Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
![CVAT logo](site/content/en/images/cvat_poster_with_name.png)

# Computer Vision Annotation Tool (CVAT)

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[![CI][ci-img]][ci-url]
[![Gitter chat][gitter-img]][gitter-url]
[![Discord][discord-img]][discord-url]
[![Coverage Status][coverage-img]][coverage-url]
[![server pulls][docker-server-pulls-img]][docker-server-image-url]
[![ui pulls][docker-ui-pulls-img]][docker-ui-image-url]
[![DOI][doi-img]][doi-url]

CVAT is an interactive video and image annotation
tool for computer vision. It is used by tens of thousands of users and
companies around the world. Our mission is to help developers, companies, and
organizations around the world to solve real problems using the Data-centric
AI approach.

CVAT is free and open-source.

**A new repo**: CVAT core team moved the active development of the tool
to this new repository.

Start using CVAT online for free: [cvat.ai](https://cvat.ai).
Or set it up as a self-hosted solution:
[Self-hosted Installation Guide](https://opencv.github.io/cvat/docs/administration/basics/installation/).

![CVAT screencast](site/content/en/images/cvat-ai-screencast.gif)

## Quick start ⚡

- [Installation guide](https://opencv.github.io/cvat/docs/administration/basics/installation/)
- [Manual](https://opencv.github.io/cvat/docs/manual/)
- [Contributing](https://opencv.github.io/cvat/docs/contributing/)
- [Datumaro dataset framework](https://github.com/cvat-ai/datumaro/blob/develop/README.md)
- [Server API](#api)
- [Python SDK](#sdk)
- [Command line tool](#cli)
- [XML annotation format](https://opencv.github.io/cvat/docs/manual/advanced/xml_format/)
- [AWS Deployment Guide](https://opencv.github.io/cvat/docs/administration/basics/aws-deployment-guide/)
- [Frequently asked questions](https://opencv.github.io/cvat/docs/faq/)
- [Where to ask questions](#where-to-ask-questions)

## Partners ❤️

CVAT is used by teams all over the world. In the list, you can find key companies which
help us support the product or an essential part of our ecosystem. If you use us,
please drop us a line at [contact@cvat.ai](mailto:contact+github@cvat.ai).

- [Human Protocol](https://hmt.ai) uses CVAT as a way of adding annotation service to the Human Protocol.
- [FiftyOne](https://fiftyone.ai) is an open-source dataset curation and model analysis
  tool for visualizing, exploring, and improving computer vision datasets and models that are
  [tightly integrated](https://voxel51.com/docs/fiftyone/integrations/cvat.html) with CVAT
  for annotation and label refinement.

## Public datasets

[ATLANTIS](https://github.com/smhassanerfani/atlantis), an open-source dataset for semantic segmentation
of waterbody images, developed by [iWERS](http://ce.sc.edu/iwers/) group in the
Department of Civil and Environmental Engineering at the University of South Carolina is using CVAT.

For developing a semantic segmentation dataset using CVAT, see:

- [ATLANTIS published article](https://www.sciencedirect.com/science/article/pii/S1364815222000391)
- [ATLANTIS Development Kit](https://github.com/smhassanerfani/atlantis/tree/master/adk)
- [ATLANTIS annotation tutorial videos](https://www.youtube.com/playlist?list=PLIfLGY-zZChS5trt7Lc3MfNhab7OWl2BR).

## CVAT online: [cvat.ai](https://cvat.ai)

This is an online version of CVAT. It's free, efficient, and easy to use.

[cvat.ai](https://cvat.ai) runs the latest version of the tool. You can create up
to 10 tasks there and upload up to 500Mb of data to annotate. It will only be
visible to you or the people you assign to it.

For now, it does not have [analytics features](https://opencv.github.io/cvat/docs/administration/advanced/analytics/)
like management and monitoring the data annotation team.

We plan to enhance [cvat.ai](https://cvat.ai) with new powerful features. Stay tuned!

## Prebuilt Docker images 🐳

Prebuilt docker images are the easiest way to start using CVAT locally. They are available on Docker Hub:

- [cvat/server](https://hub.docker.com/r/cvat/server)
- [cvat/ui](https://hub.docker.com/r/cvat/ui)

The images have been downloaded more than 1M times so far.

## Screencasts 🎦

Here are some screencasts showing how to use CVAT.

