Updated May 23, 2024

CVAT (Computer Vision Annotation Tool) is an open-source, Kubernetes-native application designed to streamline the process of annotating images and videos for computer vision tasks. With CVAT, teams can efficiently label datasets, enabling the training of accurate and robust machine learning models. Deploying CVAT in your Kubernetes cluster provides a scalable and flexible solution for managing annotation workflows, enhancing collaboration among team members, and accelerating model development cycles. CVAT supports a wide range of annotation types, including bounding boxes, polygons, polylines, and keypoints, catering to diverse computer vision applications. By leveraging CVAT, organizations can unlock the full potential of their computer vision projects, from data preparation to model deployment.

You can deploy CVAT in your Nebius AI Managed Service for Kubernetes clusters using this Marketplace product.

Deployment instructions

Before installing this product:

  1. Create a Kubernetes cluster and a node group with GPUs in it. If you have created a GPU cluster, select it when creating the node group.

  2. Install kubectl and configure it to work with the created cluster.

To install the product:

  1. On the cluster page in the management console, go to the Marketplace tab, select the product, and click Install.

  2. Configure the application:

    • Namespace: Select a namespace or create a new one.
    • Application name: Enter an application name.
    • Enable Grafana: Select the option to monitor collected metrics in Grafana.
    • Hostname: Choose a hostname for connecting to CVAT after the installation.
    • Persistent volume size in GB: Enter the size of the default images and datasets storage.
  3. Click Install.

  4. Wait for the application to change its status to Deployed.

To connect to the CVAT UI:

  1. Find out the CVAT server pod’s name:

    kubectl get pods -n <namespace> -l app=cvat-app -l component=server
    
  2. Connect to the CVAT pod:

    kubectl -n <namespace> exec -it <CVAT-pod-name> -- /bin/sh
    
  3. Create a privileged user:

    python manage.py createsuperuser
    
  4. Choose a name, email (optionally), and a password for the new user.

  5. Enable port forwarding:

    kubectl port-forward service/<namespace>-traefik 8000:80 -n <namespace>
    

    For example, kubectl port-forward service/cvat-traefik 8000:80 -n cvat.

  6. Add 127.0.0.1 <hostname> line to the /etc/hosts file.

To check that CVAT is working:

  1. Go to http://<hostname>:8000 in your web browser to access the CVAT UI.

  2. Log in with the credentials of the previously created user.

To store images and datasets in a Nebius AI Object Storage bucket:

  1. Create an Object Storage bucket.

  2. Configure access to the bucket:

    1. Create a service account and add it to the editors group.

    2. Create a static access key for the service account.

  3. Connect the bucket with CVAT:

    1. On the CVAT main page, open Cloud Storages tab in the top panel.

    2. On the right, click + to add a new cloud storage integration.

    3. Configure the storage:

      1. Choose a storage name and description.
      2. For the Provider, select AWS.
      3. For the Authentication type, select Key id and secret access key pair.
      4. Enter a name of the previously created Object Storage bucket and its access keys.
      5. For the Endpoint URL, enter https://storage.ai.nebius.cloud.
    4. Click Submit.

    5. Open the advanced project settings in CVAT UI and enter the previously created storage name in the Select cloud storage field.

Billing type
Free
Type
Kubernetes® Application
Category
Dataset preparation
Publisher
Nebius
Use cases
  • Annotation of objects within images and videos for tasks such as object detection, instance segmentation, and object tracking.

  • Labelling images with corresponding categories or tags to train classification models for tasks like image recognition and content-based retrieval.

  • Defining pixel-level annotations to create detailed masks for each object class, essential for applications such as scene understanding and image segmentation.

  • Labelling video frames to generate annotated datasets for video understanding tasks, including action recognition, event detection, and surveillance.

Technical support

Nebius AI does not provide technical support for the product. If you have any issues, please refer to the developer’s information resources.

Product composition
Helm chartVersion
Pull-command
Documentation
cr.nemax.nebius.cloud/yc-marketplace/nebius/cvat/chart/cvat0.12.0Open
Docker imageVersion
Pull-command
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Terms
By using this product you agree to the Nebius AI Marketplace Terms of Service and the terms and conditions of the following software: MIT License
Billing type
Free
Type
Kubernetes® Application
Category
Dataset preparation
Publisher
Nebius