25 lines
691 B
YAML
25 lines
691 B
YAML
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MODEL:
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META_ARCHITECTURE: "RetinaNet"
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BACKBONE:
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NAME: "build_retinanet_resnet_fpn_backbone"
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RESNETS:
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OUT_FEATURES: ["res3", "res4", "res5"]
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ANCHOR_GENERATOR:
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SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"]
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FPN:
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IN_FEATURES: ["res3", "res4", "res5"]
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RETINANET:
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IOU_THRESHOLDS: [0.4, 0.5]
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IOU_LABELS: [0, -1, 1]
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DATASETS:
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TRAIN: ("coco_2017_train",)
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TEST: ("coco_2017_val",)
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SOLVER:
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IMS_PER_BATCH: 16
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BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate
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STEPS: (60000, 80000)
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MAX_ITER: 90000
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INPUT:
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MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
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VERSION: 2
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