diff --git a/README.md b/README.md index 9223b1c4..f9fbcc55 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +![CVAT logo](site/content/en/images/cvat_poster_with_name.png) # Computer Vision Annotation Tool (CVAT) [![CI][ci-img]][ci-url] @@ -7,19 +8,25 @@ [![ui pulls][docker-ui-pulls-img]][docker-ui-image-url] [![DOI][doi-img]][doi-url] -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 [app.cvat.ai](https://app.cvat.ai). +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. CVAT is free and open-source. -![CVAT screenshot](site/content/en/images/cvat.jpg) +**A new repo**: CVAT core team moved the active development of the tool +to this new repository. Our mission is to help developers, companies and +organizations around the world to solve real problems using the Data-centric +AI approach. -## Documentation +Start using CVAT online for free: [cvat.ai](https://cvat.ai). Or set it up as a self-hosted solution: +[read here](https://cvat-ai.github.io/cvat/docs/administration/basics/installation/). + +![CVAT screencast](site/content/en/images/cvat-ai-screencast.gif) + +## Quick start ⚡ -- [Contributing](https://cvat-ai.github.io/cvat/docs/contributing/) - [Installation guide](https://cvat-ai.github.io/cvat/docs/administration/basics/installation/) - [Manual](https://cvat-ai.github.io/cvat/docs/manual/) +- [Contributing](https://cvat-ai.github.io/cvat/docs/contributing/) - [Django REST API documentation](https://cvat-ai.github.io/cvat/docs/administration/basics/rest_api_guide/) - [Datumaro dataset framework](https://github.com/cvat-ai/datumaro/blob/develop/README.md) - [Command line interface](https://cvat-ai.github.io/cvat/docs/manual/advanced/cli/) @@ -28,7 +35,61 @@ annotation team. Try it online [app.cvat.ai](https://app.cvat.ai). - [Frequently asked questions](https://cvat-ai.github.io/cvat/docs/faq/) - [Questions](#questions) -## Screencasts +## Partners ❤️ + +CVAT is used by teams all over the world. If you use us, please drop us a line at +[contact@cvat.ai](mailto:contact+github@cvat.ai) - and we'll add you to this list. + +- [ATLANTIS](https://github.com/smhassanerfani/atlantis) is an open-source dataset for semantic segmentation + of waterbody images, depevoped by [iWERS](http://ce.sc.edu/iwers/) group in the + Department of Civil and Environmental Engineering at University of South Carolina, is using CVAT. + For developing a semantic segmentation dataset using CVAT, please check + [ATLANTIS published article](https://www.sciencedirect.com/science/article/pii/S1364815222000391), + [ATLANTIS Development Kit](https://github.com/smhassanerfani/atlantis/tree/master/adk) + and [annotation tutorial videos](https://www.youtube.com/playlist?list=PLIfLGY-zZChS5trt7Lc3MfNhab7OWl2BR). +- [Onepanel](https://github.com/onepanelio/core) is an open source + vision AI platform that fully integrates CVAT with scalable data processing + and parallelized training pipelines. +- [DataIsKey](https://dataiskey.eu/annotation-tool/) uses CVAT as their prime data labeling tool + to offer annotation services for projects of any size. +- [Human Protocol](https://hmt.ai) uses CVAT as a way of adding annotation service to the human protocol. +- [Cogito Tech LLC](https://bit.ly/3klT0h6), a Human-in-the-Loop Workforce Solutions Provider, used CVAT + in annotation of about 5,000 images for a brand operating in the fashion segment. +- [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 is +[tightly integrated](https://voxel51.com/docs/fiftyone/integrations/cvat.html) with CVAT +for annotation and label refinement. + +## 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 people you assign to it. + +For now, it does not have [analytics features](https://cvat-ai.