okhater/lolv2-realLOL v2 Dataset Overview The LOL v2 dataset contains 689 training images and 100 testing images for low light image enhancement tasks. This extended version of the original LOL dataset presents more challenging scenes, providing a robust benchmark for the development and evaluation of low light enhancement algorithms. The data was originally downloaded from the Google Drive folder and is maintained within the Awesome Low Light Enhancement GitHub repository. Citation If you use this dataset in your research, please cite the following paper: Wei Chen, Wenjing Wang, Wenhan Yang, and Jiaying Liu. LOL v2: A new benchmark for low light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. Data Details Training Set: 689 images Testing Set: 100 images This dataset is designed to challenge and benchmark state of the art low light image enhancement techniques with a diverse range of real world scenarios.

lolv2-real

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LOL v2 Dataset Overview The LOL v2 dataset contains 689 training images and 100 testing images for low-light image enhancement tasks. This extended version of the original LOL dataset presents more challenging scenes, providing a robust benchmark for the development and evaluation of low-light enhancement algorithms. The data was originally downloaded from the Google Drive folder and is maintained within the Awesome-Low-Light-Enhancement GitHub repository.… See the full description on the dataset page: https://huggingface.co/datasets/okhater/lolv2-real.

Formats:mdLibraries:Datasets, Dask, Polars, CroissantLicense:apache-2.0

imported

huggingface

minio

license:apache-2.0

size_categories:1K<n<10K

format:imagefolder

modality:image

library:datasets

library:mlcroissant

region:us

0downloads0likes1.1 KBLicense:apache-2.0

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LOL v2 Dataset

Overview

The LOL v2 dataset contains 689 training images and 100 testing images for low-light image enhancement tasks. This extended version of the original LOL dataset presents more challenging scenes, providing a robust benchmark for the development and evaluation of low-light enhancement algorithms.

The data was originally downloaded from the Google Drive folder and is maintained within the Awesome-Low-Light-Enhancement GitHub repository.

Citation

If you use this dataset in your research, please cite the following paper:

Wei Chen, Wenjing Wang, Wenhan Yang, and Jiaying Liu. LOL-v2: A new benchmark for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

Data Details

Training Set: 689 images
Testing Set: 100 images

This dataset is designed to challenge and benchmark state-of-the-art low-light image enhancement techniques with a diverse range of real-world scenarios.

Formats

License

apache-2.0

Source

Mirrored from an external registry.

Last synced 6/14/2026

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