Open Source
Data Labeling Tool

At Label Studio, we’re always looking for ways to help you accelerate your data annotation process. With the release of version 1.3.0, you can perform model-assisted labeling with any connected machine learning backend.

By interactively predicting annotations, expert human annotators can work alongside pretrained machine learning models or rule-based heuristics to more efficiently complete labeling tasks, helping you get more value from your annotation process and make progress in your machine learning workflow sooner.

It can be difficult to get from raw data to a fully trained model, but the more you can do to automate your machine learning pipeline, the easier the process is. If you’re using Amazon SageMaker but have complex labeling scenarios and corner cases, add Label Studio to your Amazon SageMaker machine learning pipeline and simplify annotating your data.

If you have a machine learning pipeline, or retrain your models frequently based on newly-annotated data, you know that it can be challenging to automate that process. Now that Label Studio supports webhooks, you can automatically receive updates every time a new annotation is created or a project is updated to include different labels.

Object detection is an important task in machine learning, used to underpin facial recognition technologies, essential computer vision tasks for autonomous driving use cases, and more.

Like all machine learning tasks, creating datasets and training the machine learning models for your use case is a tedious and time-consuming requirement. With Label Studio you can collaborate with a team of annotators and quickly label a training dataset for a custom YOLO object detection model.