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* Test Commit for Remove Range Test Commit for Remove Range * Remove annotations in range merged with remove annotations button merged Remove annotations in range merged with remove annotations button merged * Update annotation-reducer.ts * Update annotation-actions.ts * Update annotation-reducer.ts * Converting remove range component to hook based component Removed all the global states previously used and converted all the parameters to local state in annotation menu and remove range component. * Improved clear in cvat core and implemented remove range Added arguments of startframe and endframe to clear method in annotation-collection, and also added the updating of the states with payload on removeannotationsinrangeasync action in the reducer. * Matching only the needed parts There are few additional old files that were needed to be removed to be completely matched with develop branch of cvat * Delete out.json * Update annotations-collection.js * Added a checkbox to remove range modal Added a checkbox to remove range modal that can be used to select if only the keyframes should be deleted in tracks or the whole track * ESLint fixed All the updated files were formatted as per ESLint except one line in that even cvat base is also overlooking i.e. Row 162, Column 15: "JSX props should not use functions" in cvat\cvat-ui\src\components\annotation-page\top-bar\annotation-menu.tsx. * More ESLint and other updates Changed all the suggested changes and also removed unnecessary files in dist. Removed unnecessary explicit removals in objects and additional wrappers. * Update annotation-menu.tsx Fixed the mistake of wrong variable name. * Update remove-range-confirm.tsx Additional ESLint Issue fixed * Changed the approach of removeAnnotations modal Changed the approach of removeAnnotations modal so that it could match the implementation of all the other components * Added to changelog Fixed type annotations in the annotation-menu component for remove annotations, and updated cvat-ui and cvat-core npm versions. |
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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
- Contributing
- Installation guide
- Manual
- 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 | X |
| f-BRS | interactor | PyTorch | X | |
| HRNet | interactor | PyTorch | X | |
| Inside-Outside Guidance | interactor | PyTorch | X | |
| Faster RCNN | detector | TensorFlow | X | X |
| Mask RCNN | detector | TensorFlow | X | X |
| RetinaNet | detector | PyTorch | 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:
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
Projects using CVAT
- Onepanel is an open source vision AI platform that fully integrates CVAT with scalable data processing and parallelized training pipelines.
- DataIsKey uses CVAT as their prime data labeling tool to offer annotation services for projects of any size.
- Human Protocol uses CVAT as a way of adding annotation service to the human protocol.