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8 min read Oct 02, 2024
Label-studio-ml-backend Add Model File

Label Studio ML Backend: Adding Custom Models

Label Studio is a powerful open-source platform for data labeling and annotation, allowing you to build and train machine learning models efficiently. The Label Studio ML Backend empowers you to integrate custom machine learning models directly into your labeling workflow, streamlining the process and significantly boosting your data annotation efficiency.

One crucial aspect of using Label Studio ML Backend is understanding how to add custom models to the system. This process involves uploading your pre-trained model files and configuring Label Studio to recognize and utilize them for predictions during the annotation process.

Why Use Custom Models in Label Studio?

Leveraging custom models in Label Studio offers several advantages:

  • Enhanced Accuracy: Using models trained on specific datasets relevant to your project can significantly improve the accuracy and speed of labeling.
  • Reduced Labeling Time: By pre-labeling data with predictions from your custom models, you can drastically cut down on the time required for human annotators to complete the labeling process.
  • Improved Efficiency: Pre-labeling and model-assisted labeling can significantly boost the efficiency of your labeling workflow, enabling you to annotate larger datasets faster.
  • Increased Flexibility: Label Studio ML Backend supports a wide range of model formats and frameworks, offering flexibility and freedom to integrate your preferred models.

Adding Your Custom Models to Label Studio ML Backend

Here's a step-by-step guide on how to add your custom models to Label Studio ML Backend:

  1. Preparing Your Model:

    • Choose Your Model: Select the appropriate model type based on your task and dataset (e.g., object detection, image classification, text classification).
    • Train Your Model: Train your model on a dataset relevant to your labeling project.
    • Save Your Model: Export your trained model in a format compatible with Label Studio ML Backend (e.g., ONNX, TensorFlow, PyTorch).

  2. Configuring Label Studio ML Backend:

    • Install Required Dependencies: Ensure you have the necessary dependencies installed based on your model format.
    • Set up Your Environment: Configure your environment variables (e.g., model path, API key) to access the model file and Label Studio ML Backend.
    • Create a Model Configuration File: Create a JSON configuration file that specifies the model's name, type, path, and any additional parameters needed for inference.

  3. Uploading Your Model to Label Studio ML Backend:

    • Create a New Model: Use the Label Studio ML Backend API or web interface to create a new model entry.
    • Upload Your Model File: Upload the pre-trained model file (e.g., .onnx, .pb, .pt) to the Label Studio ML Backend storage.
    • Link to Configuration File: Associate your model file with the configuration file you created earlier.

  4. Testing and Integrating:

    • Verify Model Functionality: Test the model's predictions on a sample dataset to ensure it's functioning correctly.
    • Configure Model Usage: Configure your labeling tasks in Label Studio to utilize the newly added model for pre-labeling or prediction assistance.

Example: Adding a Custom Object Detection Model

Let's say you have an object detection model trained to recognize different types of fruits in images. Here's a simplified example of adding this model to Label Studio ML Backend:

1. Prepare Your Model:

  • Model: Trained YOLOv5 model for fruit detection
  • Training Data: Dataset of images with annotated fruit instances
  • Save: Export the model in ONNX format (model.onnx)

2. Configure Label Studio ML Backend:

  • Install: Install the ONNX runtime library
  • Environment: Define environment variables:
    • MODEL_PATH: Path to your model file (e.g., "/path/to/model.onnx")
    • MODEL_NAME: "FruitDetector"
  • Configuration File: Create a JSON file (config.json) with:
    {
  "name": "FruitDetector",
  "type": "object_detection",
  "model_path": "/path/to/model.onnx",
  "framework": "onnx",
  "input_format": "image",
  "output_format": "bbox"
}

3. Upload Your Model:

  • Create Model Entry: Use the Label Studio ML Backend API or web interface to create a new model entry.
  • Upload: Upload model.onnx
  • Link: Associate model.onnx with the config.json file.

4. Testing and Integration:

  • Verify: Test the model's predictions on a few sample images.
  • Configure: Configure your labeling tasks in Label Studio to use "FruitDetector" for pre-labeling or prediction assistance.

Key Considerations:

  • Model Format Compatibility: Ensure your model format is supported by Label Studio ML Backend. Refer to the official documentation for supported frameworks and formats.
  • Model Performance: Evaluate the accuracy of your model on a holdout dataset to ensure its performance meets your project's requirements.
  • Resource Management: Be mindful of the computational resources required for your model's inference. Adjust model complexity or consider using hardware acceleration for larger models.

Conclusion

Integrating custom models into Label Studio ML Backend unlocks a significant boost in labeling efficiency, accuracy, and flexibility. By following these steps, you can easily add your pre-trained models and leverage their power to streamline your data annotation workflows.

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