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# Computer Vision Annotation Tool (CVAT)
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[![CI][ci-img]][ci-url]
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@ -7,19 +8,25 @@
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[![ui pulls][docker-ui-pulls-img]][docker-ui-image-url]
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[![DOI][doi-img]][doi-url]
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CVAT is free, online, interactive video and image annotation
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tool for computer vision. It is being used by our team to
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annotate million of objects with different properties. Many UI
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and UX decisions are based on feedbacks from professional data
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annotation team. Try it online [app.cvat.ai](https://app.cvat.ai).
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CVAT is an interactive video and image annotation
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tool for computer vision. It is used by tens of thousands of users and
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companies around the world. CVAT is free and open-source.
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**A new repo**: CVAT core team moved the active development of the tool
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to this new repository. Our mission is to help developers, companies and
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organizations around the world to solve real problems using the Data-centric
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AI approach.
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## Documentation
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Start using CVAT online for free: [cvat.ai](https://cvat.ai). Or set it up as a self-hosted solution:
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[read here](https://cvat-ai.github.io/cvat/docs/administration/basics/installation/).
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## Quick start ⚡
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- [Contributing](https://cvat-ai.github.io/cvat/docs/contributing/)
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- [Installation guide](https://cvat-ai.github.io/cvat/docs/administration/basics/installation/)
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- [Manual](https://cvat-ai.github.io/cvat/docs/manual/)
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- [Contributing](https://cvat-ai.github.io/cvat/docs/contributing/)
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- [Django REST API documentation](https://cvat-ai.github.io/cvat/docs/administration/basics/rest_api_guide/)
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- [Datumaro dataset framework](https://github.com/cvat-ai/datumaro/blob/develop/README.md)
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- [Command line interface](https://cvat-ai.github.io/cvat/docs/manual/advanced/cli/)
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@ -28,7 +35,61 @@ annotation team. Try it online [app.cvat.ai](https://app.cvat.ai).
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- [Frequently asked questions](https://cvat-ai.github.io/cvat/docs/faq/)
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- [Questions](#questions)
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## Screencasts
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## Partners ❤️
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CVAT is used by teams all over the world. If you use us, please drop us a line at
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[contact@cvat.ai](mailto:contact+github@cvat.ai) - and we'll add you to this list.
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- [ATLANTIS](https://github.com/smhassanerfani/atlantis) is an open-source dataset for semantic segmentation
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of waterbody images, depevoped by [iWERS](http://ce.sc.edu/iwers/) group in the
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Department of Civil and Environmental Engineering at University of South Carolina, is using CVAT.
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For developing a semantic segmentation dataset using CVAT, please check
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[ATLANTIS published article](https://www.sciencedirect.com/science/article/pii/S1364815222000391),
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[ATLANTIS Development Kit](https://github.com/smhassanerfani/atlantis/tree/master/adk)
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and [annotation tutorial videos](https://www.youtube.com/playlist?list=PLIfLGY-zZChS5trt7Lc3MfNhab7OWl2BR).
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- [Onepanel](https://github.com/onepanelio/core) is an open source
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vision AI platform that fully integrates CVAT with scalable data processing
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and parallelized training pipelines.
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- [DataIsKey](https://dataiskey.eu/annotation-tool/) uses CVAT as their prime data labeling tool
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to offer annotation services for projects of any size.
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- [Human Protocol](https://hmt.ai) uses CVAT as a way of adding annotation service to the human protocol.
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- [Cogito Tech LLC](https://bit.ly/3klT0h6), a Human-in-the-Loop Workforce Solutions Provider, used CVAT
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in annotation of about 5,000 images for a brand operating in the fashion segment.
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- [FiftyOne](https://fiftyone.ai) is an open-source dataset curation and model analysis
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tool for visualizing, exploring, and improving computer vision datasets and models that is
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[tightly integrated](https://voxel51.com/docs/fiftyone/integrations/cvat.html) with CVAT
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for annotation and label refinement.
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## CVAT online: [cvat.ai](https://cvat.ai)
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This is an online version of CVAT. It's free, efficient, and easy to use.
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[cvat.ai](https://cvat.ai) runs the latest version of the tool. You can create up
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to 10 tasks there and upload up to 500Mb of data to annotate. It will only be
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visible to you or people you assign to it.
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For now, it does not have [analytics features](https://cvat-ai.github.io/cvat/docs/administration/advanced/analytics/)
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like management and monitoring the data annotation team.
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We plan to enhance [cvat.ai](https://cvat.ai) with new powerful features. Stay tuned!
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## Prebuilt Docker images 🐳
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Prebuilt docker images are the easiest way to start using CVAT locally. They are available on Docker Hub:
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- [cvat/server](https://hub.docker.com/r/cvat/server)
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- [cvat/ui](https://hub.docker.com/r/cvat/ui)
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The images have been downloaded more than 1M times so far.
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## REST API
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CVAT has a REST API. Its current version is `2.0-alpha`. We focus on its
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improvement and therefore REST API may be changed in the next release.
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## Screencasts 🎦
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Here are some screencasts showing how to use CVAT.