<!--lint disable maximum-line-length-->

[Computer Vision Annotation Course](https://www.youtube.com/playlist?list=PL0to7Ng4PuuYQT4eXlHb_oIlq_RPeuasN): we introduce our course series designed to help you annotate data faster and better using CVAT. This course is about CVAT deployment and integrations, it includes presentations and covers the following topics:

- **Speeding up your data annotation process: introduction to CVAT and Datumaro**. What problems do CVAT and Datumaro solve, and how they can speed up your model training process. Some resources you can use to learn more about how to use them.
- **Deployment and use CVAT**. Use the app online at [app.cvat.ai](app.cvat.ai). A local deployment. A containerized local deployment with docker-compose (for regular use), and a local cluster deployment with Kubernetes (for enterprise users). A 2-minute tour of the interface, a breakdown of CVAT’s internals, and a demonstration of how to deploy CVAT using docker-compose.

[Product tour](https://www.youtube.com/playlist?list=PL0to7Ng4Puua37NJVMIShl_pzqJTigFzg): in this course, we show how to use CVAT, and help to get familiar with CVAT functionality and interfaces. This course does not cover integrations and is dedicated solely to CVAT. It covers the following topics:

- **Pipeline**. In this video, we show how to use [app.cvat.ai](app.cvat.ai): how to sign up, upload your data, annotate it, and download it.

<!--lint enable maximum-line-length-->

For feedback, please see [Contact us](#contact-us)

## API

- [Documentation](https://opencv.github.io/cvat/docs/api_sdk/api/)

## SDK

- Install with `pip install cvat-sdk`
- [PyPI package homepage](https://pypi.org/project/cvat-sdk/)
- [Documentation](https://opencv.github.io/cvat/docs/api_sdk/sdk/)

## CLI

- Install with `pip install cvat-cli`
- [PyPI package homepage](https://pypi.org/project/cvat-cli/)
- [Documentation](https://opencv.github.io/cvat/docs/api_sdk/cli/)

## Supported annotation formats

CVAT supports multiple annotation formats. You can select the format
after clicking the **Upload annotation** and **Dump annotation** buttons.
[Datumaro](https://github.com/cvat-ai/datumaro) dataset framework allows
additional dataset transformations with its command line tool and Python library.

For more information about the supported formats, see:
[Annotation Formats](https://opencv.github.io/cvat/docs/manual/advanced/formats/).

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| Annotation format                                                                                | Import | Export |
| ------------------------------------------------------------------------------------------------ | ------ | ------ |
| [CVAT for images](https://opencv.github.io/cvat/docs/manual/advanced/xml_format/#annotation)     | ✔️     | ✔️     |
| [CVAT for a video](https://opencv.github.io/cvat/docs/manual/advanced/xml_format/#interpolation) | ✔️     | ✔️     |
| [Datumaro](https://github.com/cvat-ai/datumaro)                                                  | ✔️     | ✔️     |
| [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)                                            | ✔️     | ✔️     |
| Segmentation masks from [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)                    | ✔️     | ✔️     |
| [YOLO](https://pjreddie.com/darknet/yolo/)                                                       | ✔️     | ✔️     |
| [MS COCO Object Detection](http://cocodataset.org/#format-data)                                  | ✔️     | ✔️     |
| [MS COCO Keypoints Detection](http://cocodataset.org/#format-data)                               | ✔️     | ✔️     |
| [TFrecord](https://www.tensorflow.org/tutorials/load_data/tfrecord)                              | ✔️     | ✔️     |
| [MOT](https://motchallenge.net/)                                                                 | ✔️     | ✔️     |
| [MOTS PNG](https://www.vision.rwth-aachen.de/page/mots)                                          | ✔️     | ✔️     |
| [LabelMe 3.0](http://labelme.csail.mit.edu/Release3.0)                                           | ✔️     | ✔️     |
| [ImageNet](http://www.image-net.org)                                                             | ✔️     | ✔️     |
| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)                             | ✔️     | ✔️     |
| [WIDER Face](http://shuoyang1213.me/WIDERFACE/)                                                  | ✔️     | ✔️     |
| [VGGFace2](https://github.com/ox-vgg/vgg_face2)                                                  | ✔️     | ✔️     |
| [Market-1501](https://www.aitribune.com/dataset/2018051063)                                      | ✔️     | ✔️     |
| [ICDAR13/15](https://rrc.cvc.uab.es/?ch=2)                                                       | ✔️     | ✔️     |
| [Open Images V6](https://storage.googleapis.com/openimages/web/index.html)                       | ✔️     | ✔️     |
| [Cityscapes](https://www.cityscapes-dataset.com/login/)                                          | ✔️     | ✔️     |
| [KITTI](http://www.cvlibs.net/datasets/kitti/)                                                   | ✔️     | ✔️     |
| [Kitti Raw Format](https://www.cvlibs.net/datasets/kitti/raw_data.php)                           | ✔️     | ✔️     |
| [LFW](http://vis-www.cs.umass.edu/lfw/)                                                          | ✔️     | ✔️     |
| [Supervisely Point Cloud Format](https://docs.supervise.ly/data-organization/00_ann_format_navi) | ✔️     | ✔️     |

