* Adding cypress support for running tests in the CI. Modifications: - .travis.yml: adding command for run testin via cypress - Dockerfile.ci: addin install necessary package for correct cypress works. Adding install cypress via npm. - docker-compose.ci.yml: Starting necessary docker containers for cypress testing. - tests/cypress.json: Cypress configuration file - tests/cypress/integration/auth_page.js: little bit fix. - CONTRIBUTING.md: add documentation how to run cypress tests locally * Add documentation how to run cypress tests locally. Remove wait() from auth_page.js. * Add run cypress in CI with the different config file cypress_ci.json Update CONTRIBUTING.md. Co-authored-by: Dmitry Kruchinin <dmitryx.kruchinin@intel.com> |
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README.md
Computer Vision Annotation Tool (CVAT)
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 cvat.org.
Documentation
- Installation guide
- User's guide
- Django REST API documentation
- Datumaro dataset framework
- Command line interface
- XML annotation format
- AWS Deployment Guide
- Frequently asked questions
- Questions
Screencasts
- Introduction
- Annotation mode
- Interpolation of bounding boxes
- Interpolation of polygons
- Attribute mode
- Segmentation mode
- Tutorial for polygons
- Semi-automatic segmentation
Supported annotation formats
Format selection is possible after clicking on the Upload annotation and Dump annotation buttons. Datumaro dataset framework allows additional dataset transformations via its command line tool and Python library.
| Annotation format | Import | Export |
|---|---|---|
| CVAT for images | X | X |
| CVAT for a video | X | X |
| Datumaro | X | |
| PASCAL VOC | X | X |
| Segmentation masks from PASCAL VOC | X | X |
| YOLO | X | X |
| MS COCO Object Detection | X | X |
| TFrecord | X | X |
| MOT | X | X |
| LabelMe 3.0 | X | X |
Links
- 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
Online demo: cvat.org
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:
Limitations:
- No more than 10 tasks per user
- Uploaded data is limited to 500Mb
REST API
Automatically generated Swagger documentation for Django REST API is
available on <cvat_origin>/api/swagger
(default: localhost:8080/api/swagger).
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')
LICENSE
Code released under the MIT License.
Questions
CVAT usage related questions or unclear concepts can be posted in our Gitter chat for quick replies from 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.
If you are not sure or just want to browse other users common questions, Gitter chat is the way to go.
Other ways to ask questions and get our support:
- #cvat tag on StackOverflow*
- Forum on Intel Developer Zone

