In the rapidly evolving field of computer vision, datasets used for model training can contain thousands or even millions of images, making manual data labeling a major bottleneck due to its time-consuming nature and high price. To address this challenge, automating annotation tasks through automated data labeling has become crucial, as it significantly improves efficiency without increasing costs.
This article will help you identify automated data labeling techniques that are explicitly tailored to your project's specific needs and your team, or just you if you are annotating solo.
And remember, properly implemented automation methods can drastically accelerate annotation tasks, consistently delivering high-quality labels efficiently and economically.
CVAT provides several robust auto-annotation methods designed to streamline and enhance your data labeling workflows:
- Nuclio Functions: Made for real-time automated labeling without external dependencies. The serverless annotation models run within your self-hosted CVAT infrastructure and provide customizable, on-premises automation that integrates seamlessly into your existing machine learning workflows.
- External Service Integration (Hugging Face and Roboflow): CVAT supports importing models directly from cloud-based annotation platforms, including Hugging Face and Roboflow. This enables straightforward access to powerful, pre-trained models, expanding your automated data labeling capabilities with minimal setup.
- CLI Annotation: Execute annotation tasks locally through command-line interfaces (CLI). This method supports efficient batch processing and automation scripts for high-volume visual data labeling projects, providing you with complete control and flexibility.
- AI Agents: Acts as a seamless integration bridge between your AI models and the CVAT platform. By selecting a suitable model, you can quickly establish a direct connection, leveraging your custom-trained models for precise automated labeling in real-time.
Let's dive into details.
Nuclio (CVAT Community and Enterprise)
Nuclio is an integrated serverless function framework that enables you to run deep learning (DL) models within your CVAT environment. It’s beneficial when your use case involves objects or categories that generic, pre-trained models can’t recognize. For example, rare defects in industrial components or specialized instruments used in lab research. Public models may not be trained on these specifics, and in cases like this, custom-trained deep learning (DL) models become necessary.
In CVAT, custom DL models can be connected as serverless functions through Nuclio. Once deployed, they can automatically generate annotations, such as bounding boxes, masks, or tracks. However, the quality of these auto-generated labels depends on the model. For niche or complex tasks, the predictions often need refinement. Still, if the model can handle even 50% of the workload, it significantly reduces the time spent on manual annotation.
How Data Labeling with Nuclio Works
Nuclio functions are serverless annotation models that operate directly within your CVAT infrastructure.
Annotation models (such as YOLOv11 or SAM2) are encapsulated as Nuclio functions via specific metadata and associated implementation code. After deployment, models become accessible through CVAT’s internal model registry for immediate auto-labeling use.
Supported Annotation Models and Data Formats with Nuclio
Nuclio functions are set up by administrators using Docker Compose (docker-compose.serverless.yml) and configured with a metadata file (function.yml) that defines the model behavior and expected labels. Once deployed, models are immediately accessible through CVAT’s internal model registry, enabling quick and seamless auto-labeling tasks.
Nuclio functions support various annotation models, including object detection using bounding boxes, masks, and polygons, frame-by-frame tracking, re-identification, and interactive mask generation. You will find one that fits your machine learning needs.
Automated Annotation with Nuclio: Pros and Cons
Nuclio functions offer several advantages, including support for diverse and advanced annotation types, direct integration with CVAT for seamless operation, and suitability for customized and complex workflows.
The main drawbacks are that setting up and managing Nuclio functions require administrative access and technical expertise, which can be challenging for users without specialized knowledge. Additionally, these functions are only available in CVAT On-Prem installations, meaning they cannot be used in cloud-based or managed versions of CVAT.
When to Use Nuclio for Automated Data Labeling
In practice, Nuclio is a strong choice for teams working in tightly controlled environments or tackling highly specialized tasks. For instance, an automotive supplier might use it to detect microcracks on engine parts during quality control, a task too specific for public models to handle reliably.
