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# Computer Vision Annotation Tool (CVAT)
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<a href="https://www.producthunt.com/posts/cvat-computer-vision-annotation-tool?utm_source=badge-featured&utm_medium=badge&utm_souce=badge-cvat-computer-vision-annotation-tool" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=353415&theme=light" alt="CVAT – Computer Vision Annotation Tool - The open data annotation platform for AI | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
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[![CI][ci-img]][ci-url]
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[![Gitter chat][gitter-img]][gitter-url]
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[![Discord][discord-img]][discord-url]
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[![Coverage Status][coverage-img]][coverage-url]
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[![server pulls][docker-server-pulls-img]][docker-server-image-url]
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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 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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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://opencv.github.io/cvat/docs/administration/basics/installation/).
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## Quick start ⚡
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- [Installation guide](https://opencv.github.io/cvat/docs/administration/basics/installation/)
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- [Manual](https://opencv.github.io/cvat/docs/manual/)
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- [Contributing](https://opencv.github.io/cvat/docs/contributing/)
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- [Django REST API documentation](https://opencv.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://opencv.github.io/cvat/docs/manual/advanced/cli/)
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- [XML annotation format](https://opencv.github.io/cvat/docs/manual/advanced/xml_format/)
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- [AWS Deployment Guide](https://opencv.github.io/cvat/docs/administration/basics/aws-deployment-guide/)
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- [Frequently asked questions](https://opencv.github.io/cvat/docs/faq/)
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- [Where to ask questions](#where-to-ask-questions)
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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), an open-source dataset for semantic segmentation
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of waterbody images, depeloped 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://opencv.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: [documentation](https://opencv.github.io/cvat/docs/administration/basics/rest_api_guide/).
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Its current version is `2.0-alpha`. We focus on its improvement, and the API may be changed in the next releases.
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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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- [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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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 the supported formats, look at the
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[documentation](https://opencv.github.io/cvat/docs/manual/advanced/formats/).
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<!--lint disable maximum-line-length-->
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| Annotation format | Import | Export |
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| --------------------------------------------------------------------------------------------------------- | ------ | ------ |
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| [CVAT for images](https://opencv.github.io/cvat/docs/manual/advanced/xml_format/#annotation) | ✔️ | ✔️ |
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| [CVAT for a video](https://opencv.github.io/cvat/docs/manual/advanced/xml_format/#interpolation) | ✔️ | ✔️ |
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| [Datumaro](https://github.com/cvat-ai/datumaro) | | ✔️ |
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| [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | ✔️ | ✔️ |
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| Segmentation masks from [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | ✔️ | ✔️ |
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| [YOLO](https://pjreddie.com/darknet/yolo/) | ✔️ | ✔️ |
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| [MS COCO Object Detection](http://cocodataset.org/#format-data) | ✔️ | ✔️ |
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| [MS COCO Keypoints Detection](http://cocodataset.org/#format-data) | ✔️ | ✔️ |
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| [TFrecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) | ✔️ | ✔️ |
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| [MOT](https://motchallenge.net/) | ✔️ | ✔️ |
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| [LabelMe 3.0](http://labelme.csail.mit.edu/Release3.0) | ✔️ | ✔️ |
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| [ImageNet](http://www.image-net.org) | ✔️ | ✔️ |
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| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) | ✔️ | ✔️ |
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| [WIDER Face](http://shuoyang1213.me/WIDERFACE/) | ✔️ | ✔️ |
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| [VGGFace2](https://github.com/ox-vgg/vgg_face2) | ✔️ | ✔️ |
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| [Market-1501](https://www.aitribune.com/dataset/2018051063) | ✔️ | ✔️ |
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| [ICDAR13/15](https://rrc.cvc.uab.es/?ch=2) | ✔️ | ✔️ |
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| [Open Images V6](https://storage.googleapis.com/openimages/web/index.html) | ✔️ | ✔️ |
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| [Cityscapes](https://www.cityscapes-dataset.com/login/) | ✔️ | ✔️ |
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| [KITTI](http://www.cvlibs.net/datasets/kitti/) | ✔️ | ✔️ |
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| [LFW](http://vis-www.cs.umass.edu/lfw/) | ✔️ | ✔️ |
