Repository files navigation

This Fork (branch:storage_limit_removal) disables the health check to see if there is more than 10% free storage space. The CVAT standard docker container cannot start the web server properly if there is no more than 10% of the total storage space available due to django's health check function.

see:

• CVAT fails health check using >90% disk #5449

• cvat_custom/cvat/settings/production.py

  Lines 15 to 17 inf8935a4

  	HEALTH_CHECK= {
  	'DISK_USAGE_MAX':None,
  	}

• cvat_custom/cvat/settings/testing_rest.py

  Lines 24 to 26 inf8935a4

  	HEALTH_CHECK= {
  	'DISK_USAGE_MAX':None,
  	}

git clone https://github.com/PINTO0309/cvat_custom.gitcd cvat_customdocker compose \-f docker-compose.yml \-f docker-compose.dev.yml \builddocker compose up -ddockerexec -it cvat_server bash -ic'python3 ~/manage.py createsuperuser'

Accesshttp://localhost:8080 from your browser.

git clone https://github.com/opencv/cvat.gitdocker compose up -d

Accesshttp://localhost:8080 from your browser.

Wait 30 seconds for error to occur.

Totally useless error messages are displayed. This error message does not explain anything about the nature of the error. The real cause of the error is that the WEB server fails to start if the storage space is less than 10% free in the django health check.

Cannot connect to the serverMake sure the CVAT backend and all necessary services (Database, Redis and Open Policy Agent) are running and available.If you upgraded from version 2.2.0 or earlier, manual actions may be needed, see the Upgrade Guide.

CVAT is an interactive video and image annotationtool for computer vision. It is used by tens of thousands of users andcompanies around the world. Our mission is to help developers, companies, andorganizations around the world to solve real problems using the Data-centricAI approach.

Start using CVAT online:cvat.ai. You can use it for free,orsubscribe to get unlimited data,organizations, autoannotations, andRoboflow and HuggingFace integration.

Or set CVAT up as a self-hosted solution:Self-hosted Installation Guide.We provideEnterprise support forself-hosted installations with premium features: SSO, LDAP, Roboflow andHuggingFace integrations, and advanced analytics (coming soon). We alsodo trainings and a dedicated support with 24 hour SLA.

• Installation guide
• Manual
• Contributing
• Datumaro dataset framework
• Server API
• Python SDK
• Command line tool
• XML annotation format
• AWS Deployment Guide
• Frequently asked questions
• Where to ask questions

CVAT is used by teams all over the world. In the list, you can find key companies whichhelp us support the product or an essential part of our ecosystem. If you use us,please drop us a line atcontact@cvat.ai.

• Human Protocol uses CVAT as a way of adding annotation service to the Human Protocol.
• FiftyOne is an open-source dataset curation and model analysistool for visualizing, exploring, and improving computer vision datasets and models that aretightly integrated with CVATfor annotation and label refinement.

ATLANTIS, an open-source dataset for semantic segmentationof waterbody images, developed byiWERS group in theDepartment of Civil and Environmental Engineering at the University of South Carolina is using CVAT.

For developing a semantic segmentation dataset using CVAT, see:

• ATLANTIS published article
• ATLANTIS Development Kit
• ATLANTIS annotation tutorial videos.

This is an online version of CVAT. It's free, efficient, and easy to use.

cvat.ai runs the latest version of the tool. You can create upto 10 tasks there and upload up to 500Mb of data to annotate. It will only bevisible to you or the people you assign to it.

For now, it does not haveanalytics featureslike management and monitoring the data annotation team. It also does not allow exporting images, just the annotations.

We plan to enhancecvat.ai with new powerful features. Stay tuned!

Prebuilt docker images are the easiest way to start using CVAT locally. They are available on Docker Hub:

• cvat/server
• cvat/ui

The images have been downloaded more than 1M times so far.

Here are some screencasts showing how to use CVAT.

Computer Vision Annotation Course:we introduce our course series designed to help you annotate data faster and betterusing CVAT. This course is about CVAT deployment and integrations, it includespresentations 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 modeltraining process. Some resources you can use to learn more about how to use them.
• Deployment and use CVAT. Use the app online atapp.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-minutetour of the interface, a breakdown of CVAT’s internals, and a demonstration of howto deploy CVAT using Docker Compose.

Product tour: 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 useapp.cvat.ai: how to sign up, upload your data, annotate it, and download it.

For feedback, please seeContact us

• Documentation

• Install withpip install cvat-sdk
• PyPI package homepage
• Documentation

• Install withpip install cvat-cli
• PyPI package homepage
• Documentation

CVAT supports multiple annotation formats. You can select the formatafter clicking theUpload annotation andDump annotation buttons.Datumaro dataset framework allowsadditional dataset transformations with its command line tool and Python library.

For more information about the supported formats, see:Annotation Formats.

CVAT supports automatic labeling. It can speed up the annotation processup to 10x. Here is a list of the algorithms we support, and the platforms they can be run on:

The code is released under theMIT License.

This software uses LGPL-licensed libraries from theFFmpeg project.The exact steps on how FFmpeg was configured and compiled can be found in theDockerfile.

FFmpeg is an open-source framework licensed under LGPL and GPL.Seehttps://www.ffmpeg.org/legal.html. You are solely responsiblefor determining if your use of FFmpeg requires anyadditional licenses. CVAT.ai Corporation is not responsible for obtaining anysuch licenses, nor liable for any licensing fees due inconnection with your use of FFmpeg.

Gitter 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 is the place to also ask questions or discuss any other stuff related to CVAT.

LinkedIn for the company and work-related questions.

YouTube to see screencast and tutorials about the CVAT.

GitHub issues for feature requests or bug reports.If it's a bug, please add the steps to reproduce it.

#cvat tag on StackOverflow is one more way to askquestions and get our support.

contact@cvat.ai to reach out to us if you need commercial support.

• Intel AI blog: New Computer Vision Tool Accelerates Annotation of Digital Images and Video

• Intel Software: Computer Vision Annotation Tool: A Universal Approach to Data Annotation

• VentureBeat: Intel open-sources CVAT, a toolkit for data labeling

• How to Use CVAT (Roboflow guide)

• How to auto-label data in CVAT with one of 50,000+ models on Roboflow Universe