cvat

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

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cvat

12,212

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

labelme

13,138

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.

VoTT

4,277

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

Quick Overview

CVAT (Computer Vision Annotation Tool) is an open-source web-based image and video annotation tool for computer vision. It is designed to provide users with convenient and intuitive tools for annotating data used in machine learning and computer vision tasks. CVAT supports various annotation shapes, interpolation of bounding boxes between key frames, and automatic annotation using TensorFlow Object Detection API integration.

Pros

Versatile annotation support for images and videos
User-friendly web interface with keyboard shortcuts
Integration with popular ML frameworks and export to various formats
Active development and community support

Cons

Steep learning curve for advanced features
Resource-intensive for large datasets
Limited built-in project management features
Occasional performance issues with complex annotations

Code Examples

As CVAT is primarily a web-based tool, there aren't typical code examples for its usage. However, here are some examples of how to interact with CVAT programmatically using its REST API:

Authenticating with the CVAT server:

import requests

session = requests.Session()
session.post('http://localhost:8080/api/v1/auth/login', json={
    "username": "user",
    "password": "password"
})

Creating a new task:

response = session.post('http://localhost:8080/api/v1/tasks', json={
    "name": "My Task",
    "labels": [{"name": "car"}, {"name": "person"}],
    "project_id": 1
})
task_id = response.json()['id']

Uploading data to a task:

with open('image.jpg', 'rb') as f:
    session.post(f'http://localhost:8080/api/v1/tasks/{task_id}/data', 
                 files={'client_files[0]': f})

Getting Started

To get started with CVAT:

Install Docker and Docker Compose

Clone the CVAT repository:

git clone https://github.com/opencv/cvat
cd cvat

Build and run CVAT:
```
docker-compose up -d
```
Open http://localhost:8080 in your web browser
Create an account and start annotating

For more detailed instructions, refer to the official CVAT documentation.

Competitor Comparisons

cvat

12,212

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

Pros of CVAT

More comprehensive documentation and user guides
Larger community and more frequent updates
Supports a wider range of annotation tasks and formats

Cons of CVAT

Higher system requirements for installation and operation
Steeper learning curve for new users
More complex setup process for advanced features

Code Comparison

CVAT:

from cvat_sdk import make_client
from cvat_sdk.core.proxies.tasks import ResourceType

client = make_client("https://your.cvat.instance")
client.tasks.create_from_data(
    name="My Task",
    labels=[{"name": "car"}, {"name": "person"}],
    resource_type=ResourceType.LOCAL,
    resources=["path/to/image1.jpg", "path/to/image2.jpg"],
)

CVAT>:

# No equivalent code available for direct comparison
# CVAT> is not a separate repository or project
# It may refer to a specific branch or version of CVAT

Note: CVAT> is not a separate repository from CVAT. It likely refers to a specific branch, version, or feature of the main CVAT project. Therefore, a direct code comparison is not applicable in this case.

labelme

13,138

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

Pros of labelme

Lightweight and easy to install, suitable for small-scale projects
Simple and intuitive interface for basic image annotation tasks
Supports polygon, rectangle, circle, line, and point annotations

Cons of labelme

Limited features compared to CVAT's comprehensive toolset
Less suitable for large-scale, collaborative annotation projects
Lacks advanced video annotation capabilities

Code Comparison

labelme:

from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
    label_name = shape['label']
    if label_name in label_name_to_value:
        label_value = label_name_to_value[label_name]
    else:
        label_value = len(label_name_to_value)
        label_name_to_value[label_name] = label_value

CVAT:

from cvat_sdk import make_client
from cvat_sdk.core.proxies.tasks import ResourceType

client = make_client(host="localhost", port=8080)
client.tasks.create(
    name="My task",
    labels=[{"name": "car"}, {"name": "person"}],
    resource_type=ResourceType.LOCAL,
    resources=["path/to/video.mp4"],
)

labelImg

22,458

Pros of labelImg

Lightweight and easy to install
Simple, intuitive interface for basic image labeling tasks
Supports multiple image formats and annotation types

