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。
datasets【数据与研究】Validate and troubleshoot InternVideo dataset layouts, annotation schemas, and data-readiness across generations.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
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.
references/data-formats.md to identify the expected format for the target generation and workflow.scripts/validate_internvideo_annotations.py --help and then validate the user's annotation file(s) with the closest format mode.references/troubleshooting.md when a loader reports missing files, empty datasets, placeholder/token-count assertions, shared-memory/cache failures, or benchmark-data gaps.--check-paths only when the user wants local path existence checks and has provided the relevant --media-root.video-mllm, next-pretraining, single-modality, or multi-modality).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.