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source · 2026-04-25

DeepLabCut（GitHub repository）：DeepLabCut 是少量标注即可训练的无标记关键点追踪工具。

#near-cvpr-2025#video-understanding#secondary-source

DeepLabCut（GitHub repository）：DeepLabCut 是少量标注即可训练的无标记关键点追踪工具。

资料来源：GitHub repository
发表 / 发布日期：2023-01-01
资料加入日期：2026-04-25

一句话结论

DeepLabCut 是少量标注即可训练的无标记关键点追踪工具。

资料定位

这是从体育机器视觉文献清单中拆出的独立来源。当前 first-pass 编译先把它放进 raw/source 证据层，后续可按重要性升级为深分析笔记。

对体育 AI 子线的价值

它适合体育小样本关键点、器械追踪和研究型标注流程。

局限或疑问

  • 当前是 bibliography-driven first-pass source note，优先保证来源可追溯和方向定位。
  • 重要 paper 后续应补 analysis.md、关键图页、实验设置和指标细节。

原始材料

  • raw/ingest/2026-04-25-deeplabcut/source.md
  • raw/ingest/2026-04-25-deeplabcut/paper-text.md
  • raw/ingest/2026-04-25-deeplabcut/links.yaml

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Metadata

{
  "id": "2026-04-25-deeplabcut",
  "type": "source",
  "title": "DeepLabCut（GitHub repository）：DeepLabCut 是少量标注即可训练的无标记关键点追踪工具。",
  "status": "reviewed",
  "created": "2026-04-25",
  "updated": "2026-04-25",
  "venue": "GitHub repository",
  "published_at": "2023-01-01",
  "ingested_at": "2026-04-25",
  "tags": [
    "near-cvpr-2025",
    "video-understanding",
    "secondary-source"
  ],
  "note_status": "reviewed",
  "source_type": "repository",
  "authors": [],
  "canonical_links": [
    "https://github.com/deeplabcut/deeplabcut"
  ],
  "raw_entry": "raw/ingest/2026-04-25-deeplabcut/",
  "topics": [
    "topics/sports-ai-video-understanding",
    "topics/sports-ai-roadmap",
    "topics/video-understanding"
  ],
  "entities": [
    "entities/sportsmot"
  ],
  "claims": [],
  "questions": []
}