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faster_rcnn_fbnetv3a_C4.yaml
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faster_rcnn_fbnetv3a_C4.yaml
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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
INPUT:
MAX_SIZE_TEST: 320
MAX_SIZE_TRAIN: 320
MIN_SIZE_TEST: 224
MIN_SIZE_TRAIN: (224,)
VERSION: 2