MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
  NUM_CLASSES: 81
  FASTER_RCNN: True
NUM_GPUS: 4
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.01
  GAMMA: 0.1
  MAX_ITER: 15000
  STEPS: [0, 7500, 10000]
  # Equivalent schedules with...
  # 1 GPU:
  #   BASE_LR: 0.0025
  #   MAX_ITER: 60000
  #   STEPS: [0, 30000, 40000]
  # 2 GPUs:
  #   BASE_LR: 0.005
  #   MAX_ITER: 30000
  #   STEPS: [0, 15000, 20000]
  # 4 GPUs:
  #   BASE_LR: 0.01
  #   MAX_ITER: 15000
  #   STEPS: [0, 7500, 10000]
  # 8 GPUs:
  #   BASE_LR: 0.02
  #   MAX_ITER: 7500
  #   STEPS: [0, 3750, 5000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
TRAIN:
  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
  DATASETS: ('coco_2014_train',)
  SCALES: (500,)
  MAX_SIZE: 833
  BATCH_SIZE_PER_IM: 256
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
TEST:
  DATASETS: ('coco_2014_minival',)
  SCALE: 500
  MAX_SIZE: 833
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
  RPN_POST_NMS_TOP_N: 1000
OUTPUT_DIR: .
