Label-Studio-Ml-Backend

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Label Studio ML backend is an SDK that wraps machine learning code into a web server, automating labeling tasks within Label Studio. It supports various models for pre-annotation, interactive labeling, and training.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is label-studio-ml-backend?

The Label Studio ML backend is an SDK designed to transform your machine learning code into a web server. This server seamlessly integrates with a running Label Studio instance, enabling the automation of diverse labeling tasks. It supports a wide array of models, including text classification with Huggingface and scikit-learn, object detection with YOLO and Grounding DINO, NER with Flair and SpaCy, and OCR with EasyOCR and PaddleOCR. Developers can implement custom prediction and training logic, leveraging helper methods for data storage and retrieval. The SDK provides examples for quick setup and deployment options to platforms like GCP, making it a versatile tool for integrating ML into data annotation workflows.

Best used for

Ideal for developers and data scientists who need to integrate machine learning models directly into their data annotation workflows, automate pre-annotation, and enable interactive labeling. Especially valuable for those building custom ML backends for specific project requirements and deploying them efficiently.

Common actions

integrate machine learning

automate data labeling

deploy ML models

develop custom backends

workflowsautomated workflowdeepfakeopen-sourcecollaborationlow-code/no-codegithub copilotface swapping"AI Agents"

Capabilities

Key features

Wrap ML code
Automate labeling tasks
Pre-annotation support
Interactive labeling
Model training
Custom prediction logic
GCP deployment

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of machine learning models can be integrated with Label Studio ML backend?

The Label Studio ML backend supports a wide range of models, including text classification (Huggingface, scikit-learn), object detection (YOLO, Grounding DINO), Named Entity Recognition (Flair, SpaCy, Hugging Face NER), Optical Character Recognition (EasyOCR, PaddleOCR, Tesseract), and Large Language Models (Hugging Face LLM, OpenAI, Azure LLMs).

Can I use the ML backend for both pre-annotation and interactive labeling?

Yes, the ML backend supports both pre-annotation, where models provide initial annotations for tasks, and interactive labeling, where models offer real-time predictions as users perform actions on the labeling page. Some models also support training based on submitted annotations.

How can I deploy a custom ML backend developed with this SDK?

You can deploy your custom ML backend using Docker with `docker-compose` for local development. For cloud deployment, the SDK provides specific commands to deploy to Google Cloud Platform (GCP), requiring gcloud installation and API activation.

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Open Source & Models Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Prompt Engineering

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Also listed in

This tool also appears in

Data & Analytics › Data Labeling & Annotation

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