That said, Nuclio isn't limited to niche use cases. You can use it to deploy any deep learning model your team needs. Nuclio's flexibility makes it a good fit solution not just for rare or complex tasks, but also for common, high-volume annotation workflows where having control over the model and infrastructure matters.
Integration with RoboFlow & Hugging Face (CVAT Online & Enterprise)
For teams that don’t have the infrastructure to host their models or want to move quickly, CVAT supports integration with external AI model platforms, such as RoboFlow and Hugging Face. These platforms host a variety of pre-trained models and also let you upload your own.
How Data Labelling with Hugging Face and Roboflow Works
Setting up a third-party model in CVAT is straightforward. You navigate to the Models page, paste in the model’s URL and access token, and the model becomes available for use. No administrative privileges are required, and any team member can start using it immediately. This makes it ideal for collaborative environments. For guidance, users can refer to tutorials and demonstration videos that show how to add models and use them for annotation tasks.
Supported Annotation Models and Data Formats with Hugging Face and Roboflow
The third-party integrations support a wide range of pretrained public models and custom-trained models hosted on Hugging Face and Roboflow. While the exact model types depend on the external service, typical support includes object detection and classification tasks, making it suitable for many standard annotation workflows.
Automated Annotation with Hugging Face and Roboflow: Pros and Cons
Third-party model integrations are easy to configure and use, don’t require any on-premise deployment or server setup, and give users access to an extensive library of powerful models. The convenience of using pretrained or custom models directly from platforms like Hugging Face and Roboflow significantly speeds up the annotation process.
There are some trade-offs. Performance can be slower since data is sent frame by frame to remote servers for processing, and overall availability depends on the uptime and responsiveness of the external platform's API.
When to Use Hugging Face or Roboflow for Automated Data Labeling
This method is an excellent fit for startups, distributed teams, or individual users who need to annotate large volumes of common, well-understood data without setting up their infrastructure.
For example, a logistics company could quickly deploy a RoboFlow model to detect package types across thousands of warehouse images, or an agricultural monitoring team might use a Hugging Face model to classify crop health in drone imagery, using a pretrained model from an external provider.
Auto-Annotation with CVAT CLI (CVAT Community)
CVAT’s Command Line Interface (CLI), powered by its Python SDK, lets you run annotations entirely on your local machine without the need to deploy models on the server or connect to external services. You define custom annotation logic in a Python script, specifying which labels the model should detect and how it should process the input data.
Once the function is ready, you can run a CLI command to apply the model to a task.
How Data Labeling with CVAT CLI Works
You begin by writing a simple script tailored to your model and task. Then, using the CVAT CLI, you run the script locally to annotate your dataset. This method is well-documented, with step-by-step tutorials available to guide users through the process.
Supported Annotation Models and Data Formats with CVAT CLI
Using the CLI, you can automatically generate annotations for complete tasks in several standard formats, including object detection, pose estimation, and oriented bounding boxes, based on your local model’s capabilities.
With the CLI approach, you can utilize any model to meet your annotation needs.
Automated Annotation with CVAT CLI: Pros and Cons
This CLI-based method requires no server configuration and is ideal for solo users who want to experiment with models locally. Since everything runs on your machine, it offers maximum data privacy.
Another key advantage is cost: because there are no external API calls or cloud services involved, there are no additional usage fees. You only pay for your hardware and resources, making it a highly economical option for small-scale or exploratory projects.
However, you need sufficient machine resources to run the model, and annotation is done entirely via scripts; there is no graphical interface. Only whole-task annotation (not frame-by-frame) is supported, and the current implementation is limited to detection-type models, such as bounding boxes, pose, and oriented bounding boxes.
When to Use CVAT CLI for Automated Data Labeling
Available for everyone. For example, this method can be utilized by AI research teams conducting experiments with various object detection models, or by institutions such as hospitals and banks that handle sensitive data and must comply with stringent privacy regulations.