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. + +## REST API + +CVAT has a REST API. Its current version is `2.0-alpha`. We focus on its +improvement and therefore REST API may be changed in the next release. + +## Screencasts 🎦 + +Here are some screencasts showing how to use CVAT. - [Introduction](https://youtu.be/JERohTFp-NI) - [Annotation mode](https://youtu.be/vH_639N67HI) @@ -42,12 +103,12 @@ annotation team. Try it online [app.cvat.ai](https://app.cvat.ai). ## Supported annotation formats -Format selection is possible after clicking on the Upload annotation and Dump -annotation buttons. [Datumaro](https://github.com/cvat-ai/datumaro) +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 via its command line tool and Python library. -For more information about supported formats look at the +For more information about supported formats, look at the [documentation](https://cvat-ai.github.io/cvat/docs/manual/advanced/formats/). @@ -79,6 +140,10 @@ For more information about supported formats look at the ## Deep learning serverless functions for automatic labeling +CVAT supports automatic labelling. 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 ran on: + | Name | Type | Framework | CPU | GPU | @@ -102,35 +167,9 @@ For more information about supported formats look at the -## Online demo: [app.cvat.ai](https://app.cvat.ai) - -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](https://cvat-ai.github.io/cvat/docs/administration/advanced/analytics/) - -Limitations: - -- No more than 10 tasks per user -- Uploaded data is limited to 500Mb - -## Prebuilt Docker images +## License -Prebuilt docker images for CVAT releases are available on Docker Hub: - -- [cvat/server](https://hub.docker.com/r/cvat/server) -- [cvat/ui](https://hub.docker.com/r/cvat/ui) - -## REST API -The current REST API version is `2.0-alpha`. We focus on its improvement and therefore -REST API may be changed in the next release. - -## LICENSE - -Code released under the [MIT License](https://opensource.org/licenses/MIT). +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). @@ -142,28 +181,6 @@ additional licenses. CVAT.ai is not responsible for obtaining any such licenses, nor liable for any licensing fees due in connection with your use of FFmpeg. -## Partners - -- [ATLANTIS](https://github.com/smhassanerfani/atlantis) is an open-source dataset for semantic segmentation - of waterbody images, depevoped by [iWERS](http://ce.sc.edu/iwers/) group in the - Department of Civil and Environmental Engineering at University of South Carolina, using CVAT. - For developing a semantic segmentation dataset using CVAT, please check - [ATLANTIS published article](https://www.sciencedirect.com/science/article/pii/S1364815222000391), - [ATLANTIS Development Kit](https://github.com/smhassanerfani/atlantis/tree/master/adk) - and [annotation tutorial videos](https://www.youtube.com/playlist?list=PLIfLGY-zZChS5trt7Lc3MfNhab7OWl2BR). -- [Onepanel](https://github.com/onepanelio/core) is an open source - vision AI platform that fully integrates CVAT with scalable data processing - and parallelized training pipelines. -- [DataIsKey](https://dataiskey.eu/annotation-tool/) uses CVAT as their prime data labeling tool - to offer annotation services for projects of any size. -- [Human Protocol](https://hmt.ai) uses CVAT as a way of adding annotation service to the human protocol. -- [Cogito Tech LLC](https://bit.ly/3klT0h6), a Human-in-the-Loop Workforce Solutions Provider, used CVAT - in annotation of about 5,000 images for a brand operating in the fashion segment. -- [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 is -[tightly integrated](https://voxel51.com/docs/fiftyone/integrations/cvat.html) with CVAT -for annotation and label refinement. - ## Questions CVAT usage related questions or unclear concepts can be posted in our @@ -172,7 +189,7 @@ 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). +report) on [GitHub\* issues](https://github.com/cvat-ai/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. diff --git a/site/content/en/images/cvat-ai-screencast.gif b/site/content/en/images/cvat-ai-screencast.gif new file mode 100644 index 00000000..1c8fad6a Binary files /dev/null and b/site/content/en/images/cvat-ai-screencast.gif differ diff --git a/site/content/en/images/cvat_poster_with_name.png b/site/content/en/images/cvat_poster_with_name.png new file mode 100644 index 00000000..fab01231 Binary files /dev/null and b/site/content/en/images/cvat_poster_with_name.png differ