Label Studio

Label Studio is an open-source platform used for labeling data across different models.

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What is Label Studio?

Label Studio is a freely available data labeling tool created to prepare training data for computer vision, natural language processing, speech, voice, and video models with flexibility in labeling various types of data.

How to use Label Studio?

To start using Label Studio, follow these easy steps: 1. Get the Label Studio package by installing it with pip, brew, or by copying it from the GitHub repository. 2. Open Label Studio using the package you installed or Docker. 3. Bring your data into Label Studio. 4. Pick the type of data you have (like images, audio, text, time series, multi-domain, or video) and choose what labeling task you want to do (like classifying images, finding objects, or writing down audio). 5. Begin labeling your data with tags and templates that you can change. 6. Link your ML/AI pipeline and use webhooks, the Python SDK, or API to log in, manage projects, and make model predictions. 7. Look at and manage your dataset in the Data Manager with filters that can do more. 8. Support many projects, uses, and users on the Label Studio platform.

Features

Adaptable data annotation for various data formats

Use Cases

Preparing Training Data for Computer Vision Models
Preparing Training Data for AI-Powered Language Models
Preparing high-quality data for training speech and voice models.
Preparing Training Data for Video Models
Classification of pictures, sounds, written content, and sequential data
Identifying objects and monitoring their movement within images and videos.
Classification of images into distinct parts
Audio Speaker Identification and Emotion Analysis
Audio Transcription
Classification of documents and extraction of named entities
Answering questions and analyzing emotions
Analyzing Time Series Data and Identifying Events
Dialogue Processing and Optical Character Recognition
Applications spanning multiple domains that need different kinds of data labeling.

Frequently Asked Questions

Label Studio is an open-source platform that helps prepare training data for various AI models, including computer vision, natural language processing, speech, voice, and video. It provides flexibility for labeling different types of data.

To get started with Label Studio, follow these simple steps: 1. Set up Label Studio by installing the package using pip, brew, or by cloning the repository from GitHub. 2. Run Label Studio using the installed package or Docker. 3. Bring your data into Label Studio. 4. Pick the data type you're working with, such as images, audio, text, time series, multi-domain, or video, and then choose the specific labeling task, like image classification, object detection, or audio transcription. 5. Begin labeling your data with customizable tags and templates. 6. Link Label Studio to your Machine Learning or Artificial Intelligence pipeline, and use webhooks, Python SDK, or API for secure authentication, project management, and model predictions. 7. Use the Data Manager to explore and manage your dataset with advanced filters. 8. Manage multiple projects, use cases, and users within the Label Studio platform.

Label Studio is specifically created to support a wide range of data formats including images, audio files, text, time series data, and video content.

Label Studio offers multiple integration options. It provides webhooks, a Python software development kit, and an application programming interface. These tools enable smooth connection with your Machine Learning and Artificial Intelligence pipeline. You can use them to authenticate, create projects, import tasks, manage model predictions, and more.

Label Studio provides ML-assisted labeling using predictions to help with the labeling process. It integrates with ML models at the backend, saving time and increasing efficiency.

Label Studio supports connection to cloud object storage via integrations with S3 and GCP, allowing for direct labeling of data stored in the cloud.

It's absolutely true. Label Studio offers support for multiple projects, different use cases, and various users all within one platform, making it suitable for a wide range of labeling needs.

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