MODEL:
  TYPE: retinanet
  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
  NUM_CLASSES: 81
NUM_GPUS: 8
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.01
  GAMMA: 0.1
  MAX_ITER: 90000
  STEPS: [0, 60000, 80000]
FPN:
  FPN_ON: True
  MULTILEVEL_RPN: True
  RPN_MAX_LEVEL: 7
  RPN_MIN_LEVEL: 3
  COARSEST_STRIDE: 128
  EXTRA_CONV_LEVELS: True
RESNETS:
  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
  TRANS_FUNC: bottleneck_transformation
  NUM_GROUPS: 32
  WIDTH_PER_GROUP: 8
RETINANET:
  RETINANET_ON: True
  NUM_CONVS: 4
  ASPECT_RATIOS: (1.0, 2.0, 0.5)
  SCALES_PER_OCTAVE: 3
  ANCHOR_SCALE: 4
  LOSS_GAMMA: 2.0
  LOSS_ALPHA: 0.25
TRAIN:
  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
  SCALES: (800,)
  MAX_SIZE: 1333
  RPN_STRADDLE_THRESH: -1  # default 0
TEST:
  DATASETS: ('coco_2014_minival',)
  SCALE: 800
  MAX_SIZE: 1333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
  RPN_POST_NMS_TOP_N: 2000
OUTPUT_DIR: .
