Label Studio 1.6 Release Adds Video Object Tracking and New Annotation UI to Popular Open-Source Data Labeling Platform

Latest version of the open-source data labeling software supports all data types in one platform—images, text, audio, video and time series data

SAN FRANCISCO (PRWEB) September 27, 2022

The Label Studio 1.6 open-source release now supports video object tracking in general availability, making it the most popular open-source data labeling platform to support all data types—video, image, text and hypertext, time-series, and audio. In addition to the new video player that supports frame-by-frame video object tracking, the latest release also features a new annotation user interface (UI) that is more efficient, ergonomic, and flexible.

Label Studio is the most popular open-source labeling software, with more than 5 million downloads, 150,000 users, 10,000 stars on GitHub, and a community of nearly 6,000 data science professionals. The platform was designed to be flexible and extensible—not only by supporting a wide range of machine learning and AI use cases—but by providing a programmable interface and webhooks, Python SDK, integrations with all major cloud storage providers, and customizable workflows and labeling interfaces for each project.

Video Classification was available prior to version 1.6, but the latest release introduces a new video player and enables Video Object Tracking, giving users the ability to:

• Label and track objects across subsequent frames, for fine-grained control
• Use optional hotkey-driven navigation for even greater granularity in tracking objects
• Add keyframes and automatically interpolate bounding boxes between keyframes to easily transition bounding box sizes and positions
• Select and label between timestamps on the video with improved timeline segmentation

The major update to the annotation UI in the 1.6 release supports the new video player, but benefits labeling workflows for every data type. New functionality includes:

• Dual ‘Region' and ‘Details' control panels vs. a single sidebar, allowing annotators to view all pertinent information about the task without scrolling
• Collapsable, draggable, and resize-able panels for annotators to customize their workspace, especially useful to view images or video at a larger scale
• More granular controls for each bounding box, including the ability to precisely set width, height, and coordinates to fit an object for pixel-perfect annotations
• A cleaner interface to group, organize regions, link, add metadata and delete regions, plus new functionality to lock regions

Heartex, creator of the open-source Label Studio project, offers Label Studio Enterprise as a cloud service to meet demand for customers who are scaling data labeling operations. Heartex Label Studio Enterprise includes expanded security, productivity, operational, quality management, reporting, and analytics capabilities required by organizations running machine learning and AI models in production.

"For the first time, data science professionals have a single, open-source data labeling platform and workflow for all of their projects, regardless of data type, without having to cobble together and manage different tools," said Michael Malyuk, CEO and co-founder of Heartex. "Label Studio 1.6 is an important move toward a future in which we think about dataset development as a pipeline workflow, making the work of these professionals more productive, streamlined, and extensible."

Resources:

• Repo to download Label Studio 1.6
• Demo to learn more about the software
• Release notes
• Webinar

*About Heartex*
Heartex is the company behind Label Studio, the most popular open-source data labeling platform for Machine Learning & AI. Founded in 2019 by data scientists and engineers who faced common challenges with model accuracy due to poor quality training data, the team believed the only viable solution was to enable internal teams with domain expertise to annotate and curate training data. They created Label Studio with a focus on usability, flexibility, and collaborative workflows that support internal data labeling operations at scale and increase the accuracy of ML/AI models.

Today, Label Studio has been used by more than 150,000 people around the world to label 20M+ pieces of data, including production ML/AI initiatives for enterprises like Bombora, Geberit, Outreach, Trivago, Wyze, Zurich Insurance Group, and more. For more information, visit http://www.heartex.com.

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