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paa_dcnv2_X_101_64x4d_FPN_2x.yaml
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paa_dcnv2_X_101_64x4d_FPN_2x.yaml
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-64x4d"
RPN_ONLY: True
PAA_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
PAA:
ANCHOR_SIZES: (64, 128, 256, 512, 1024) # 8S
ASPECT_RATIOS: (1.0,)
SCALES_PER_OCTAVE: 1
USE_DCN_IN_TOWER: True
TOPK: 9 # topk for selecting candidate positive samples from each level
IOU_THRESHOLD: 0.1
REG_LOSS_WEIGHT: 1.3
USE_IOU_PRED: True
IOU_LOSS_WEIGHT: 0.5
INFERENCE_SCORE_VOTING: True
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
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
VOTE: True
VOTE_TH: 0.66
MERGE_TYPE: "soft-vote"
H_FLIP: True
SCALES: (400, 500, 600, 640, 700, 900, 1000, 1100, 1200, 1300, 1400, 1800)
SCALE_RANGES: [[96, 10000], [96, 10000], [64, 10000], [64, 10000], [64, 10000], [0, 10000], [0, 10000], [0, 256], [0, 256], [0, 192], [0, 192], [0, 96]]
MAX_SIZE: 3000
SCALE_H_FLIP: True