Skip to main content
This guide will walk you through creating your first annotation project in CVAT, from initial setup to exporting your labeled data.

​
Choose your deployment

Before you start, decide how you want to use CVAT:

​
Create your first project

Projects help organize related annotation tasks and define shared labels.
1

Navigate to Projects

From the CVAT dashboard, click the Projects tab in the top navigation.
2

Create new project

Click the + Create Project button.Fill in the project details:
  • Name: Choose a descriptive name (e.g., “Car Detection Dataset”)
  • Labels: Define the object classes you want to annotate
[
  {"name": "car", "color": "#ff0000"},
  {"name": "person", "color": "#00ff00"},
  {"name": "bicycle", "color": "#0000ff"}
]
3

Configure advanced settings (optional)

  • Set a bug tracker URL for issue reporting
  • Configure source and target storage (cloud storage)
  • Assign project owner and team members
4

Save the project

Click Submit to create your project.
You can also create tasks without a project, but projects make it easier to manage multiple related tasks with consistent labels.

​
Create and upload data to a task

Tasks contain the actual data (images or videos) to be annotated.
1

Create a new task

From your project page or the Tasks tab, click + Create Task.Configure the task:
  • Name: Descriptive task name (e.g., “Street scenes - batch 1”)
  • Project: Select the project you created (optional)
  • Labels: If not using a project, define labels here
2

Upload your data

Choose your data source:
  • Click Select files and choose images or videos from your computer
  • Supports: JPG, PNG, BMP, MP4, AVI, MOV, and more
  • You can select multiple files at once
3

Configure advanced options (optional)

  • Chunk size: Frames per segment (for large videos)
  • Overlap: Frame overlap between jobs for consistency
  • Segment size: Frames per job for distributing work
  • Image quality: Compression level (1-100)
  • Frame filter: Skip frames (e.g., step=10 for every 10th frame)
4

Submit and wait for processing

Click Submit. CVAT will:
  1. Upload your files
  2. Process and compress images/videos
  3. Create jobs for annotation
Processing time depends on data size. Large videos may take several minutes.

​
Annotate your data

Now you’re ready to start labeling!
1

Open a job

From the task page, click on a job to open the annotation interface.The interface has three main areas:
  • Canvas: The image/video display area
  • Objects sidebar: List of annotations
  • Controls toolbar: Drawing and navigation tools
2

Create annotations

Select a shape type from the left toolbar:
  1. Click the Rectangle tool
  2. Select a label
  3. Click and drag on the canvas to draw a box
  4. Release to complete
Shortcut: N key
3

Edit and refine annotations

  • Move: Click and drag annotations
  • Resize: Drag corner/edge handles
  • Delete: Select annotation and press Delete
  • Copy: Ctrl+C, paste with Ctrl+V
  • Attributes: Set in the right sidebar after selecting an annotation
4

Track objects in videos (interpolation)

For video annotation, use Track mode:
  1. Switch to Track mode in the toolbar
  2. Create a shape on the first frame
  3. Move to another frame
  4. Adjust the shape position
  5. CVAT automatically interpolates between keyframes
Use automatic tracking with AI models to follow objects across frames automatically.
5

Save your work

Click Save (or Ctrl+S) to save annotations.
Save frequently to avoid losing work. Auto-save is enabled by default but manual saves are recommended.

​
Export annotations

Once you’ve completed annotation, export your dataset in the format you need.
1

Open export menu

From the task page, click Actions → Export task dataset.
2

Choose export format

Select from 40+ supported formats:
COCO 1.0
Popular format for object detection, segmentation, and keypoint detection.
3

Download the dataset

Click OK and wait for processing. A download link will appear when ready.The export includes:
  • Images or video frames
  • Annotation files in chosen format
  • Label mapping and metadata

​
Automate with CLI or SDK

For larger workflows, use the CVAT Python SDK or CLI: 
from cvat_sdk import Client

# Connect to CVAT
client = Client(url='https://app.cvat.ai')
client.login(('username', 'password'))

# Create a task
task = client.tasks.create_from_data(
    spec={
        'name': 'My Task',
        'labels': [
            {'name': 'car'},
            {'name': 'person'}
        ]
    },
    resource_type='local',
    resources=['image1.jpg', 'image2.jpg']
)

print(f"Created task ID: {task.id}")

# Export annotations
task.export_dataset('YOLO 1.1', 'dataset.zip')
Install with:
pip install cvat-sdk

​
Next steps

You’ve completed your first annotation workflow! Here’s what to explore next:

Key concepts

Learn about projects, tasks, jobs, labels, and CVAT’s architecture

Annotation guide

Explore advanced annotation features and tools

Quality control

Set up quality control workflows for your team

Dataset formats

Learn about 30+ supported annotation formats

API reference

Explore the full API, SDK, and CLI documentation
Join our Discord community to ask questions, share tips, and connect with other CVAT users!

Build docs developers (and LLMs) love

Get started for freeTalk to us