> This is the documentation for LibreYOLO 1.6.0. Current version: https://www.libreyolo.com/docs/models/hvi-cidnet

# HVI-CIDNet
HVI-CIDNet restores low-light images through hue, saturation and intensity processing.
Tasks: Restoration. Install: pip install libreyolo.
Verified against LibreYOLO v1.6.0.

## Install

```bash
pip install "libreyolo"
```

## Predict

**Python**

```python
from libreyolo import LibreYOLO, SAMPLE_IMAGE

model = LibreYOLO("LibreHVICIDNett-restore.pt", device="cpu")
result = model(SAMPLE_IMAGE, gamma=1.0, saturation=1.0, intensity=1.0)
result.restored.save("enhanced.png")
```

`gamma`, `saturation` and `intensity` each default to 1.0. Validation uses paired image data. Training and export are not supported.


## Validate

**Python**

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreHVICIDNett-restore.pt", device="cpu")
# Paired restoration dataset YAML: degraded images in inputs/, clean targets in targets/, matched by file stem.
metrics = model.val(data="path/to/your/restore.yaml", workers=0)
print(metrics)
```

Use the dataset format for this task. [Validation](/docs/v1.6.0/train/validation) explains the dataset requirements and returned metrics.

## Checkpoints

| File | Input (px) | Task | Weights license |
| --- | --- | --- | --- |
| `LibreHVICIDNett-restore.pt` |  | Restoration | mit |

## Licensing

Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice.

- Original work: HVI-CIDNet, HVI-CIDNet authors
- Upstream license: MIT
- Upstream source: https://github.com/Fediory/HVI-CIDNet
- LibreYOLO code: MIT
- Weights: MIT, republished at https://huggingface.co/LibreYOLO
- Interpretation: The publisher declares MIT for the model and generalization checkpoint.

## Citation

```bibtex
@article{yan2025hvi,
  title={HVI: A New color space for Low-light Image Enhancement},
  author={Yan, Qingsen and Feng, Yixu and Zhang, Cheng and Pang, Guansong and Shi, Kangbiao and Wu, Peng and Dong, Wei and Sun, Jinqiu and Zhang, Yanning},
  journal={arXiv preprint arXiv:2502.20272},
  year={2025}
}
```

Copied from https://raw.githubusercontent.com/Fediory/HVI-CIDNet/master/Readme.md


## HVI-CIDNet architecture

- Tiny: [SVG](/diagrams/models/hvi-cidnet/t-restore.svg), [interactive diagram](/diagrams/models/hvi-cidnet/t-restore.html)