Yunan Li  Yihao Zhang  Shoude Li  Long Tian  Dou Quan  Chaoneng Li  Qiguang Miao 
Xidian University; Xi'an Key Laboratory of Big Data and Intelligent Vision

Code | Paper | Supp

• First Release.

• Release Code of Image Classification.

  • ResNet18 on CODaN
  • ResNet50 on COCO&ExDark

• Release Code of Object Detection.

• Release Code of Action Recognition.

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We utlized the VOCO dataset(part of the VOC dataset and COCO dataset. The exact composition can be found in Supplementary, and we will also be uploading our training data in a few days), you can apply the Zero-DCE method to enhance low-light data in the test night folder. For darkening the data in the train folder, you may either train the CUT model yourself using unpaired normal-light and low-light data for darkening the normal-light data, or directly use our pre-trained model parameters.

We use the pre-trained YOLOv5m model. You can directly place it in the ./weights folder.

You can run the following command to train the model:

python train_byol.py --weights weights/yolov5m.pt --cfg models/yolov5m.yaml --data data/Exdark_night.yaml --batch-size 8 --epochs 30 --imgsz 608 --hyp data/hyps/hyp.scratch-high.yaml --back_ratio 0.3 --byol_weight 0.1. 

--back_ratio is used to specify the background occlusion ratio. --byol_weight is used to specify the weight for contrastive learning.

You can run the following command to validate the model:

python val.py --data data/Exdark_night.yaml --batch-size 8 --weights runs/train/exp1/weights/best.pt --imgsz 608 --task test --verbose

We utilized the CODaN dataset, you can apply the Zero-DCE method to enhance low-light data in the test_night folder. For darkening the data in the train folder, you may either train the CUT model yourself using unpaired normal-light and low-light data for darkening the normal-light training data, or directly use our pre-trained model parameters.

Of course, you can download our preprocessed CODaN dataset directly and put it under ./classification/resnet18/data/. In this version, the test_night_zdce folder contains data that has been enhanced for low-light conditions using Zero-DCE, and the train_day2night folder contains data darkened.

We use a pre-trained ResNet-18 as the baseline, which you can download and place in ./classification/resnet18/checkpoints/baseline_resnet.

Run train.sh in ./classification/resnet18 or the following command to start training:

python train.py --use_BYOL \
  --checkpoint  'checkpoints/baseline_resnet/model_best.pt' \
  --experiment  'your_own_folder'

Use --checkpoint to specify the pre-trained model and --experiment to set the storage location for model checkpoints and logs.

Our training log is provided in ./classification/resnet18/checkpoints/our_train_log.txt, which you can use as a reference.

Run test.sh in ./classification/resnet18 to evaluate the model's performance or to validate our pre-trained model.

python test.py  --checkpoint 'checkpoints/train/model_best.pt'

coming soon.

coming soon.

If our work is useful for your research, please consider citing:

@inproceedings{li2025watching,
  title={Watching it in Dark: A Target-Aware Representation Learning Framework for High-Level Vision Tasks in Low Illumination},
  author={Li, Yunan and Zhang, Yihao and Li, Shoude and Tian, Long and Quan, Dou and Li, Chaoneng and Miao, Qiguang},
  booktitle={ECCV},
  year={2024}
}

This work is heavily based on CIConv, YOLOv5, ARID and Similarity Min-Max. Thanks to all the authors for their great work.