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- [Introduction](https://youtu.be/JERohTFp-NI)
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- [Annotation mode](https://youtu.be/vH_639N67HI)
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@ -42,12 +103,12 @@ annotation team. Try it online [app.cvat.ai](https://app.cvat.ai).
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## Supported annotation formats
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Format selection is possible after clicking on the Upload annotation and Dump
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annotation buttons. [Datumaro](https://github.com/cvat-ai/datumaro)
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CVAT supports multiple annotation formats. You can select the format after clicking the "Upload annotation" and "Dump
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annotation" buttons. [Datumaro](https://github.com/cvat-ai/datumaro)
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dataset framework allows additional dataset transformations via its command
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line tool and Python library.
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For more information about supported formats look at the
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For more information about supported formats, look at the
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[documentation](https://cvat-ai.github.io/cvat/docs/manual/advanced/formats/).
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<!--lint disable maximum-line-length-->
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@ -79,6 +140,10 @@ For more information about supported formats look at the
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## Deep learning serverless functions for automatic labeling
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CVAT supports automatic labelling. It can speed up the annotation process
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up to 10x. Here is a list of the algorithms we support, and the platforms they
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can be ran on:
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<!--lint disable maximum-line-length-->
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| Name | Type | Framework | CPU | GPU |
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@ -102,35 +167,9 @@ For more information about supported formats look at the
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<!--lint enable maximum-line-length-->
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## Online demo: [app.cvat.ai](https://app.cvat.ai)
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This is an online demo with the latest version of the annotation tool.
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Try it online without local installation. Only own or assigned tasks
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are visible to users.
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Disabled features:
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- [Analytics: management and monitoring of data annotation team](https://cvat-ai.github.io/cvat/docs/administration/advanced/analytics/)
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Limitations:
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- No more than 10 tasks per user
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- Uploaded data is limited to 500Mb
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## Prebuilt Docker images
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## License
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Prebuilt docker images for CVAT releases are available on Docker Hub:
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- [cvat/server](https://hub.docker.com/r/cvat/server)
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- [cvat/ui](https://hub.docker.com/r/cvat/ui)
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## REST API
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The current REST API version is `2.0-alpha`. We focus on its improvement and therefore
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REST API may be changed in the next release.
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## LICENSE
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Code released under the [MIT License](https://opensource.org/licenses/MIT).
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The code is released under the [MIT License](https://opensource.org/licenses/MIT).
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This software uses LGPL licensed libraries from the [FFmpeg](https://www.ffmpeg.org) project.
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The exact steps on how FFmpeg was configured and compiled can be found in the [Dockerfile](Dockerfile).
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@ -142,28 +181,6 @@ additional licenses. CVAT.ai is not responsible for obtaining any
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such licenses, nor liable for any licensing fees due in
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connection with your use of FFmpeg.
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## Partners
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- [ATLANTIS](https://github.com/smhassanerfani/atlantis) is an open-source dataset for semantic segmentation
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of waterbody images, depevoped by [iWERS](http://ce.sc.edu/iwers/) group in the
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Department of Civil and Environmental Engineering at University of South Carolina, using CVAT.
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For developing a semantic segmentation dataset using CVAT, please check
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[ATLANTIS published article](https://www.sciencedirect.com/science/article/pii/S1364815222000391),
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[ATLANTIS Development Kit](https://github.com/smhassanerfani/atlantis/tree/master/adk)
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and [annotation tutorial videos](https://www.youtube.com/playlist?list=PLIfLGY-zZChS5trt7Lc3MfNhab7OWl2BR).
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- [Onepanel](https://github.com/onepanelio/core) is an open source
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vision AI platform that fully integrates CVAT with scalable data processing
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and parallelized training pipelines.
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- [DataIsKey](https://dataiskey.eu/annotation-tool/) uses CVAT as their prime data labeling tool
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to offer annotation services for projects of any size.
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- [Human Protocol](https://hmt.ai) uses CVAT as a way of adding annotation service to the human protocol.
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- [Cogito Tech LLC](https://bit.ly/3klT0h6), a Human-in-the-Loop Workforce Solutions Provider, used CVAT
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in annotation of about 5,000 images for a brand operating in the fashion segment.
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- [FiftyOne](https://fiftyone.ai) is an open-source dataset curation and model analysis
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|
|
|
tool for visualizing, exploring, and improving computer vision datasets and models that is
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[tightly integrated](https://voxel51.com/docs/fiftyone/integrations/cvat.html) with CVAT
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for annotation and label refinement.
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## Questions
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CVAT usage related questions or unclear concepts can be posted in our
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@ -172,7 +189,7 @@ contributors and other users.
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However, if you have a feature request or a bug report that can reproduced,
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feel free to open an issue (with steps to reproduce the bug if it's a bug
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report) on [GitHub\* issues](https://github.com/opencv/cvat/issues).
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report) on [GitHub\* issues](https://github.com/cvat-ai/cvat/issues).
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If you are not sure or just want to browse other users common questions,
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[Gitter chat](https://gitter.im/opencv-cvat) is the way to go.
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Anna Petrovicheva
4 years ago
committed by
GitHub
