Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
53 lines
1.4 KiB
YAML
53 lines
1.4 KiB
YAML
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Dogs dataset http://vision.stanford.edu/aditya86/ImageNetDogs/ by Stanford
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# Documentation: https://docs.ultralytics.com/datasets/pose/dog-pose/
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# Example usage: yolo train data=dog-pose.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── dog-pose ← downloads here (337 MB)
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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: dog-pose # dataset root dir
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train: images/train # train images (relative to 'path') 6773 images
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val: images/val # val images (relative to 'path') 1703 images
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# Keypoints
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kpt_shape: [24, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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# Classes
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names:
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0: dog
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# Keypoint names per class
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kpt_names:
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0:
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- front_left_paw
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- front_left_knee
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- front_left_elbow
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- rear_left_paw
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- rear_left_knee
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- rear_left_elbow
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- front_right_paw
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- front_right_knee
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- front_right_elbow
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- rear_right_paw
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- rear_right_knee
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- rear_right_elbow
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- tail_start
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- tail_end
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- left_ear_base
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- right_ear_base
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- nose
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- chin
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- left_ear_tip
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- right_ear_tip
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- left_eye
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- right_eye
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- withers
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- throat
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# Download script/URL (optional)
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download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dog-pose.zip
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