---
language:
- en
pretty_name: LLVIP Infrared Pedestrian Detection Dataset (Adapter Only, No Mirror)
task_categories:
- object-detection
tags:
- object-detection
- infrared
- low-light
- pedestrian-detection
- autonomous-driving
- computer-vision
- ultralytics
- yolo
---
# LLVIP: Visible-Infrared Paired Low-light Vision Dataset (No Data Mirror -- Adapter Only)
> **This card describes a [DetectionBench](https://github.com/dronefreak/DetectionBench) dataset adapter for LLVIP. It does NOT host or redistribute the dataset itself -- LLVIP has an explicit non-commercial license with no redistribution grant. See [Getting the Data](#getting-the-data) for the official download links.**
## Dataset Description
- **Homepage:** https://bupt-ai-cz.github.io/LLVIP/
- **Repository:** https://github.com/bupt-ai-cz/LLVIP
- **Paper:** https://arxiv.org/abs/2108.10831
- **Point of Contact:** czhu@bupt.edu.cn
## Disclaimer
DetectionBench is **not** an official release of LLVIP and does not host any LLVIP images, annotations, or derived files anywhere -- not on Hugging Face, not in this repository.
LLVIP was created by Xinyu Jia, Chuang Zhu, Minzhen Li, Wenqi Tang, and Wenli Zhou (Beijing University of Posts and Telecommunications), who retain all rights under an explicit, formal license (the official repository's `Term of Use and License.md`, not just an informal statement). This repository does **not** claim ownership of any images or annotations.
What this repository provides instead:
1. A [DetectionBench dataset adapter](https://github.com/dronefreak/DetectionBench/blob/main/src/detectionbench/datasets/llvip.py) that converts an official LLVIP download into DetectionBench's canonical training layout, once you have obtained the data yourself.
2. A helper command, `detectionbench-download-dataset --dataset llvip`, that prints the official download links (Google Drive and Baidu Netdisk) and can fetch the Google Drive copy automatically via `gdown` -- see [Getting the Data](#getting-the-data).
3. This banner, generated locally from an LLVIP copy already converted through the adapter, purely to illustrate the dataset's domain and annotation style.
---
# Dataset Overview
LLVIP is a registered visible/infrared image-pair dataset for pedestrian detection in low-light conditions: 30,976 image pairs (paired visible + infrared frames, pixel-aligned) captured at night, annotated with pedestrian bounding boxes. Its central finding is that infrared imagery is dramatically more useful than visible-light imagery for detection under these conditions -- the paired visible frames are frequently near-black.
**This adapter uses the infrared images only.** Building a joint RGB+IR detector is a legitimate, separate research direction that DetectionBench's single-image training/eval pipeline does not attempt; the adapter picks the modality that is independently usable for standard detection.
---
# Getting the Data
LLVIP is **not** mirrored here. Get it directly from the authors:
```bash
detectionbench-download-dataset --dataset llvip
```
| Resource | Link |
|---|---|
| LLVIP dataset (Google Drive) | https://drive.google.com/file/d/1VTlT3Y7e1h-Zsne4zahjx5q0TK2ClMVv/view |
| LLVIP dataset (Baidu Netdisk, access code `14lc`) | https://pan.baidu.com/s/1eQO1Is2NPyd-mgmv1Csbfg |
Once downloaded, convert it into DetectionBench's canonical layout:
```bash
detectionbench-prepare-coco --dataset llvip --raw-dir --output-dir
detectionbench-convert-coco-to-yolo --input-dir --output-dir
```
LLVIP ships with no official validation split; the adapter carves a seeded 15% slice out of train.
**Note:** the adapter expects `infrared/{train,test}/*.jpg` + `Annotations/*.xml` under the raw directory, matching the official README's documented layout -- verified against a real download: 10,221 train / 1,804 valid (15% carve of the official 12,025 train images) / 3,463 test, 29,113 / 5,017 / 8,302 `person` boxes respectively.
### Classes (1)
`person`
---
# Dataset Sources
## Original Paper
**LLVIP: A Visible-infrared Paired Dataset for Low-light Vision**
Xinyu Jia, Chuang Zhu, Minzhen Li, Wenqi Tang, Wenli Zhou
IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021.
## Official Resources
- **GitHub Repository:** https://github.com/bupt-ai-cz/LLVIP
- **Homepage:** https://bupt-ai-cz.github.io/LLVIP/
---
# License
**Non-commercial use only**, per the official `Term of Use and License.md`:
- Free for academic and non-academic entities for non-commercial purposes (research, teaching, publications, personal experimentation).
- Attribution required.
- **No commercial use** of the dataset or derivative work.
- Faces are blurred in places; using the dataset to identify or invade any individual's privacy is explicitly prohibited.
- The license reserves "all rights not expressly granted" -- it does not grant third-party redistribution of the raw dataset, only conditions for using derivative *annotations*.
Accordingly:
- No Hugging Face mirror of the data is provided or planned.
- The DetectionBench adapter is provided for local, non-commercial research use against a copy you download yourself.
- If you need broader rights, contact the original authors.
---
# Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{jia2021llvip,
title={LLVIP: A Visible-infrared Paired Dataset for Low-light Vision},
author={Jia, Xinyu and Zhu, Chuang and Li, Minzhen and Tang, Wenqi and Zhou, Wenli},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops},
pages={3496--3504},
year={2021}
}
```
---
# Acknowledgements
We sincerely thank Xinyu Jia, Chuang Zhu, and their co-authors for creating and publicly releasing this valuable low-light visible-infrared benchmark.