Cons of labelImg

Limited to 2D image annotation
Lacks advanced features like automatic annotation or AI-assisted labeling
No built-in project management or collaboration tools

Code Comparison

labelImg (Python):

def saveFile(self, _value=False):
    if self.filePath:
        try:
            self.saveLabels(self.filePath)
            self.setClean()
            return True
        except:
            self.errorMessage(u'Error saving file', u'Error saving labels to file')
            return False

CVAT (JavaScript):

async function saveAnnotations(session, annotations) {
    const data = {
        version: 1,
        tags: [],
        shapes: annotations.map((anno) => ({
            type: anno.type,
            points: anno.points,
            label: anno.label,
        })),
    };
    await session.annotations.put(data);
}

CVAT offers a more comprehensive annotation platform with advanced features, while labelImg provides a simpler solution for basic image labeling tasks. CVAT's code demonstrates its support for multiple annotation types and server-side storage, whereas labelImg focuses on local file operations.

VoTT

4,277

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

Pros of VoTT

Simpler setup and installation process
More user-friendly interface for beginners
Better support for offline usage

Cons of VoTT

Limited support for complex annotation tasks
Fewer advanced features compared to CVAT
Less active development and community support

Code Comparison

VoTT (React-based UI):

export default class Canvas extends React.Component<ICanvasProps, ICanvasState> {
    public static defaultProps: ICanvasProps = {
        selectedAsset: null,
        editorMode: EditorMode.Select,
        project: null,
        lockedTags: [],
        hoveredLabel: null,
    };
}

CVAT (Vue.js-based UI):

export default {
    name: 'CvatCanvasInstance',
    props: {
        curZLayer: {
            type: Number,
            default: 0,
        },
        mode: {
            type: String,
            default: 'idle',
        },
    },
    data() {
        return {
            canvasInstance: null,
        };
    },
};

Both repositories offer annotation tools for computer vision tasks, but CVAT provides more advanced features and scalability for larger projects. VoTT is more suitable for simpler tasks and individual users, while CVAT is better for team collaborations and complex annotation workflows. The code snippets show different frontend frameworks used: React for VoTT and Vue.js for CVAT.

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README

CVAT Platform

Computer Vision Annotation Tool (CVAT)

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. 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: cvat.ai. You can use it for free, or subscribe to get unlimited data, organizations, autoannotations, and Roboflow and HuggingFace integration.

Or set CVAT up as a self-hosted solution: Self-hosted Installation Guide. We provide Enterprise support for self-hosted installations with premium features: SSO, LDAP, Roboflow and HuggingFace integrations, and advanced analytics (coming soon). We also do trainings and a dedicated support with 24 hour SLA.

Quick start â¡

Partners â¤ï¸

CVAT is used by teams all over the world. In the list, you can find key companies which help us support the product or an essential part of our ecosystem. If you use us, please drop us a line at contact@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 analysis tool for visualizing, exploring, and improving computer vision datasets and models that are tightly integrated with CVAT for annotation and label refinement.

Public datasets

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

For developing a semantic segmentation dataset using CVAT, see:

CVAT online: cvat.ai

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 the people you assign to it.

For now, it does not have analytics features like management and monitoring the data annotation team. It also does not allow exporting images, just the annotations.

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

Prebuilt Docker images ð³

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.

Screencasts ð¦

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 better using CVAT. This course is about CVAT deployment and integrations, it includes presentations 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 model training process. Some resources you can use to learn more about how to use them.
Deployment and use CVAT. Use the app online at app.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-minute tour of the interface, a breakdown of CVATâs internals, and a demonstration of how to 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 use app.cvat.ai: how to sign up, upload your data, annotate it, and download it.

