Project | Arxiv | Benchmarks|
- ⚡(2021-09-01): We have released the dataset, please visit homepage for access to the dataset. (Note that we removed some low-quality images from the original dataset, and for this version there are 30976 images.)
If you use this data for your research, please cite our paper LLVIP: A Visible-infrared Paired Dataset for Low-light Vision:
@inproceedings{jia2021llvip,
title={LLVIP: A Visible-infrared Paired Dataset for Low-light Vision},
author={Jia, Xinyu and Zhu, Chuang and Li, Minzhen and Tang, Wenqi and Zhou, Wenli},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={3496--3504},
year={2021}
}
Baselines
- Install requirements
git clone https://github.com/bupt-ai-cz/LLVIP.git cd LLVIP/FusionGAN # Create your virtual environment using anaconda conda create -n FusionGAN python=3.7 conda activate FusionGAN conda install matplotlib scipy==1.2.1 tensorflow-gpu==1.14.0 pip install opencv-python sudo apt install libgl1-mesa-glx
- File structure
FusionGAN ├── ... ├── Test_LLVIP_ir | ├── 190001.jpg | ├── 190002.jpg | └── ... ├── Test_LLVIP_vi | ├── 190001.jpg | ├── 190002.jpg | └── ... ├── Train_LLVIP_ir | ├── 010001.jpg | ├── 010002.jpg | └── ... └── Train_LLVIP_vi ├── 010001.jpg ├── 010002.jpg └── ...
python main.py --epoch 10 --batch_size 32See more training options in 'main.py'.
python test_one_image.pyRemember to put pretrained model in your 'checkpoint' folder and change corresponding model name in 'test_one_image.py'. To acquire complete LLVIP dataset, please visit https://bupt-ai-cz.github.io/LLVIP/.
Baselines
- Install requirements
git clone https://github.com/bupt-ai-cz/LLVIP.git cd LLVIP/yolov5 pip install -r requirements.txt - File structure
We provide a script named
yolov5 ├── ... └──LLVIP ├── labels | ├──train | | ├── 010001.txt | | ├── 010002.txt | | └── ... | └──val | ├── 190001.txt | ├── 190002.txt | └── ... └── images ├──train | ├── 010001.jpg | ├── 010002.jpg | └── ... └── val ├── 190001.jpg ├── 190002.jpg └── ...xml2txt_yolov5.pyto convert xml files to txt files, remember to modify the file path before using.
python train.py --img 1280 --batch 8 --epochs 200 --data LLVIP.yaml --weights yolov5l.pt --name LLVIP_exportSee more training options in train.py. The pretrained model yolov5l.pt can be downloaded from here. The trained model will be saved in ./runs/train/LLVIP_export/weights folder.
python val.py --data --img 1280 --weights last.pt --data LLVIP.yamlRemember to put the trained model in the same folder as val.py.
- Click Here for the tutorial of Yolov3.
We retrained and tested Yolov5l and Yolov3 on the updated dataset (30976 images).
Where AP means the average of AP at IoU threshold of 0.5 to 0.95, with an interval of 0.05.
We also calculated log average miss rate based on the test results and drew the miss rate-FPPI curve.
Baseline
- Install requirements
cd pix2pixGAN pip install -r requirements.txt - Prepare dataset
- File structure
pix2pixGAN ├── ... └──datasets ├── ... └──LLVIP ├── train | ├── 010001.jpg | ├── 010002.jpg | ├── 010003.jpg | └── ... └── test ├── 190001.jpg ├── 190002.jpg ├── 190003.jpg └── ...
python train.py --dataroot ./datasets/LLVIP --name LLVIP --model pix2pix --direction AtoB --batch_size 8 --preprocess scale_width_and_crop --load_size 320 --crop_size 256 --gpu_ids 0 --n_epochs 100 --n_epochs_decay 100python test.py --dataroot ./datasets/LLVIP --name LLVIP --model pix2pix --direction AtoB --gpu_ids 0 --preprocess scale_width_and_crop --load_size 320 --crop_size 256See ./pix2pixGAN/options for more train and test options.
We retrained and tested pix2pixGAN on the updated dataset(30976 images). The structure of generator is unet256, and the structure of discriminator is the basic PatchGAN as default.
This LLVIP Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.
Welcome to point out errors in data annotation. Also welcome to contribute more data annotations, such as segmentation. Please contact us.
email: shengjie.Liu@bupt.edu.cn, czhu@bupt.edu.cn, jiaxinyujxy@qq.com, tangwenqi@bupt.edu.cn