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121 lines
7.2 KiB
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121 lines
7.2 KiB
Markdown
# Computer Vision Annotation Tool (CVAT)
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[](https://travis-ci.org/openvinotoolkit/cvat)
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[](https://app.codacy.com/gh/openvinotoolkit/cvat?utm_source=github.com&utm_medium=referral&utm_content=openvinotoolkit/cvat&utm_campaign=Badge_Grade_Dashboard)
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[](https://gitter.im/opencv-cvat)
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[](https://coveralls.io/github/openvinotoolkit/cvat?branch=develop)
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[](https://zenodo.org/badge/latestdoi/139156354)
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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 [cvat.org](https://cvat.org).
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## Documentation
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- [Installation guide](cvat/apps/documentation/installation.md)
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- [User's guide](cvat/apps/documentation/user_guide.md)
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- [Django REST API documentation](#rest-api)
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- [Datumaro dataset framework](datumaro/README.md)
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- [Command line interface](utils/cli/)
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- [XML annotation format](cvat/apps/documentation/xml_format.md)
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- [AWS Deployment Guide](cvat/apps/documentation/AWS-Deployment-Guide.md)
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- [Frequently asked questions](cvat/apps/documentation/faq.md)
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- [Questions](#questions)
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## Screencasts
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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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- [Interpolation of bounding boxes](https://youtu.be/Hc3oudNuDsY)
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- [Interpolation of polygons](https://youtu.be/K4nis9lk92s)
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- [Tag_annotation_video](https://youtu.be/62bI4mF-Xfk)
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- [Attribute mode](https://youtu.be/iIkJsOkDzVA)
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- [Segmentation mode](https://youtu.be/9Fe_GzMLo3E)
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- [Tutorial for polygons](https://youtu.be/C7-r9lZbjBw)
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- [Semi-automatic segmentation](https://youtu.be/9HszWP_qsRQ)
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## Supported annotation formats
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Format selection is possible after clicking on the Upload annotation
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and Dump annotation buttons.
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[Datumaro](https://github.com/openvinotoolkit/datumaro) dataset
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framework allows additional dataset transformations
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via its command line tool and Python library.
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| Annotation format | Import | Export |
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| ------------------------------------------------------------------------------------------ | ------ | ------ |
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| [CVAT for images](cvat/apps/documentation/xml_format.md#annotation) | X | X |
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| [CVAT for a video](cvat/apps/documentation/xml_format.md#interpolation) | X | X |
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| [Datumaro](https://github.com/openvinotoolkit/datumaro) | | X |
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| [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | X | X |
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| Segmentation masks from [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | X | X |
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| [YOLO](https://pjreddie.com/darknet/yolo/) | X | X |
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| [MS COCO Object Detection](http://cocodataset.org/#format-data) | X | X |
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| [TFrecord](https://www.tensorflow.org/tutorials/load_data/tf_records) | X | X |
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| [MOT](https://motchallenge.net/) | X | X |
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| [LabelMe 3.0](http://labelme.csail.mit.edu/Release3.0) | X | X |
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## Deep learning models for automatic labeling
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| Name | Type | Framework |
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| ------------------------------------------------------------------------------------------------------- | ---------- | ---------- |
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| [Deep Extreme Cut](/serverless/openvino/dextr/nuclio) | interactor | OpenVINO |
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| [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio) | detector | TensorFlow |
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| [Mask RCNN](/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio) | detector | OpenVINO |
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| [YOLO v3](/serverless/openvino/omz/public/yolo-v3-tf/nuclio) | detector | OpenVINO |
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| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio) | detector | OpenVINO |
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| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector | OpenVINO |
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| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio) | detector | TensorFlow |
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| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio) | reid | OpenVINO |
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## Online demo: [cvat.org](https://cvat.org)
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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](/components/analytics/README.md)
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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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## REST API
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Automatically generated Swagger documentation for Django REST API is
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available on ``<cvat_origin>/api/swagger``
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(default: ``localhost:8080/api/swagger``).
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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'``)
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## LICENSE
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Code released under the [MIT License](https://opensource.org/licenses/MIT).
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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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[Gitter chat](https://gitter.im/opencv-cvat) for **quick replies** from
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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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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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Other ways to ask questions and get our support:
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* [\#cvat](https://stackoverflow.com/search?q=%23cvat) tag on StackOverflow*
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* [Forum on Intel Developer Zone](https://software.intel.com/en-us/forums/computer-vision)
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## Links
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- [Intel AI blog: New Computer Vision Tool Accelerates Annotation of Digital Images and Video](https://www.intel.ai/introducing-cvat)
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- [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)
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- [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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