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- MODEL:
- META_ARCHITECTURE: "RetinaNet"
- BACKBONE:
- NAME: "build_retinanet_resnet_fpn_backbone"
- RESNETS:
- OUT_FEATURES: ["res3", "res4", "res5"]
- ANCHOR_GENERATOR:
- SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"]
- FPN:
- IN_FEATURES: ["res3", "res4", "res5"]
- RETINANET:
- IOU_THRESHOLDS: [0.4, 0.5]
- IOU_LABELS: [0, -1, 1]
- SMOOTH_L1_LOSS_BETA: 0.0
- DATASETS:
- TRAIN: ("coco_2017_train",)
- TEST: ("coco_2017_val",)
- SOLVER:
- IMS_PER_BATCH: 16
- BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate
- STEPS: (60000, 80000)
- MAX_ITER: 90000
- INPUT:
- MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
- VERSION: 2
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