Labellerr

Harness the potential of intelligent automation with Labellerr, a solution designed to streamline workflows and enhance operational efficiency. Combining AI Data Annotation, Image & Video Labeling, Dataset Management, and Quality Assurance, the platform assists with everything from generating insights to optimizing routine processes. Whether improving productivity, supporting faster decisions, or reducing manual effort, Labellerr Software helps businesses embrace innovation and adapt to an increasingly data-driven environment.

Overview

Key takeaways

Insights from verified user reviews

Learn about Labellerr, an AI data labeling platform offering automated annotation, image and video labeling, quality control, integrations, and more.

The platform, Labellerr supports images, videos, text, audio, and PDFs, allowing teams to manage different types of data from one workspace. Its automation capabilities can reduce the amount of manual work involved in preparing datasets while allowing users to maintain quality control throughout the annotation process.

Features and Functionalities

  • Automated Labeling

    With an automated labeling feature, Labellerr allows the team to automate the labeling process in order to speed up their work and reduce repetitive tasks. The prompt-based labeling, model-assisted labeling, and active learning can be used to increase the speed of dataset preparation for AI and machine learning projects.

  • Image and Video Annotation

    The data labeling platform offers various types of visual annotation features such as classification, object detection, bounding boxes, segmentation, tracking, and keypoint annotation which can be used in computer vision projects.

  • Object Labeling

    With the object labeling feature, teams can label objects within visual datasets. This is achieved using various types of annotation methods including but not limited to bounding boxes, segmentation, among others.

  • Annotation With AI Assistance

    Labellerr utilizes AI models to facilitate the annotation process. The platform is capable of working with Segment Anything models by Meta like SAM, SAM 2, and SAM 3, which may help to automate the segmentation process and reduce the need for manual labeling.

  • Handling Multiple Data Types

    People can work with various data types in one system. Labellerr allows images, videos, PDF files, text, and audio; in addition, the enterprise plan includes support for such data formats as DICOM, LiDAR, and NIfTI.

  • Quality Control

    Quality management tools allow teams to examine and validate annotations before using them for the training of models.

Core Use Cases & Primary Users

This part describes ways in which Labellerr could be used by companies which require dataset preparation, annotation, management, and review for the purposes of developing AI and machine learning algorithms.

AI & ML Teams

Teams working with AI can use the platform for preparing training datasets, setting up annotation processes, and integrating annotated data into their model building processes.

Computer Vision Teams

Computer vision teams can leverage the platform for image annotation tasks related to object detection, object recognition, object segmentation, object tracking, and keypoint analysis.

Healthcare Organizations

Operations teams can leverage Asana to organize periodic operations, assign duties, check deadlines, and have better visibility of workflows.

Robotics Teams

Robotics teams can utilize Labellerr for multimodal datasets and annotations that include objects, poses, actions, keypoint annotation, and environmental information. Such datasets will be helpful for building robotics and spatial AI models.

Research Teams

Labellerr can be used by research teams to build datasets for their experiments on AI and machine learning. For Researcher plan, data credits and a few seats are available for free.

Labellerr is recommended for:

AI and machine learning teams Computer vision teams Data scientists Machine learning engineers Robotics teams Healthcare AI companies Researchers Companies dealing with big data sets Vision, NLP, and LLM application development teams

Integrations

Labellerr can be integrated with existing data and machine learning environments in order to make it easier for companies to implement annotations in their overall AI process.

It can be integrated with:

AWS Google Cloud Microsoft Azure AWS SageMaker Google Vertex AI Azure ML Machine learning training environments Custom ML pipelines SDK and API workflows

What Is the Cost of Labellerr?

The cost of using Labellerr depends on the scale and needs of the business. The researcher plan is available for free. It comes with 2,500 data credits, one seat, one workspace, and support for all data types.

The pro plan is priced at $9,999 per year and comes with 100,000 data credits, up to 200 seats, unlimited projects, automation features, SDK and Human-in-the-Loop services. Enterprise pricing is customizable and includes features like unlimited data credits and seats, multiple workspaces, SSO, enterprise support, and optional private cloud/on-premise deployment.

It is recommended that businesses check out the pricing of Labellerr for the most accurate information as costs can vary depending on other factors.

Demo and Free Trial

Labellerr provides ways for teams to test the platform's data annotation services before choosing a plan. For example, Labellerr offers a free plan, which is called Researcher and includes up to 2,500 data credits. However, paid plans offer more automation, collaboration, integration, and enterprise-level services.

Teams can use the above-mentioned services to check whether Labellerr suits the scale of datasets and annotation tasks and the machine learning environment.

Frequently Asked Questions

What are the main features of Labellerr?

Key features include automated labeling, image and video annotation, object labeling, segmentation, model-assisted labeling, active learning, quality assurance, project management, analytics, SDK integration, and support for multiple data types.

Is Labellerr an AI data labeling platform?

Yes. Labellerr is an AI data labeling platform designed to help teams prepare and annotate datasets for artificial intelligence and machine learning applications. It combines manual annotation with automated and AI-assisted labeling capabilities.

What is a cluster based data quality labeling annotation pipeline?

A cluster based data quality labeling annotation pipeline is a workflow that groups similar data items or annotations into clusters so teams can identify patterns, review data quality, and prioritize labeling or correction tasks. This type of workflow can help AI teams manage large datasets more efficiently.

Does Labellerr provide image annotation services?

Yes. Labellerr provides image annotation capabilities and annotation services covering tasks such as object detection, classification, segmentation, and other visual labeling requirements. It also provides access to human-in-the-loop annotation services through applicable plans.