Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/bigwangyudong/lqit
Low Quality Image Toolbox
https://github.com/bigwangyudong/lqit
foggy-object-detection low-quality-image pytroch underwater-image underwater-object-detection
Last synced: 24 days ago
JSON representation
Low Quality Image Toolbox
- Host: GitHub
- URL: https://github.com/bigwangyudong/lqit
- Owner: BIGWangYuDong
- License: apache-2.0
- Created: 2022-10-29T05:57:02.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-21T05:42:36.000Z (about 2 months ago)
- Last Synced: 2024-12-10T14:51:44.775Z (about 1 month ago)
- Topics: foggy-object-detection, low-quality-image, pytroch, underwater-image, underwater-object-detection
- Language: Python
- Homepage:
- Size: 914 KB
- Stars: 42
- Watchers: 1
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Low-Quality Image ToolBox
English | [简体中文](README_zh-CN.md)
## Introduction
LQIT is an open source Low-Quality Image Toolbox, including low-quality (underwater, foggy, low-light, etc.) image enhancement tasks,
and related high-level computer vision tasks (such as object detection). LQIT depends on [PyTorch](https://pytorch.org/) and [OpenMMLab 2.0 series](https://github.com/open-mmlab).
The main branch works with **PyTorch 1.6+**.
The compatibility to earlier versions of PyTorch is not fully tested.
## What's New
**v0.0.1rc2** was released in 28/10/2023:
- Support [FeiShu (Lark) robot](configs/lark/README.md)
- Support [TIENet](https://link.springer.com/article/10.1007/s11760-023-02695-9), [UOD-AIR](https://ieeexplore.ieee.org/abstract/document/9949063), and [RDFFNet](https://link.springer.com/article/10.1007/s11760-022-02410-0)
- Release `RTTS` foggy object detection models
Please refer to [changelog](docs/en/notes/changelog.md) for details and release history.
## Installation & Dataset Preparation
LQIT depends on [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv), and [MMEval](https://github.com/open-mmlab/mmeval).
It also can use [OpenMMLab codebases](https://github.com/open-mmlab) as a dependency, such as [MMDetection](https://github.com/open-mmlab/mmdetection/tree/master).
Please refer to [Installation](docs/en/get_started.md) for installation of LQIT and [data preparation](data/README.md) for dataset preparation.
## Contributing
We appreciate all contributions to improve LQIT. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
## License
LQIT is released under the [Apache 2.0 license](LICENSE), while some specific features in this library are with other licenses. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters.
## Contact
If you have any questions, please contact Yudong Wang at [email protected] or [email protected].