
    DEFORMABLE_GROUPS: 1
  RETINANET:
    USE_C5: False # FCOS uses P5 instead of C5
  FCOS:
    # normalizing the regression targets with FPN strides
    NORM_REG_TARGETS: True
    # positioning centerness on the regress branch.
    # Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042
    CENTERNESS_ON_REG: True
    # using center sampling and GIoU.
    # Please refer to https://github.com/yqyao/FCOS_PLUS
    CENTER_SAMPLING_RADIUS: 1.5
    IOU_LOSS_TYPE: "giou"
    # we only use dcn in the last layer of towers
    USE_DCN_IN_TOWER: True
DATASETS:
  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
  TEST: ("coco_2014_minival",)
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"
TEST:
  BBOX_AUG:
    ENABLED: False
    H_FLIP: True
    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
    MAX_SIZE: 2000
    SCALE_H_FLIP: True