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https://github.com/cleanlab/cleanvision

Automatically find issues in image datasets and practice data-centric computer vision.
https://github.com/cleanlab/cleanvision

computer-vision data-centric-ai data-exploration data-profiling data-quality data-science data-validation deep-learning exploratory-data-analysis image-analysis image-classification image-generation image-quality image-segmentation

Last synced: 3 months ago
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Automatically find issues in image datasets and practice data-centric computer vision.

Host: GitHub
URL: https://github.com/cleanlab/cleanvision
Owner: cleanlab
License: agpl-3.0
Created: 2022-05-26T07:14:11.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2024-03-06T22:16:31.000Z (4 months ago)
Last Synced: 2024-03-06T23:29:09.673Z (4 months ago)
Topics: computer-vision, data-centric-ai, data-exploration, data-profiling, data-quality, data-science, data-validation, deep-learning, exploratory-data-analysis, image-analysis, image-classification, image-generation, image-quality, image-segmentation
Language: Python
Homepage: https://cleanvision.readthedocs.io/
Size: 2.11 MB
Stars: 900
Watchers: 15
Forks: 69
Open Issues: 30
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Lists

awesome-stars - cleanlab/cleanvision - Automatically find issues in image datasets and practice data-centric computer vision. (Python)

README

        


  





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!

[![Read the Docs](https://readthedocs.org/projects/cleanvision/badge/?version=latest)](https://cleanvision.readthedocs.io/en/latest/)

[![pypi](https://img.shields.io/pypi/v/cleanvision?color=blue)](https://pypi.org/pypi/cleanvision/)

[![os](https://img.shields.io/badge/platform-noarch-lightgrey)](https://pypi.org/pypi/cleanvision/)

[![py\_versions](https://img.shields.io/badge/python-3.7%2B-blue)](https://pypi.org/pypi/cleanvision/)

[![codecov](https://codecov.io/github/cleanlab/cleanvision/branch/main/graph/badge.svg?token=y1N6MluN9H)](https://codecov.io/gh/cleanlab/cleanvision)

[![Slack Community](https://img.shields.io/static/v1?logo=slack&style=flat&color=white&label=slack&message=community)](https://cleanlab.ai/slack)

[![Twitter](https://img.shields.io/twitter/follow/CleanlabAI?style=social)](https://twitter.com/CleanlabAI)

[![Cleanlab Studio](https://raw.githubusercontent.com/cleanlab/assets/master/shields/cl-studio-shield.svg)](https://cleanlab.ai/studio/?utm_source=github&utm_medium=readme&utm_campaign=clostostudio)

## 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 on how to use CleanVision

- [Tutorial](https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.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 `

- [Additional example notebooks](https://github.com/cleanlab/cleanvision-examples)

- [Documentation](https://cleanvision.readthedocs.io/)

- [Blog Post](https://cleanlab.ai/blog/cleanvision/)

- [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), CleanVision can detect the following types of issues:

|   | Issue Type       | Description                                                     | Issue Key        | Example                                                                                                                                 |

|---|------------------|-----------------------------------------------------------------|------------------|-----------------------------------------------------------------------------------------------------------------------------------------|

| 1 | Exact Duplicates | Images that are identical to each other                         | exact_duplicates | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/exact_duplicates.png)                     |

| 2 | Near Duplicates  | Images that are visually almost identical                       | near_duplicates  | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/near_duplicates.png)                      |

| 3 | Blurry           | Images where details are fuzzy (out of focus)                   | blurry           | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/blurry.png)                               |

| 4 | Low Information  | Images lacking content (little entropy in pixel values)         | low_information  | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/low_information.png)                      |

| 5 | Dark             | Irregularly dark images (*under*exposed)                        | dark             | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/dark.jpg)                                 |

| 6 | Light            | Irregularly bright images (*over*exposed)                       | light            | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/light.jpg)                                |

| 7 | Grayscale        | Images lacking color                                            | grayscale        | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/grayscale.jpg)                            |

| 8 | Odd Aspect Ratio | Images with an unusual aspect ratio (overly skinny/wide)        | odd_aspect_ratio | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/odd_aspect_ratio.jpg)                     |

| 9 | Odd Size         | Images that are abnormally large or small compared to the rest of the dataset | odd_size         |  |

CleanVision supports Linux, macOS, and Windows and runs on Python 3.7+.

## Join our community

* The best place to learn is [our Slack community](https://cleanlab.ai/slack). Join the discussion there to see how

  folks are using this library, discuss upcoming features, or ask for private support.

* Need professional help with CleanVision?  Join our [\#help Slack channel](https://cleanlab.ai/slack) and message us there, or reach out via email: [email protected]

* Interested in contributing? See the [contributing guide](CONTRIBUTING.md). An easy starting point is to

  consider [issues](https://github.com/cleanlab/cleanvision/labels/good%20first%20issue) marked `good first issue` or

  simply reach out in [Slack](https://cleanlab.ai/slack). We welcome your help building a standard open-source library

  for data-centric computer vision!

* Ready to start adding your own code? See the [development guide](DEVELOPMENT.md).

* Have an issue? [Search existing issues](https://github.com/cleanlab/cleanvision/issues?q=is%3Aissue)

  or [submit a new issue](https://github.com/cleanlab/cleanvision/issues/new/choose).

* Have ideas for the future of data-centric computer vision? Check

  out [our active/planned Projects and what we could use your help with](https://github.com/cleanlab/cleanvision/projects).

## License

Copyright (c) 2022 Cleanlab Inc.

cleanvision is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public

License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later

version.

cleanvision is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied

warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

See [GNU Affero General Public LICENSE](https://github.com/cleanlab/cleanvision/blob/main/LICENSE) for details.

Commercial licensing is available for enterprise teams that want to use CleanVision in production workflows, but are unable to open-source their code [as is required by the current license](https://github.com/cleanlab/cleanvision/blob/main/LICENSE). Please email us: [email protected]

[issue]: https://github.com/cleanlab/cleanvision/issues/new