AI Agent-Based Functions: Scalable and Shareable Annotation (CVAT Online & Enterprise)
AI Agents are CVAT’s newest auto-annotation method, designed to connect your custom AI models with CVAT through a specifically designed service that works as a bridge between the model and CVAT.
Unlike Nuclio, agents don’t require server-side deployment; instead, they operate independently and communicate with the platform to handle annotation tasks.
How Data Labeling with AI Agents Works
To set up an AI agent, you first create a native Python function that wraps your model’s inference logic using the CVAT SDK. You then register this function with CVAT using the CLI—only metadata, such as function names and label definitions, are required. Only they are uploaded, not the model itself.
After registration, you launch a local or cloud-based AI agent that "listens" for incoming annotation tasks. When a task is queued, the agent retrieves the relevant data, runs inference using your function, and sends the results back to CVAT for review. You can also scale your operations by running multiple agents simultaneously, enabling distributed processing across machines or teams.
Supported Annotation Models and Data Formats with CVAT AI Agents
At launch, AI agents support only detection-based annotation types, including object detection with bounding boxes and oriented boxes. While interactive features and support for more complex tasks are still in progress, the current capabilities make them suitable for a wide range of automated data labeling workflows.
Automated Annotations with CVAT AI Agents: Pros and Cons
One of the key advantages of AI agents is their ease of deployment, as no server integration or administrator access is required, which reduces friction for both individuals and teams. The deployment is flexible, working equally well on local machines or in the cloud.
Agents can be shared and reused across different teams or organizations, helping streamline operations. They also build on CVAT’s existing CLI functions, reducing the need for additional setup and accelerating the onboarding process. From a cost perspective, this method avoids external API fees and scales effectively with your hardware, giving you control over both performance and expenses.
However, AI agents are still under active development, meaning some features—such as interactive annotations—are not yet available. Current functionality is limited to detection tasks, and users need a basic understanding of the command-line interface to set up and operate agents effectively. Despite these limitations, the flexibility and extensibility of this method make it a compelling option for teams building custom automation workflows.
When to Use CVAT AI Agents for Automated Data Labeling
AI agents are ideal for anyone who needs both flexibility and scalability. For example, a company building autonomous vehicles might run multiple agents to annotate thousands of driving scene images in parallel. Or a retail chain could deploy an internal model as an agent and share it with staff across different locations to ensure consistent product labeling.
Let's Compare the Automated Data Labeling Approaches
Choosing the proper auto-labeling method in CVAT depends on what you’re working with—your team size, available tools, privacy needs, and the type of annotations you need.
If you want something quick and easy, third-party models from Hugging Face or Roboflow are straightforward to integrate and use, but they rely on external services and may be slower. For complete control and flexibility, Nuclio functions or AI agents enable you to run your models inside CVAT; however, they require some setup and technical knowledge. If you’re working solo or want to keep everything local, CVAT’s CLI-based annotation is lightweight, private, and cost-free—but it's best suited for simpler tasks and lacks a UI.
Hybrid approaches are great if you want to mix speed with accuracy. They use automation for the easy parts and let humans handle the tricky bits—ideal when your dataset has both repetitive patterns and edge cases.
The table below breaks down the primary methods, allowing you to quickly find what fits your workflow.
Conclusion
The best auto-annotation method in CVAT depends on your specific needs, whether it's local control, ease of setup, support for complex annotations, or the ability to scale up at no additional cost.
- Use Nuclio for advanced workflows and full model customization in self-hosted environments.
- Choose third-party integrations like Hugging Face or Roboflow for quick access to pretrained models with minimal setup.
- Use CVAT CLI for lightweight, local automation without server dependencies, and when you need to use any model for your machine learning needs.
- Deploy AI agents to scale annotations flexibly across teams using your models.
Each option is built to fit different teams, infrastructures, and project sizes.
Start now: Log in or sign up at CVAT Online, or contact us to explore CVAT Enterprise for full-featured, scalable deployments.
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