---
license: other
license_name: repcount-academic
license_link: https://svip-lab.github.io/dataset/RepCount_dataset.html
language:
  - en
tags:
  - video
  - repetitive-action-counting
  - action-recognition
pretty_name: RepCount-A (playable MP4)
size_categories:
  - 1K<n<10K
---

# RepCount-A — playable MP4 package

RepCount Part-A (LLSP) repackaged as plain **H.264 MP4 files** plus per-split JSON
annotations, for direct use in video-LLM evaluation pipelines and standard players.

- **1,041 videos** — train 758 / validation 131 / test 152 (~9 GB, mostly 360p, native aspect)
- Fine-grained annotations: repetition `count` + per-cycle boundary frame indices (`cycle_bounds`)

## Structure

```
train/                       758 .mp4
validation/                  131 .mp4
test/                        152 .mp4
train_annotations.json
validation_annotations.json
test_annotations.json
```

Each annotation entry:

```json
{
  "video": "stu10_0.mp4",
  "video_id": "stu10_0",
  "source_name": "stu10_0.mp4",
  "action_type": "pull_up",
  "count": 14,
  "cycle_bounds": [1, 23, 23, 40, ...]
}
```

`cycle_bounds` is a flat list of frame indices marking the start/end of each action
cycle (pairs), as annotated by the original authors. Standard metrics: **MAE** and
**OBO** (off-by-one accuracy).

## Provenance

Videos and annotations originate from the official RepCount release
(`RepCountA.tar.gz`, LLSP structure), obtained via the
[lmms-lab-eval/repcounta-lance](https://huggingface.co/datasets/lmms-lab-eval/repcounta-lance)
mirror; video bytes are extracted as-is (no re-encoding). Annotation frame indices
were spot-verified against decoded frame counts.

## License / attribution

RepCount is released for **academic use** by its authors. If you use this data, cite:

```bibtex
@inproceedings{hu2022transrac,
  title={TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting},
  author={Hu, Huazhang and Dong, Sixun and Zhao, Yiqun and Lian, Dongze and Li, Zhengxin and Gao, Shenghua},
  booktitle={CVPR},
  year={2022}
}
```

Official dataset page: https://svip-lab.github.io/dataset/RepCount_dataset.html