README.md

# Computer Vision Annotation Tool (CVAT)

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CVAT is free, online, interactive video and image annotation
tool for computer vision. It is being used by our team to
annotate million of objects with different properties. Many UI
and UX decisions are based on feedbacks from professional data
annotation team. Try it online [cvat.org](https://cvat.org).

![CVAT screenshot](cvat/apps/documentation/static/documentation/images/cvat.jpg)

## Documentation

- [Installation guide](cvat/apps/documentation/installation.md)
- [User's guide](cvat/apps/documentation/user_guide.md)
- [Django REST API documentation](#rest-api)
- [Datumaro dataset framework](datumaro/README.md)
- [Command line interface](utils/cli/)
- [XML annotation format](cvat/apps/documentation/xml_format.md)
- [AWS Deployment Guide](cvat/apps/documentation/AWS-Deployment-Guide.md)
- [Frequently asked questions](cvat/apps/documentation/faq.md)
- [Questions](#questions)

## Screencasts

- [Introduction](https://youtu.be/JERohTFp-NI)
- [Annotation mode](https://youtu.be/vH_639N67HI)
- [Interpolation of bounding boxes](https://youtu.be/Hc3oudNuDsY)
- [Interpolation of polygons](https://youtu.be/K4nis9lk92s)
- [Tag_annotation_video](https://youtu.be/62bI4mF-Xfk)
- [Attribute mode](https://youtu.be/iIkJsOkDzVA)
- [Segmentation mode](https://youtu.be/9Fe_GzMLo3E)
- [Tutorial for polygons](https://youtu.be/C7-r9lZbjBw)
- [Semi-automatic segmentation](https://youtu.be/9HszWP_qsRQ)

## Supported annotation formats

Format selection is possible after clicking on the Upload annotation
and Dump annotation buttons. [Datumaro](datumaro/README.md) dataset
framework allows additional dataset transformations
via its command line tool and Python library.

| Annotation format                                                                          | Import | Export |
| ------------------------------------------------------------------------------------------ | ------ | ------ |
| [CVAT for images](cvat/apps/documentation/xml_format.md#annotation)                        | X      | X      |
| [CVAT for a video](cvat/apps/documentation/xml_format.md#interpolation)                    | X      | X      |
| [Datumaro](datumaro/README.md)                                                             |        | X      |
| [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)                                      | X      | X      |
| Segmentation masks from [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)              | X      | X      |
| [YOLO](https://pjreddie.com/darknet/yolo/)                                                 | X      | X      |
| [MS COCO Object Detection](http://cocodataset.org/#format-data)                            | X      | X      |
| [TFrecord](https://www.tensorflow.org/tutorials/load_data/tf_records)                      | X      | X      |
| [MOT](https://motchallenge.net/)                                                           | X      | X      |
| [LabelMe 3.0](http://labelme.csail.mit.edu/Release3.0)                                     | X      | X      |

## Deep learning models for automatic labeling

| Name                                                                                                    | Type       | Framework  |
| ------------------------------------------------------------------------------------------------------- | ---------- | ---------- |
| [Deep Extreme Cut](/serverless/openvino/dextr/nuclio)                                                   | interactor | OpenVINO   |
| [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio)                              | detector   | TensorFlow |
| [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   |
| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   |
| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector   | OpenVINO   |
| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio)                                         | detector   | TensorFlow |
| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio)     | reid       | OpenVINO   |

## Online demo: [cvat.org](https://cvat.org)

This is an online demo with the latest version of the annotation tool.
Try it online without local installation. Only own or assigned tasks
are visible to users.

Disabled features:
- [Analytics: management and monitoring of data annotation team](/components/analytics/README.md)

Limitations:
- No more than 10 tasks per user
- Uploaded data is limited to 500Mb

## REST API

Automatically generated Swagger documentation for Django REST API is
available on ``<cvat_origin>/api/swagger``
(default: ``localhost:8080/api/swagger``).

Swagger documentation is visiable on allowed hostes, Update environement variable in docker-compose.yml file with cvat hosted machine IP or domain name. Example - ``ALLOWED_HOSTS: 'localhost, 127.0.0.1'``)

## LICENSE

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

## Questions

CVAT usage related questions or unclear concepts can be posted in our
[Gitter chat](https://gitter.im/opencv-cvat) for **quick replies** from
contributors and other users.

However, if you have a feature request or a bug report that can reproduced,
feel free to open an issue (with steps to reproduce the bug if it's a bug
report) on [GitHub* issues](https://github.com/opencv/cvat/issues).

If you are not sure or just want to browse other users common questions,
[Gitter chat](https://gitter.im/opencv-cvat) is the way to go.

Other ways to ask questions and get our support:
* [\#cvat](https://stackoverflow.com/search?q=%23cvat) tag on StackOverflow*
* [Forum on Intel Developer Zone](https://software.intel.com/en-us/forums/computer-vision)

## 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/)