57 lines
1.8 KiB
YAML
57 lines
1.8 KiB
YAML
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Ground 2D Detection Dataset for Mono3D
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# Custom annotation format with difficulty scores and class mapping
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: /mnt/nfs/mono3d/ydong_data/Detection/Detection2D_20260427 # dataset root dir
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train: /mnt/nfs/mono3d/ydong_data/Detection/Detection2D_20260427/train.txt # train images
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val: /mnt/nfs/mono3d/ydong_data/Detection/Detection2D_20260427/val.txt # val images
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test: # test images (optional)
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# Class mapping: string class names to numeric IDs
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# Format: class_name: class_id (allows easy merging, e.g., car: 0, van: 0)
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class_map:
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car: 0
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suv: 1
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pickup: 2
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medium_car: 3
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van: 4
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bus: 5
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truck: 6
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tanker: 6
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large_truck: 6
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construction_vehicle: 6
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special_vehicle: 7
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unknown: 8
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pedestrian: 9
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bicyclist: 10
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motorcyclist: 10
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bicycle: 11
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motorcycle: 11
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tricycle: 12
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tricyclist: 12
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traffic_sign: 13
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wheel: 14
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plate: 15
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face: 16
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car_fake: 17
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bicyclist_fake: 18
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pedestrian_fake: 19
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car_carrier: 6
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platform_truck: 6
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# Training parameters
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min_wh: 8.0 # Keep boxes whose width or height is at least this many pixels
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# Recommended: 2 * smallest_stride (2 * 8 = 16) for network detectability
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# Color space of input images
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use_yuv444: false # Convert YUV444 to BGR in dataloader (BT.601 full range)
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# Label file format (7 columns):
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# [class_name x_center y_center width height difficulty1 difficulty2]
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# Difficulty-based loss weighting: difficulty_weights 设计上是给 0/1/2/3 难度目标配置权重的，但当前 Ground 2D 检测的 box/cls/dfl loss 没有实际按它加权；当前 difficulty 主要作为额外 difficulty 二分类监督使用
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difficulty_weights: [1.0, 1.0, 0.7, 0.3]
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