faster_rcnn_fbnetv3a_C4_LSJ.yaml

MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  MASK_ON: False
  FBNET_V2:
    ARCH: "FBNetV3_A"
    NORM: "naiveSyncBN"
    WIDTH_DIVISOR: 8
  BACKBONE:
    NAME: FBNetV2C4Backbone
  ANCHOR_GENERATOR:
    # SIZES: [[32, 64, 128, 256, 512]]  # NOTE: for smaller resolution (320 < 512)
    SIZES: [[32, 64, 96, 128, 160]]
    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
  RPN:
    HEAD_NAME: FBNetV2RpnHead
    IN_FEATURES: ["trunk3"]
    # Default values are 12000/2000 for train and 6000/1000 for test. In FBNet
    # we use smaller numbers.  TODO: reduce proposals for test in .yaml directly.
    PRE_NMS_TOPK_TRAIN: 2000
    POST_NMS_TOPK_TRAIN: 2000
    PRE_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TEST: 30
  ROI_HEADS:
    NAME: StandardROIHeads
    IN_FEATURES: ["trunk3"]
  ROI_BOX_HEAD:
    NAME: FBNetV2RoIBoxHead
    POOLER_RESOLUTION: 6
    NORM: "naiveSyncBN"
  ROI_MASK_HEAD:
    NAME: "MaskRCNNConvUpsampleHead"
    NUM_CONV: 4
    POOLER_RESOLUTION: 14
MODEL_EMA:
  ENABLED: True
  DECAY: 0.9998
DATASETS:
  TRAIN: ("coco_2017_train",)
  TEST: ("coco_2017_val",)
SOLVER:
  IMS_PER_BATCH: 32
  BASE_LR: 0.16
  MAX_ITER: 540000
  LR_SCHEDULER_NAME: WarmupCosineLR
TEST:
  EVAL_PERIOD: 10000
D2GO_DATA:
  AUG_OPS:
    TRAIN: [
      'ResizeScaleOp::{"min_scale": 0.1, "max_scale": 2.0, "target_height": 224, "target_width": 320}',
      "RandomFlipOp",
      'FixedSizeCropOp::{"crop_size": [224, 320]}',
    ]
    TEST: ["ResizeShortestEdgeOp"]
INPUT:
  RECOMPUTE_BOXES: True
  MAX_SIZE_TEST: 320
  MAX_SIZE_TRAIN: 320
  MIN_SIZE_TEST: 224
  MIN_SIZE_TRAIN: (224,)
VERSION: 2