Labellerr

Labellerr, developed by Tensor Matics Inc, is an automated data annotation platform that aims to assist AI teams in preparing high-quality labeled data for computer vision model training. According to the vendor, Labellerr leverages advanced analytics, smart QA, and automated annotation capabilities to process large volumes of images and videos within a few weeks. This software solution is designed for businesses of various sizes, from startups to large enterprises, and is used by AI engineers, data scientists, machine learning teams, computer vision researchers, and data annotation specialists in industries such as automotive, healthcare, security & surveillance, agritech, and retail.

Automated Labeling: According to the vendor, Labellerr offers prompt-based labeling, model-assisted labeling, and active learning-based labeling automation, which can potentially accelerate the labeling process by utilizing automation techniques.

Multiple Data Types Support: The vendor claims that Labellerr allows users to connect various data types, including images, videos, PDFs, text, or audio, to create projects, eliminating the need for multiple tools.

Smart QA: Labellerr reportedly provides pre-trained model-based and ground truth-based quality assurance, potentially reducing the time spent on manual quality checks.

MLOps Integration: The vendor states that Labellerr offers extensive integration support, allowing seamless integration with popular AI development environments like GCP Vertex AI, AWS Sagemaker, and custom-made solutions, potentially streamlining computer vision AI infrastructure.

Project Management with Advanced Analytics: Labellerr is said to provide project management capabilities with advanced analytics, enabling users to track progress and optimize the data labeling workflow.

24/7 Support: The vendor claims that Labellerr offers round-the-clock technical support for critical projects, ensuring a fast response time to address any issues or queries.

Auto Labeling Tool to Turn Raw Images Into Labels in Minutes: According to the vendor, Labellerr utilizes foundation models to automate the data labeling process, potentially enabling ML teams to generate large volumes of labeled data quickly.

Smart Feedback Loop: The vendor states that Labellerr employs an AI supervisor bot to govern the end-to-end data pipeline, automating data collection, curation, and annotation processes.

Pre-label Large Datasets in Few Hours: Leveraging foundation models, Labellerr is claimed to be capable of pre-labeling large volumes of datasets more efficiently compared to manual annotation, potentially saving significant time and cost.

Annotation Services for Automotive: Labellerr is said to support various annotation services for automotive use cases, including object detection, road segmentation, and polyline drawing for autonomous vehicles.