Link to the old videos removed as there were changes made in CVAT
interface and now these videos are a bit confusing Links to playlists of
the updated videos were added with the description what these videos
contact Contacts sections updated
General proofreading
[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.
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For feedback, please see [Contact us](#contact-us)
## API
@ -115,13 +125,13 @@ Here are some screencasts showing how to use CVAT.
## Supported annotation formats
CVAT supports multiple annotation formats. You can select the format after clicking the "Upload annotation" and "Dump
@ -156,9 +166,8 @@ For more information about the 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:
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:
<!--lint disable maximum-line-length-->
@ -188,34 +197,37 @@ can be ran on:
The code is released under the [MIT License](https://opensource.org/licenses/MIT).
This software uses LGPLlicensed libraries from the [FFmpeg](https://www.ffmpeg.org) project.
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 opensource framework licensed under LGPL and GPL.
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.
## Where to ask questions
## 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.
[Gitter chat][gitter-url]: you can post CVAT usage related questions there.
Typically they get answered fast by the core team or community. There you can also browse other common questions.
[LinkedIn](https://www.linkedin.com/company/cvat-ai/) for the company and work-related questions.
[Discord][discord-url] is the place to also ask questions or discuss any other stuff related to CVAT.
[YouTube](https://www.youtube.com/@cvat-ai) to see screencast and tutorials about the CVAT.
[GitHub issues](https://github.com/cvat-ai/cvat/issues): please post them for feature requests or bug reports.
[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
[#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): reach out to us with feedback, comments, or inquiries.
[contact@cvat.ai](mailto:contact+github@cvat.ai) to reach out to us with feedback, comments, or inquiries.
## Links
- [Computer Vision Annotation Tool (CVAT) has a New Home](https://opencv.org/opencv-and-cvat-computer-vision-annotation-tool-intel/)
- [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/)
Mariia Acoca
3 years ago
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