---
title: SVHighlights
canonical_url: "https://www.modelscope.cn/datasets/UltraDoughnut/SVHighlights"
md_url: "https://www.modelscope.cn/datasets/UltraDoughnut/SVHighlights.md"
repository: UltraDoughnut/SVHighlights
last_updated: 2026-10-22
license: Apache-2.0
storage_size: "530 GB"
downloads: 181
stars: 0
---

# SVHighlights

> SVHighlights - UltraDoughnut 在 ModelScope 开源的数据集。Mirror note. This ModelScope copy additionally includes the raw YouTube videos referenced by metadata/videolist.csv, downloaded with yt-dlp (720p mp4) as videos/ .mp4 (635 of 638 unique IDs). Missing because…

UltraDoughnut/SVHighlights 是 ModelScope 魔搭社区上的数据集，存储大小 530 GB，采用 Apache-2.0 许可。

- **Repository**: UltraDoughnut/SVHighlights
- **License**: Apache-2.0
- **Storage size**: 530 GB
- **Downloads**: 181
- **Stars**: 0
- **Last updated**: 2026-10-22

Source: https://www.modelscope.cn/datasets/UltraDoughnut/SVHighlights

---

> **Mirror note.** This ModelScope copy additionally includes the raw YouTube videos referenced by `metadata/video_list.csv`, downloaded with yt-dlp (720p mp4) as `videos/<youtube_id>.mp4` (635 of 638 unique IDs). Missing because YouTube reports them unavailable: `107IBonma_o`, `21fJiYJNcik`, `a5_Eqony5RE`. Use `video_map.csv` to map `vid` (e.g. `soccer_5`) to the full-broadcast and highlight YouTube IDs and trim windows. Videos remain subject to their publishers' terms; annotations are CC BY-NC 4.0 from the original authors (research, non-commercial only). Original dataset: https://huggingface.co/datasets/idong1004/SVHighlights


# SVHighlights: Towards Extremely Long Sport Video Highlight Detection

[![arXiv](https://img.shields.io/badge/arXiv-2606.06926-b31b1b.svg)](https://arxiv.org/abs/2606.06926)
[![KDD 2026](https://img.shields.io/badge/KDD-2026-1f6feb.svg)](https://doi.org/10.1145/3770855.3817564)
[![GitHub](https://img.shields.io/badge/GitHub-SVHighlights-181717.svg?logo=github)](https://github.com/leedongkyu2019/SVHighlights)
[![Project Page](https://img.shields.io/badge/Project_Page-Online-2ea44f.svg)](https://leedongkyu2019.github.io/SVHighlights/)
[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC_BY--NC_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)

**Donggyu Lee\*, Youngbin Ki\*, Jeonghun Kang, Taehwan Kim** — UNIST
KDD 2026 · Datasets & Benchmarks Track (*equal contribution)

<p align="center">
<img src="https://raw.githubusercontent.com/leedongkyu2019/SVHighlights/main/assets/teaser.png" width="60%">
</p>

SVHighlights is the first highlight-detection benchmark for **extremely long
sports videos** — **320 full-length broadcasts** averaging **2.00 hours**
across **8 sports** (40 videos each: american football, baseball, basketball,
ice hockey, racing, rugby, soccer, volleyball), totaling **640.18 hours** —
roughly 30–60× longer than prior highlight-detection benchmarks. We do **not**
redistribute the original videos; per-clip `{0, 1}` highlight labels are
distilled by aligning each official highlight video to its full broadcast via
pixel-level PSNR matching. This release contains QVHighlights-style features
and all preprocessing artefacts needed to reproduce or extend the benchmark.

## 📦 Contents

```
annotations/
  alignment.tar                 # per-video PSNR alignment outputs    -> alignment/
  segments.tar                  # context-aware segments (TF-SELECTOR) -> segments/
  whisper.tar                   # word-level WhisperX transcripts     -> whisper/
  shots.tar                     # shot boundaries                     -> shots/
  label.json                    # per-clip {0, 1} highlight labels (eval GT)
  all_filtered_frame_idx.json   # final aligned frame index per highlight clip
  segment_caption.json          # VLM segment captions
  volume.json, minmax_volume.json
features/
  <sport>.tar                   # one tarball per sport (~1.5–2.5 GB each)
                                #   extracts to <sport>/{vid_clip,vid_slowfast,txt_clip,aud_pann}/
```

Directory-style annotations and features are packed as tarballs so the dataset
downloads as a few large objects instead of thousands of small files. Video
URLs and per-video trim boundaries are released as `video_list.csv` in the
[GitHub repo](https://github.com/leedongkyu2019/SVHighlights).

## ⬇️ Download

```bash
# 1) Download (use --max-workers 1 to stay under HF's anonymous API rate limit)
huggingface-cli download idong1004/SVHighlights \
  --repo-type dataset --local-dir ./data --max-workers 1

# 2) Extract the annotation tarballs in place
for a in alignment segments whisper shots; do
  tar -xf data/annotations/${a}.tar -C data/annotations/ && rm data/annotations/${a}.tar
done

# 3) Extract the per-sport feature tarballs in place
for s in american_football baseball basketball ice_hockey race rugby soccer volleyball; do
  tar -xf data/features/${s}.tar -C data/features/ && rm data/features/${s}.tar
done
```

## 📜 License & video terms

The SVHighlights annotations and extracted features are released under
**[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)** —
free for non-commercial research with attribution. The accompanying code
on [GitHub](https://github.com/leedongkyu2019/SVHighlights) is released
separately under **Apache 2.0**. The original sports broadcasts are **not**
redistributed; `video_list.csv` provides public URLs only, and use of the
source videos is subject to the original publishers' terms of service.

## 📚 Citation

```bibtex
@inproceedings{Lee_2026,
  title={SVHighlights: Towards Extremely Long Sport Video Highlight Detection},
  url={http://dx.doi.org/10.1145/3770855.3817564},
  DOI={10.1145/3770855.3817564},
  booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
  publisher={ACM},
  author={Lee, Donggyu and Ki, Youngbin and Kang, Jeonghun and Kim, Taehwan},
  year={2026},
  month=Aug,
  pages={9254–9265}
}
```