---
pretty_name: DGFF (Detection-Guided Feature Feedback)
license: mit
language:
  - en
pipeline_tag: image-to-image
datasets:
  - abbaab/DGFF-dataset
tags:
  - low-light
  - image-enhancement
  - object-detection
  - feature-feedback
  - yolov8
  - unet
---

# DGFF: Detection-Guided Feature Feedback

Model repository for **"End-to-End Task-Oriented Low-Light Image Enhancement via Detection-Guided Feature Feedback"** (ICCA 2026).

> The detector teaches the enhancer what to preserve, and the gated adapters learn to pass only the useful lessons.

| Resource | Link |
|---|---|
| Code | <https://github.com/ShuvroSankar/DGFF> |
| Data (LOL + ExDark) | <https://huggingface.co/datasets/abbaab/DGFF-dataset> |

## About DGFF

DGFF is a training framework that routes intermediate **YOLOv8n** backbone features (P3, P4, P5) back into the decoder of a U-Net low-light enhancer (**LLEN**) through lightweight **gated 1x1 adapters**. The detector's view of what matters shapes the reconstruction, so enhancement preserves edges and textures that detectors rely on. The adapters are discarded at inference, so DGFF adds **zero overhead** over a plain LLEN + detector pipeline.

| Component | Parameters |
|---|---|
| LLEN (U-Net) | 4,844,803 |
| DGFF adapters (training only) | 86,464 |
| YOLOv8n backbone (frozen) | 1,272,656 |

## Headline results

| Benchmark | Metric | Result |
|---|---|---|
| LOL (validation) | PSNR / SSIM | 19.83 dB / 0.9048 |
| ExDark (domain-matched) | mAP@0.5 | 0.474 (raw baseline 0.408, LLEN-only 0.469) |
| ExDark (domain-matched) | mAP@0.5:0.95 | 0.280 (raw baseline 0.232, LLEN-only 0.276) |

The +0.5-point mAP@0.5 gain over LLEN-only is directionally consistent but not statistically significant (paired bootstrap, 95% CI [-0.0065, 0.0112]). The +6.6-point gain over the unenhanced baseline is clear. Full details are in the paper and the GitHub README.

## Contents

<!-- TODO: list the files actually stored in this repository. Example table below. -->

| File | Description |
|---|---|
| `README.md` | This card |
| *(to add)* | LLEN checkpoint after Phase 1 (LOL, best epoch 185) |
| *(to add)* | DGFF adapter weights after Phase 2 (ExDark fine-tuning) |

## How to use

1. Download the weights from this repository (`huggingface-cli download abbaab/DGFF --local-dir ./weights`).
2. Clone the code: `git clone https://github.com/ShuvroSankar/DGFF.git`
3. Download the data from [abbaab/DGFF-dataset](https://huggingface.co/datasets/abbaab/DGFF-dataset).
4. Follow the training and evaluation steps in the GitHub README.

At inference only LLEN is needed: enhance the image, then run any detector (the paper uses YOLOv8n).

## Limitations

- Trained on LOL (indoor, 485 pairs) and fine-tuned on ExDark, so an indoor/outdoor domain gap remains.
- Evaluated with YOLOv8n only; other detectors are untested.
- Classes with small, occluded or homogeneous-texture objects (Chair, Motorbike, People, Table) did not improve over LLEN-only.

## Citation

```bibtex
@inproceedings{sen2026dgff,
  title     = {End-to-End Task-Oriented Low-Light Image Enhancement via Detection-Guided Feature Feedback},
  author    = {Sen, Shuvro Sankar and Mia, MD. Maruf and Hasan, Naim and Chayon, Muhammad Hasibur Rashid},
  booktitle = {Proceedings of The 4th International Conference on Computing Advancements (ICCA 2026)},
  year      = {2026},
  address   = {Dhaka, Bangladesh},
  publisher = {ACM}
}
```

## Authors

Shuvro Sankar Sen, MD. Maruf Mia, Naim Hasan, Muhammad Hasibur Rashid Chayon, American International University - Bangladesh (AIUB).

## Acknowledgements

Trained and evaluated on [LOL](https://daooshee.github.io/BMVC2018website/) (Wei et al., 2018) and [ExDark](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset) (Loh and Chan, 2019), using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics). Dataset terms of the original sources apply.