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## Deep learning serverless functions for automatic labeling

CVAT supports automatic labeling. It can speed up the annotation process
up to 10x. Here is a list of the algorithms we support, and the platforms they can be run on:

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| Name                                                                                                    | Type       | Framework  | CPU | GPU |
| ------------------------------------------------------------------------------------------------------- | ---------- | ---------- | --- | --- |
| [Deep Extreme Cut](/serverless/openvino/dextr/nuclio)                                                   | interactor | OpenVINO   | ✔️  |     |
| [Faster RCNN](/serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio)                     | detector   | OpenVINO   | ✔️  |     |
| [Mask RCNN](/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio)           | detector   | OpenVINO   | ✔️  |     |
| [YOLO v3](/serverless/openvino/omz/public/yolo-v3-tf/nuclio)                                            | detector   | OpenVINO   | ✔️  |     |
| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio)     | reid       | OpenVINO   | ✔️  |     |
| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector   | OpenVINO   | ✔️  |     |
| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   | ✔️  |     |
| [YOLO v5](/serverless/pytorch/ultralytics/yolov5/nuclio)                                                | detector   | PyTorch    | ✔️  |     |
| [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio)                                                | tracker    | PyTorch    | ✔️  | ✔️  |
| [TransT](/serverless/pytorch/dschoerk/transt/nuclio)                                                    | tracker    | PyTorch    | ✔️  | ✔️  |
| [f-BRS](/serverless/pytorch/saic-vul/fbrs/nuclio)                                                       | interactor | PyTorch    | ✔️  |     |
| [HRNet](/serverless/pytorch/saic-vul/hrnet/nuclio)                                                      | interactor | PyTorch    |     | ✔️  |
| [Inside-Outside Guidance](/serverless/pytorch/shiyinzhang/iog/nuclio)                                   | interactor | PyTorch    | ✔️  |     |
| [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio)                              | detector   | TensorFlow | ✔️  | ✔️  |
| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio)                                         | detector   | TensorFlow | ✔️  | ✔️  |
| [RetinaNet](serverless/pytorch/facebookresearch/detectron2/retinanet/nuclio)                            | detector   | PyTorch    | ✔️  | ✔️  |
| [Face Detection](/serverless/openvino/omz/intel/face-detection-0205/nuclio)                             | detector   | OpenVINO   | ✔️  |     |

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## License

The code is released under the [MIT License](https://opensource.org/licenses/MIT).

This software uses LGPL-licensed libraries from the [FFmpeg](https://www.ffmpeg.org) project.
The exact steps on how FFmpeg was configured and compiled can be found in the [Dockerfile](Dockerfile).

FFmpeg is an open-source framework licensed under LGPL and GPL.
See [https://www.ffmpeg.org/legal.html](https://www.ffmpeg.org/legal.html). You are solely responsible
for determining if your use of FFmpeg requires any
additional licenses. CVAT.ai Corporation is not responsible for obtaining any
such licenses, nor liable for any licensing fees due in
connection with your use of FFmpeg.

## Contact us

[Gitter](https://gitter.im/opencv-cvat/public) to ask CVAT usage-related questions.
Typically questions get answered fast by the core team or community. There you can also browse other common questions.

[Discord](https://discord.gg/S6sRHhuQ7K) is the place to also ask questions or discuss any other stuff related to CVAT.

[LinkedIn](https://www.linkedin.com/company/cvat-ai/) for the company and work-related questions.

[YouTube](https://www.youtube.com/@cvat-ai) to see screencast and tutorials about the CVAT.

[GitHub issues](https://github.com/cvat-ai/cvat/issues) for feature requests or bug reports.
If it's a bug, please add the steps to reproduce it.

[#cvat](https://stackoverflow.com/search?q=%23cvat) tag on StackOverflow is one more way to ask
questions and get our support.

[contact@cvat.ai](mailto:contact+github@cvat.ai) to reach out to us if you need commercial support.

## Links

- [Intel AI blog: New Computer Vision Tool Accelerates Annotation of Digital Images and Video](https://www.intel.ai/introducing-cvat)
- [Intel Software: Computer Vision Annotation Tool: A Universal Approach to Data Annotation](https://software.intel.com/en-us/articles/computer-vision-annotation-tool-a-universal-approach-to-data-annotation)
- [VentureBeat: Intel open-sources CVAT, a toolkit for data labeling](https://venturebeat.com/2019/03/05/intel-open-sources-cvat-a-toolkit-for-data-labeling/)

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