| --- |
| 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. |