For feedback, please see Contact us

API

Documentation

SDK

Install with pip install cvat-sdk
PyPI package homepage
Documentation

CLI

Install with pip install cvat-cli
PyPI package homepage
Documentation

Supported annotation formats

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 with its command line tool and Python library.

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

Annotation format	Import	Export
CVAT for images	âï¸	âï¸
CVAT for a video	âï¸	âï¸
Datumaro	âï¸	âï¸
PASCAL VOC	âï¸	âï¸
Segmentation masks from PASCAL VOC	âï¸	âï¸
YOLO	âï¸	âï¸
MS COCO Object Detection	âï¸	âï¸
MS COCO Keypoints Detection	âï¸	âï¸
MOT	âï¸	âï¸
MOTS PNG	âï¸	âï¸
LabelMe 3.0	âï¸	âï¸
ImageNet	âï¸	âï¸
CamVid	âï¸	âï¸
WIDER Face	âï¸	âï¸
VGGFace2	âï¸	âï¸
Market-1501	âï¸	âï¸
ICDAR13/15	âï¸	âï¸
Open Images V6	âï¸	âï¸
Cityscapes	âï¸	âï¸
KITTI	âï¸	âï¸
Kitti Raw Format	âï¸	âï¸
LFW	âï¸	âï¸
Supervisely Point Cloud Format	âï¸	âï¸
YOLOv8 Detection	âï¸	âï¸
YOLOv8 Oriented Bounding Boxes	âï¸	âï¸
YOLOv8 Segmentation	âï¸	âï¸
YOLOv8 Pose	âï¸	âï¸

Deep learning serverless functions for automatic labeling

CVAT supports automatic labeling. 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 run on:

Name	Type	Framework	CPU	GPU
Segment Anything	interactor	PyTorch	âï¸	âï¸
Deep Extreme Cut	interactor	OpenVINO	âï¸
Faster RCNN	detector	OpenVINO	âï¸
Mask RCNN	detector	OpenVINO	âï¸
YOLO v3	detector	OpenVINO	âï¸
YOLO v7	detector	ONNX	âï¸	âï¸
Object reidentification	reid	OpenVINO	âï¸
Semantic segmentation for ADAS	detector	OpenVINO	âï¸
Text detection v4	detector	OpenVINO	âï¸
SiamMask	tracker	PyTorch	âï¸	âï¸
TransT	tracker	PyTorch	âï¸	âï¸
f-BRS	interactor	PyTorch	âï¸
HRNet	interactor	PyTorch		âï¸
Inside-Outside Guidance	interactor	PyTorch	âï¸
Faster RCNN	detector	TensorFlow	âï¸	âï¸
Mask RCNN	detector	TensorFlow	âï¸	âï¸
RetinaNet	detector	PyTorch	âï¸	âï¸
Face Detection	detector	OpenVINO	âï¸

License

The code is released under the MIT License.

The code contained within the /serverless directory is released under the MIT License. However, it may download and utilize various assets, such as source code, architectures, and weights, among others. These assets may be distributed under different licenses, including non-commercial licenses. It is your responsibility to ensure compliance with the terms of these licenses before using the assets.

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.

Contact us

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 ask questions and get our support.

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

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of CVAT

Cons of CVAT

Code Comparison

Pros of labelme

Cons of labelme

Code Comparison

Pros of labelImg

Cons of labelImg

Code Comparison

Pros of VoTT

Cons of VoTT

Code Comparison

Convert designs to code with AI

README

Computer Vision Annotation Tool (CVAT)

Quick start â¡

Partners â¤ï¸

Public datasets

CVAT online: cvat.ai

Prebuilt Docker images ð³

Screencasts ð¦

API

SDK

CLI

Supported annotation formats

Deep learning serverless functions for automatic labeling

License

Contact us

Links

Top Related Projects

Convert designs to code with AI

Quick start â¡

Partners â¤ï¸

Prebuilt Docker images ð³

Screencasts ð¦