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Upload Models

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model_config.yaml ADDED
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+ # https://github.com/facebookresearch/detectron2/blob/main/configs/Base-RCNN-FPN.yaml
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+ # https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml
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+ MODEL:
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+ MASK_ON: True
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+ PIXEL_STD: [57.375, 57.120, 58.395]
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+ RESNETS:
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+ STRIDE_IN_1X1: False # this is a C2 model
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+ NUM_GROUPS: 32
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+ WIDTH_PER_GROUP: 8
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+ DEPTH: 101
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+ OUT_FEATURES: ["res2", "res3", "res4", "res5"]
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+ META_ARCHITECTURE: "GeneralizedRCNN"
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+ BACKBONE:
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+ NAME: "build_resnet_fpn_backbone"
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+ FPN:
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+ IN_FEATURES: ["res2", "res3", "res4", "res5"]
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+ ANCHOR_GENERATOR:
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+ SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
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+ ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
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+ RPN:
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+ IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
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+ PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
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+ PRE_NMS_TOPK_TEST: 1000 # Per FPN level
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+ # Detectron1 uses 2000 proposals per-batch,
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+ # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
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+ # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
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+ POST_NMS_TOPK_TRAIN: 1000
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+ POST_NMS_TOPK_TEST: 1000
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+ ROI_HEADS:
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+ NAME: "StandardROIHeads"
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+ IN_FEATURES: ["p2", "p3", "p4", "p5"]
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+ ROI_BOX_HEAD:
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+ NAME: "FastRCNNConvFCHead"
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+ NUM_FC: 2
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+ POOLER_RESOLUTION: 7
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+ ROI_MASK_HEAD:
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+ NAME: "MaskRCNNConvUpsampleHead"
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+ NUM_CONV: 4
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+ POOLER_RESOLUTION: 14
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+ DATASETS:
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+ TRAIN: ("Metadata(name='wing_train', thing_classes=['wing'])",)
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+ TEST: ("Metadata(name='wing_val', thing_classes=['wing'])",)
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+ SOLVER:
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+ IMS_PER_BATCH: 16
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+ BASE_LR: 0.02
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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
perching_model_final.pth ADDED
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+ size 856189760
standard_model_final.pth ADDED
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