Label Studio 1.12.0 🚀Automate & Evaluate Labeling Predictions Using LLMs & ML Models
Quick Start
Back to integrations

Semi-Automated Bounded Box Labeling with OpenMMLab

Get startedRead the Docs

Overview

OpenMMLab integrates with the Label Studio machine learning API to provide semi-automated bounded-box labeling using the mmdetection library and the RTMDdet model. Quickly run pre-labeling on images with the integration, using standard COCO format, using MMDet’s algorithms and existing weight files. Thanks to Label Studio’s model refinement feature users can create a high-quality dataset with minimal effort compared to the manual annotation of images.

Benefits

Integrating OpenMMLab with Label Studio provides the following benefits:

  • Automated Labeling: get a quick start on your image labeling with automatic object detection
  • Standard COCO Format: bounding boxes are supplied using the standard COCO format.
  • Fast Integration: The machine learning connection is provided by the library, allowing for a rapid connection to Label Studio with minimal coding.

Related Integrations

Scikit Learn

Machine learning toolkit

LangChain

Evaluate LLM Output Quality

TensorFlow

Open source deep learning framework

Segment Anything Model

Image Segmentation Model