CVAT is an interactive video and image annotation tool for computer vision. It is used by tens of thousands of users and companies around the world. CVAT is free and open-source.
A new repo: CVAT core team moved the active development of the tool to this new repository. Our mission is to help developers, companies and organizations around the world to solve real problems using the Data-centric AI approach.
Start using CVAT online for free: cvat.ai. Or set it up as a self-hosted solution: read here.
CVAT is used by teams all over the world. If you use us, please drop us a line at contact@cvat.ai - and we'll add you to this list.
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 up to 10 tasks there and upload up to 500Mb of data to annotate. It will only be visible to you or people you assign to it.
For now, it does not have analytics features like management and monitoring the data annotation team.
We plan to enhance cvat.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:
The images have been downloaded more than 1M times so far.
CVAT has a REST API: documentation.
Its current version is 2.0-alpha
. We focus on its improvement, and the API may be changed in the next releases.
Here are some screencasts showing how to use CVAT.
CVAT supports multiple annotation formats. You can select the format after clicking 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 the supported formats, look at the documentation.
Annotation format | Import | Export |
---|---|---|
CVAT for images | ✔️ | ✔️ |
CVAT for a video | ✔️ | ✔️ |
Datumaro | ✔️ | |
PASCAL VOC | ✔️ | ✔️ |
Segmentation masks from PASCAL VOC | ✔️ | ✔️ |
YOLO | ✔️ | ✔️ |
MS COCO Object Detection | ✔️ | ✔️ |
TFrecord | ✔️ | ✔️ |
MOT | ✔️ | ✔️ |
LabelMe 3.0 | ✔️ | ✔️ |
ImageNet | ✔️ | ✔️ |
CamVid | ✔️ | ✔️ |
WIDER Face | ✔️ | ✔️ |
VGGFace2 | ✔️ | ✔️ |
Market-1501 | ✔️ | ✔️ |
ICDAR13/15 | ✔️ | ✔️ |
Open Images V6 | ✔️ | ✔️ |
Cityscapes | ✔️ | ✔️ |
KITTI | ✔️ | ✔️ |
LFW | ✔️ | ✔️ |
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:
Name | Type | Framework | CPU | GPU |
---|---|---|---|---|
Deep Extreme Cut | interactor | OpenVINO | ✔️ | |
Faster RCNN | detector | OpenVINO | ✔️ | |
Mask RCNN | detector | OpenVINO | ✔️ | |
YOLO v3 | detector | OpenVINO | ✔️ | |
Object reidentification | reid | OpenVINO | ✔️ | |
Semantic segmentation for ADAS | detector | OpenVINO | ✔️ | |
Text detection v4 | detector | OpenVINO | ✔️ | |
YOLO v5 | detector | PyTorch | ✔️ | |
SiamMask | tracker | PyTorch | ✔️ | ✔️ |
f-BRS | interactor | PyTorch | ✔️ | |
HRNet | interactor | PyTorch | ✔️ | |
Inside-Outside Guidance | interactor | PyTorch | ✔️ | |
Faster RCNN | detector | TensorFlow | ✔️ | ✔️ |
Mask RCNN | detector | TensorFlow | ✔️ | ✔️ |
RetinaNet | detector | PyTorch | ✔️ | ✔️ |
Face Detection | detector | OpenVINO | ✔️ |
The code is 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. 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.
Gitter chat: 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.
Discord is the place to also ask questions or discuss any other stuff related to CVAT.
GitHub issues: please post them 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 ask questions and get our support.
contact@cvat.ai: reach out to us with feedback, comments, or inquiries.
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