Awesome
Enhancing representation of a low-light image.
<div align="center"> <table> <td> <img src="docs/assets/enhanced_representation.gif" height="800"> </td> </table> </div>Installation
Please refer to low-light-object-detection-detectron2 for installation requirements
Datasets
ExDark
Create a new folder named "exdark" in the "low-light-object-detection-detectron2/data" folder. Create a new folder named "exdark" in the "low-light-object-detection-mmdetection/data" folder.
Download the ExDark dataset and copy the images into "low-light-object-detection-detectron2/data/exdark/images/" and "low-light-object-detection-mmdetection/data/exdark/images/" folders.
DARK FACE
Create a new folder named "darkface" in the "low-light-object-detection-detectron2/data" folder. Create a new folder named "darkface" in the "low-light-object-detection-mmdetection/data" folder.
Download the DARK FACE dataset and copy the images into "low-light-object-detection-detectron2/data/darkface/images/" and "low-light-object-detection-mmdetection/data/darkface/images/" folders.
Train
To train the ExDark and DARK FACE using FeatEnHancer based Featurized Query R-CNN run the following commands: The training utilizes 2 GPU's
sh low-light-object-detection-detectron2/train_exdark.sh
sh low-light-object-detection-detectron2/train_darkface.sh
To train the ExDark and DARK FACE using FeatEnHancer based RetinaNet run the following commands: The training utilizes 6 GPU's
sh low-light-object-detection-mmdetection/exec_script_exdark.sh
sh low-light-object-detection-mmdetection/exec_script_darkface.sh
Results and Checkpoints
ExDark
| Model | mAP | Config |
|---|---|---|
| FeatEnHancer + Featurized Query R-CNN | 86.3 | config |
DARK FACE
| Model | mAP | Config |
|---|---|---|
| FeatEnHancer + Featurized Query R-CNN | 69.0 | config |
Reproducing Results on Other Downstream Vision Tasks:
- The models developed for other downstream tasks, such as Semantic Segmentation and Video Object Detection, utilize distinct frameworks (MMDet, MMSeg, and MMTracking). Hence, it was not possible to release a unified repository at this time. However, to facilitate reproducibility of results, the same FeatEnHancer script can be employed across these different tasks.
Acknowledgment
This work would not be possible without the following codebases. We gratefully thank the authors and collaborators for their wonderful works:
Featurized Query R-CNN,
detectron2,
mmdetection,
mmsegmentation, and
mmtracking
License
The proposed FeatEnHancer is released under the Creative Commons Attribution-NonCommercial 4.0 International Licence.
Citation
If you find FeatEnHancer useful in your research or applications, please consider giving us a star :star: and citing it by the following BibTeX entry.
@InProceedings{FeatEnHancer_Hashmi_ICCV23,
author = {Hashmi, Khurram Azeem and Kallempudi, Goutham and Stricker, Didier and Afzal, Muhammad Zeshan},
title = {FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {6725-6735}
}