La interfaz está localizada; las descripciones y documentos de las Skills se traducen automáticamente con IA a su idioma. El código, los comandos, rutas y términos técnicos se mantienen en el original, y prevalece el documento del autor.
Explorar Skills
Datos e investigación · SKILL.md

datasets

datasets

Validate and troubleshoot InternVideo dataset layouts, annotation schemas, and data-readiness across generations.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: datasets description: "Validate and troubleshoot InternVideo dataset layouts, annotation schemas, and data-readiness across generations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

InternVideo datasets operating skill

Use this sub-skill when the task is about InternVid, instruction data, InternVideo2/3/Next dataset preparation, annotation JSON/JSONL/list schemas, path readiness, benchmark data caveats, or safe preflight validation before training/evaluation.

Read order

  1. references/data-formats.md to identify the expected format for the target generation and workflow.
  2. Run scripts/validate_internvideo_annotations.py --help and then validate the user's annotation file(s) with the closest format mode.
  3. references/troubleshooting.md when a loader reports missing files, empty datasets, placeholder/token-count assertions, shared-memory/cache failures, or benchmark-data gaps.

Safe validation workflow

  1. Identify workflow: InternVid search metadata, InternVideo2 single-modality list, InternVideo2 multi-modality JSON, InternVideo3 SFT meta/JSONL, InternVideo-Next pretraining list, or benchmark/evaluation data.
  2. Validate syntax and schema first with the bundled script. Use --check-paths only when the user wants local path existence checks and has provided the relevant --media-root.
  3. Report counts, errors, warnings, and any assumptions. Do not download datasets or touch object storage without approval.
  4. Hand off validated data facts to the requesting sub-skill (video-mllm, next-pretraining, single-modality, or multi-modality).

Bundled validator examples

python scripts/validate_internvideo_annotations.py \
  --format internvideo3-meta <annotation-meta.json> --follow-meta --max-records 100
python scripts/validate_internvideo_annotations.py \
  --format internvideo2-json <annotations.json> --expect-media-type video --media-root <media-root> --check-paths
python scripts/validate_internvideo_annotations.py \
  --format pretrain-list <pretraining-list.txt> --media-root <media-root> --check-paths

Resolve scripts/validate_internvideo_annotations.py relative to this datasets sub-skill directory when copying the generated skill into another environment. The script itself has no dependency on the original repository checkout.

Guardrails

  • Treat public dataset names as identifiers, not proof that data are present locally.
  • YouTube-derived datasets can have dead/missing videos; validation should distinguish annotation syntax from media availability.
  • Do not assume JSON and JSONL are interchangeable: InternVideo2 loaders commonly use JSON arrays, while InternVideo3 SFT uses JSONL.
  • Do not classify full benchmark acquisition, checkpoint download, or video decoding of large corpora as required static verification.
Skills guardadas