> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/cvat-ai/cvat/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart guide

> Get started with CVAT in minutes - create your first project, annotate data, and export results

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:

<Tabs>
  <Tab title="CVAT Cloud (recommended)">
    The fastest way to get started - no installation required.

    <Steps>
      <Step title="Sign up for CVAT Cloud">
        1. Visit [app.cvat.ai](https://app.cvat.ai)
        2. Click "Sign Up" and create your account
        3. Verify your email address
        4. Log in to start annotating

        <Info>
          The free tier includes up to 10 tasks and 500MB of data storage.
        </Info>
      </Step>

      <Step title="Access the platform">
        Once logged in, you'll see the CVAT dashboard where you can create projects and tasks.
      </Step>
    </Steps>
  </Tab>

  <Tab title="Self-hosted (Docker)">
    Install CVAT locally for complete control over your data.

    <Steps>
      <Step title="Prerequisites">
        Ensure you have:

        * Docker Engine (19.03+)
        * Docker Compose (1.29+)
        * 4GB+ RAM available
        * Linux, macOS, or Windows with WSL2
      </Step>

      <Step title="Clone and start CVAT">
        ```bash theme={null}
        git clone https://github.com/cvat-ai/cvat
        cd cvat
        docker-compose up -d
        ```

        Wait for all containers to start (this may take several minutes on first run).
      </Step>

      <Step title="Create superuser account">
        ```bash theme={null}
        docker exec -it cvat_server bash -ic 'python3 manage.py createsuperuser'
        ```

        Follow the prompts to set up your admin account.
      </Step>

      <Step title="Access CVAT">
        Open your browser and navigate to `http://localhost:8080`
      </Step>
    </Steps>
  </Tab>
</Tabs>

## Create your first project

Projects help organize related annotation tasks and define shared labels.

<Steps>
  <Step title="Navigate to Projects">
    From the CVAT dashboard, click the **Projects** tab in the top navigation.
  </Step>

  <Step title="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

    <CodeGroup>
      ```json Example: Object detection labels theme={null}
      [
        {"name": "car", "color": "#ff0000"},
        {"name": "person", "color": "#00ff00"},
        {"name": "bicycle", "color": "#0000ff"}
      ]
      ```

      ```json Example: Semantic segmentation labels theme={null}
      [
        {"name": "road", "color": "#808080"},
        {"name": "sidewalk", "color": "#f0e68c"},
        {"name": "building", "color": "#a52a2a"},
        {"name": "vegetation", "color": "#228b22"}
      ]
      ```
    </CodeGroup>
  </Step>

  <Step title="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
  </Step>

  <Step title="Save the project">
    Click **Submit** to create your project.
  </Step>
</Steps>

<Note>
  You can also create tasks without a project, but projects make it easier to manage multiple related tasks with consistent labels.
</Note>

## Create and upload data to a task

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

<Steps>
  <Step title="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
  </Step>

  <Step title="Upload your data">
    Choose your data source:

    <Tabs>
      <Tab title="Local files">
        * 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
      </Tab>

      <Tab title="Remote sources">
        Add URLs to images or videos:

        ```
        https://example.com/image1.jpg
        https://example.com/image2.jpg
        ```
      </Tab>

      <Tab title="Cloud storage">
        * Configure cloud storage connection (AWS S3, Azure, GCS)
        * Select files from your connected storage
      </Tab>

      <Tab title="Share">
        * Place files in the mounted share directory
        * Specify paths relative to the share root
      </Tab>
    </Tabs>
  </Step>

  <Step title="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)
  </Step>

  <Step title="Submit and wait for processing">
    Click **Submit**. CVAT will:

    1. Upload your files
    2. Process and compress images/videos
    3. Create jobs for annotation

    <Info>
      Processing time depends on data size. Large videos may take several minutes.
    </Info>
  </Step>
</Steps>

## Annotate your data

Now you're ready to start labeling!

<Steps>
  <Step title="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
  </Step>

  <Step title="Create annotations">
    Select a shape type from the left toolbar:

    <Tabs>
      <Tab title="Rectangle (bounding box)">
        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
      </Tab>

      <Tab title="Polygon">
        1. Click the **Polygon** tool
        2. Select a label
        3. Click points around the object perimeter
        4. Double-click or press `N` to close the polygon

        **Shortcut**: `Shift+N`
      </Tab>

      <Tab title="Points">
        1. Click the **Points** tool
        2. Select a label
        3. Click to place individual points
        4. Press `N` when done
      </Tab>

      <Tab title="Polyline">
        1. Click the **Polyline** tool
        2. Select a label
        3. Click points along the line
        4. Double-click to finish
      </Tab>
    </Tabs>
  </Step>

  <Step title="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
  </Step>

  <Step title="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

    <Tip>
      Use automatic tracking with AI models to follow objects across frames automatically.
    </Tip>
  </Step>

  <Step title="Save your work">
    Click **Save** (or `Ctrl+S`) to save annotations.

    <Warning>
      Save frequently to avoid losing work. Auto-save is enabled by default but manual saves are recommended.
    </Warning>
  </Step>
</Steps>

## Export annotations

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

<Steps>
  <Step title="Open export menu">
    From the task page, click **Actions** → **Export task dataset**.
  </Step>

  <Step title="Choose export format">
    Select from 40+ supported formats:

    <Tabs>
      <Tab title="COCO">
        ```
        COCO 1.0
        ```

        Popular format for object detection, segmentation, and keypoint detection.
      </Tab>

      <Tab title="YOLO">
        ```
        YOLO 1.1
        YOLOv8 Detection 1.0
        YOLOv8 Segmentation 1.0
        ```

        For training YOLO models.
      </Tab>

      <Tab title="Pascal VOC">
        ```
        Pascal VOC 1.1
        ```

        Classic format with XML annotations.
      </Tab>

      <Tab title="CVAT for images">
        ```
        CVAT for images 1.1
        ```

        CVAT's native format, best for re-importing.
      </Tab>
    </Tabs>
  </Step>

  <Step title="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
  </Step>
</Steps>

## Automate with CLI or SDK

For larger workflows, use the CVAT Python SDK or CLI:

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    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:

    ```bash theme={null}
    pip install cvat-sdk
    ```
  </Tab>

  <Tab title="CLI">
    ```bash theme={null}
    # Create a task
    cvat-cli --auth user:password task create \
      --labels '[{"name": "car"}, {"name": "person"}]' \
      "My Task" \
      local \
      image1.jpg image2.jpg

    # List tasks
    cvat-cli --auth user:password task ls

    # Export dataset
    cvat-cli --auth user:password task export-dataset \
      --format "YOLO 1.1" \
      123 \
      output.zip
    ```

    Install with:

    ```bash theme={null}
    pip install cvat-cli
    ```
  </Tab>
</Tabs>

## Next steps

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

<CardGroup cols={2}>
  <Card title="Key concepts" icon="graduation-cap" href="/key-concepts">
    Learn about projects, tasks, jobs, labels, and CVAT's architecture
  </Card>

  <Card title="Annotation guide" icon="pen" href="/annotation/editor-overview">
    Explore advanced annotation features and tools
  </Card>

  <Card title="Quality control" icon="shield-check" href="/projects/quality-control">
    Set up quality control workflows for your team
  </Card>

  <Card title="Dataset formats" icon="database" href="/datasets/supported-formats">
    Learn about 30+ supported annotation formats
  </Card>

  <Card title="API reference" icon="code" href="/api/overview">
    Explore the full API, SDK, and CLI documentation
  </Card>
</CardGroup>

<Tip>
  Join our Discord community to ask questions, share tips, and connect with other CVAT users!
</Tip>