技能创建器
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创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
data-preparation【数据与研究】Guides SimpleDet annotation conversion and roidb validation for COCO-like, VOC, CrowdHuman, and custom JSON detection or instance-segmentation data.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this route for annotation conversion, data/cache/*.roidb, image layouts,
class IDs, polygon masks, split names, or validation. Read
data-formats.md before conversion and
troubleshooting.md after a failure.
--output; it never downloads data.DatasetParam.image_set.gt_poly, polygon lengths, and static config limits.gt_class uses positive foreground IDs; 0 is background and CrowdHuman
ignore regions use -2.gt_bbox is (N,4) xyxy, not COCO xywh.flipped should be false in cached records; loader-time augmentation flips.image_url, positive h/w, and float32-safe im_id are required.python <skill-root>/sub-skills/data-preparation/scripts/convert_roidb.py --help
python <skill-root>/sub-skills/data-preparation/scripts/validate_roidb.py --help
python <skill-root>/sub-skills/data-preparation/scripts/validate_roidb.py --input data/cache/custom.roidb --check-images --max-records 3
The converter supports --format json|voc|coco|crowdhuman; it requires only
its format-specific public dependencies and writes only the explicit output.
The validator is read-only and can check JSON/JSONL without NumPy; pickle
roidbs require NumPy.
Read workflows.md for format-specific commands and cache handoff details.