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<!--lint enable maximum-line-length-->
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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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| ------------------------------------------------------------------------------------------------------- | ---------- | ---------- | --- | --- |
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| [Deep Extreme Cut](/serverless/openvino/dextr/nuclio) | interactor | OpenVINO | ✔️ | |
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| [Faster RCNN](/serverless/openvino/omz/public/faster_rcnn_inception_v2_coco/nuclio) | detector | OpenVINO | ✔️ | |
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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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| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio) | reid | OpenVINO | ✔️ | |
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| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector | OpenVINO | ✔️ | |
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| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio) | detector | OpenVINO | ✔️ | |
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| [YOLO v5](/serverless/pytorch/ultralytics/yolov5/nuclio) | detector | PyTorch | ✔️ | |
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| [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio) | tracker | PyTorch | ✔️ | ✔️ |
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| [f-BRS](/serverless/pytorch/saic-vul/fbrs/nuclio) | interactor | PyTorch | ✔️ | |
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| [HRNet](/serverless/pytorch/saic-vul/hrnet/nuclio) | interactor | PyTorch | | ✔️ |
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| [Inside-Outside Guidance](/serverless/pytorch/shiyinzhang/iog/nuclio) | interactor | PyTorch | ✔️ | |
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| [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio) | detector | TensorFlow | ✔️ | ✔️ |
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| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio) | detector | TensorFlow | ✔️ | ✔️ |
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| [RetinaNet](serverless/pytorch/facebookresearch/detectron2/retinanet/nuclio) | detector | PyTorch | ✔️ | ✔️ |
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| [Face Detection](/serverless/openvino/omz/intel/face-detection-0205/nuclio) | detector | OpenVINO | ✔️ | |
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<!--lint enable maximum-line-length-->
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## License
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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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FFmpeg is an open source framework licensed under LGPL and GPL.
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See [https://www.ffmpeg.org/legal.html](https://www.ffmpeg.org/legal.html). You are solely responsible
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for determining if your use of FFmpeg requires any
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additional licenses. CVAT.ai Corporation 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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## Where to ask questions
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[Gitter chat][gitter-url]: you can post CVAT usage related questions there.
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Typically they get answered fast by the core team or community. There you can also browse other common questions.
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[Discord][discord-url] is the place to also ask questions or discuss any other stuff related to CVAT.
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[GitHub issues](https://github.com/cvat-ai/cvat/issues): please post them for feature requests or bug reports.
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If it's a bug, please add the steps to reproduce it.
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[\#cvat](https://stackoverflow.com/search?q=%23cvat) tag on StackOverflow is one more way to ask
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questions and get our support.
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[contact@cvat.ai](mailto:contact+github@cvat.ai): reach out to us with feedback, comments, or inquiries.
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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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<!-- prettier-ignore-start -->
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<!-- Badges -->
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[docker-server-pulls-img]: https://img.shields.io/docker/pulls/cvat/server.svg?style=flat-square&label=server%20pulls
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[docker-server-image-url]: https://hub.docker.com/r/cvat/server
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[docker-ui-pulls-img]: https://img.shields.io/docker/pulls/cvat/ui.svg?style=flat-square&label=UI%20pulls
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[docker-ui-image-url]: https://hub.docker.com/r/cvat/ui
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[ci-img]: https://github.com/cvat-ai/cvat/workflows/CI/badge.svg?branch=develop
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[ci-url]: https://github.com/cvat-ai/cvat/actions
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[gitter-img]: https://img.shields.io/gitter/room/opencv-cvat/public?style=flat
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[gitter-url]: https://gitter.im/opencv-cvat
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[coverage-img]: https://coveralls.io/repos/github/cvat-ai/cvat/badge.svg?branch=develop
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[coverage-url]: https://coveralls.io/github/cvat-ai/cvat?branch=develop
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[doi-img]: https://zenodo.org/badge/139156354.svg
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[doi-url]: https://zenodo.org/badge/latestdoi/139156354
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[discord-img]: https://img.shields.io/discord/1000789942802337834?label=discord
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[discord-url]: https://discord.gg/fNR3eXfk6C
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