MODEL: META_ARCHITECTURE: "GeneralizedRCNN" BACKBONE: NAME: "build_resnet_fpn_backbone" RESNETS: OUT_FEATURES: ["res2", "res3", "res4", "res5"] FPN: IN_FEATURES: ["res2", "res3", "res4", "res5"] ANCHOR_GENERATOR: SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) RPN: IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level PRE_NMS_TOPK_TEST: 1000 # Per FPN level # Detectron1 uses 2000 proposals per-batch, # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. POST_NMS_TOPK_TRAIN: 1000 POST_NMS_TOPK_TEST: 1000 ROI_HEADS: NAME: "StandardROIHeads" IN_FEATURES: ["p2", "p3", "p4", "p5"] ROI_BOX_HEAD: NAME: "FastRCNNConvFCHead" NUM_FC: 2 POOLER_RESOLUTION: 7 ROI_MASK_HEAD: NAME: "MaskRCNNConvUpsampleHead" NUM_CONV: 4 POOLER_RESOLUTION: 14 DATASETS: TRAIN: ("coco_2017_train",) #TEST: ("coco_2017_val",) TEST: ("coco_2017_test-dev",) SOLVER: IMS_PER_BATCH: 16 #16 BASE_LR: 0.02 STEPS: (60000, 80000) MAX_ITER: 90000 INPUT: MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) VERSION: 2