* CVAT 3D Milestone-5 changes * Fixed Lint errors * test * Fixed review comments * Fixed Lint errors * M5 Box operations * M5- Bounding Box Operations * M5 Fixes * Cypress test added wait time * Changed the scene color to white * Fixed year in header * CameraAction naming convention changes * M5-Fixes * Fixed test fail issue * M5- Major fixes * Trigger notification * Minor Fixes M5 * M5 - Other open items * Trigger notification * Error fix * Trigger notification * Unit Test fail fix * Unit Test fail fix * Trigger notification * CVAT-3D M5 Review Fixes -1 * Add cypress test. * try run canvas3d without headless * hide tooltips * Tests adaptations * Review Fixes -2 * Review Fixes -3 * Merge changes * Revert case 64 * Revert to cvat-3D-M5 * The test adaptation * Add comments * Added wait time in cypress * Add condition for compare images * cypress test. cuboid context menu. * Cypress test. Make a copy. * Cypress test. Cuboid propagate. * Cypress test. Opacity. Outlined borders. Add cypress command. Tests adaptation. * Cypress test. Cuboid grouping. * Fixes -1 * Trigger notification * Case 64 adaptation. * Updated yml for cypress tests 3D * Cypress test. Redraw a cuboid. * Updated tests * Latest tests * Cypress test. Cancel cuboid drawning. * Cypress test. "Move the image" button interaction. * Cypress test. Delete a cuboid. * Cypress test. Save a job. Remove annotations. * Fix case 88 * Fix case 88 * Cypress test. Filters. * Minor rework case88 * Applying comments. Co-authored-by: cdp <cdp123> Co-authored-by: Jayraj <jayrajsolanki96@gmail.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
- Tag annotation video
- 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.
For more information about supported formats look at the documentation.
| 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 |
| ImageNet | X | X |
| CamVid | X | X |
| WIDER Face | X | X |
| VGGFace2 | X | X |
| Market-1501 | X | X |
| ICDAR13/15 | X | X |
Deep learning serverless functions for automatic labeling
| Name | Type | Framework | CPU | GPU |
|---|---|---|---|---|
| Deep Extreme Cut | interactor | OpenVINO | X | |
| Faster RCNN | detector | OpenVINO | X | |
| Mask RCNN | detector | OpenVINO | X | |
| YOLO v3 | detector | OpenVINO | X | |
| Object reidentification | reid | OpenVINO | X | |
| Semantic segmentation for ADAS | detector | OpenVINO | X | |
| Text detection v4 | detector | OpenVINO | X | |
| SiamMask | tracker | PyTorch | X | |
| f-BRS | interactor | PyTorch | X | |
| Inside-Outside Guidance | interactor | PyTorch | X | |
| Faster RCNN | detector | TensorFlow | X | X |
| Mask RCNN | detector | TensorFlow | X | X |
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
Prebuilt Docker images
Prebuilt docker images for CVAT releases are available on Docker Hub:
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 visible on allowed hosts, Update environment
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.
This software uses LGPL licensed libraries from the FFmpeg project. The exact steps on how FFmpeg was configured and compiled can be found in the Dockerfile.
FFmpeg is an open source framework licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due in connection with your use of FFmpeg.
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
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

