Unitlab Annotate

Unitlab Annotate is a collaborative and AI-powered tool that provides on-premises solutions and integrated labeling services. Its main focus is on efficiency, quality, and cost control, aiming to optimize the data annotation process for machine learning models.

Unitlab Annotate collects raw data, allows human annotators to collaborate on it and produce highly accurate labels for machine learning models. It uses AI assistants to automatically annotate data. An AI-driven module named SAM Model (Segment Anything) and Domain-Specific Pretrained Models are used to accurately annotate images for various computer vision applications.

Yes, Unitlab Annotate is capable of carrying out optical character recognition tasks. It can perform various OCR functionalities including Document OCR, Receipt OCR, Handwritten OCR, and Education OCR.

Unitlab Annotate uses the SAM Model (Segment Anything) and Domain-Specific Pretrained Models for auto-annotation. These models automate the annotation process, which helps in precisely annotating images for various computer vision applications.

Unitlab Annotate supports approximately 123 languages for optical character recognition tasks.

Yes, Unitlab Annotate provides pretrained object detection and recognition models which simplify the process of annotating images for various computer vision applications.

Yes, Unitlab Annotate offers tools for precise polygon annotation and polyline identification and segmentation.

Unitlab Annotate provides real-time team and project statistics, giving users insights into their data annotation performance. This includes details like project statistics, team statistics, and data annotation insights.

Yes, with the help of the Command Line Interface (CLI) / Software Development Kit (SDK) of Unitlab Annotate, users can manage their data annotation projects directly from the command line.

Unitlab Annotate enhances team collaboration by providing communication channels for annotators to improve the annotation process and receive instant feedback. Should there be an inaccurate annotated data, a comment can be left by any team member to ensure it is improved or corrected.