L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

data-preparation

data-preparation

Guides SimpleDet annotation conversion and roidb validation for COCO-like, VOC, CrowdHuman, and custom JSON detection or instance-segmentation data.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: data-preparation description: "Guides SimpleDet annotation conversion and roidb validation for COCO-like, VOC, CrowdHuman, and custom JSON detection or instance-segmentation data." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data preparation

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.

Workflow

  1. Choose COCO-like JSON, VOC XML, CrowdHuman ODGT, or custom JSON.
  2. Confirm the image/annotation layout and exact split names.
  3. Use the bundled convert_roidb.py with an explicit --output; it never downloads data.
  4. Run validate_roidb.py on a tiny result before using the cache for a model workflow.
  5. Match the output basename to DatasetParam.image_set.
  6. For masks, validate gt_poly, polygon lengths, and static config limits.

Data contract

  • 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.
  • Mask records need one raw polygon-list entry per instance.

Bundled helpers

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.

Route onward

Read workflows.md for format-specific commands and cache handoff details.

Skills enregistrées