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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Automatically find issues in image datasets and practice data-centric computer vision.
| Date | Stars |
|---|---|
| 2026-07-24 | 1193 |
| 2026-07-25 | 1194 |
| 2026-07-28 | 1194 |
| 2026-07-30 | 1194 |
| 2026-07-31 | 1195 |
| 2026-08-06 | 1195 |
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<p align="center">
<img src="https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/cleanvision_logo_open_source_transparent.png" width=50% height=50%>
</p>
<img width="1200" alt="Screen Shot 2023-03-10 at 10 23 33 AM" src="https://user-images.githubusercontent.com/10901697/224394144-bb0e1c85-6851-4828-bcd2-4ed234270a78.png">
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc.
This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning.
CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset!
[](https://cleanvision.readthedocs.io/en/latest/)
[](https://pypi.org/pypi/cleanvision/)
[](https://pypi.org/pypi/cleanvision/)
[](https://pypi.org/pypi/cleanvision/)
[](https://codecov.io/gh/cleanlab/cleanvision)
## Installation
```shell
pip install cleanvision
```
## Quickstart
Download an example dataset (optional). Or just use any collection of image files you have.
```shell
wget -nc 'https://cleanlab-public.s3.amazonaws.com/CleanVision/image_files.zip'
```
1. Run CleanVision to audit the images.
```python
from cleanvision import Imagelab
# Specify path to folder containing the image files in your dataset
imagelab = Imagelab(data_path="FOLDER_WITH_IMAGES/")
# Automatically check for a predefined list of issues within your dataset
imagelab.find_issues()
# Produce a neat report of the issues found in your dataset
imagelab.report()
```
2. CleanVision diagnoses many types of issues, but you can also check for only specific issues.
```python
issue_types = {"dark": {}, "blurry": {}}
imagelab.find_issues(issue_types=issue_types)
# Produce a report with only the specified issue_types
imagelab.report(issue_types=issue_types)
```
## More resources
- [Tutorial](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html)
- [Documentation](https://cleanvision.readthedocs.io/)
- [Blog](https://cleanvision.readthedocs.io/en/latest/blog.html)
- [Run CleanVision on a HuggingFace dataset](https://cleanvision.readthedocs.io/en/latest/tutorials/huggingface_dataset.html)
- [Run CleanVision on a Torchvision dataset](https://cleanvision.readthedocs.io/en/latest/tutorials/torchvision_dataset.html)
- [Example script](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/run.py) that can be run with: `python examples/run.py --path <FOLDER_WITH_IMAGES>`
- [Additional example notebooks](https://github.com/cleanlab/cleanvision-examples)
- [FAQ](https://cleanvision.readthedocs.io/en/latest/faq.html)
## *Clean* your data for better Computer *Vision*
The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. CleanVision helps you automatically identify common types of data issues lurking in image datasets.
This package currently detects issues in the raw images themselves, making it a useful tool for any computer vision
task such as: classification, segmentation, object detection, pose estimation, keypoint detection, [generative modeling](https://openai.com/research/dall-e-2-pre-training-mitigations), etc.
To detect issues in the labels of your image data, you can instead
use the [cleanlab](https://github.com/cleanlab/cleanlab/) package.
In any collection of image files (most [formats](https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html) supported), CleanVisExcerpt of 7,180 characters
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Jonas Mueller · MIT
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Yonghye Kwon · MarkAny · South Korea
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f6b336fef4fddeab, topic:computer-vision, topic:image-segmentation, topic:image-classification