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What is Supervisely?

Our leading-edge platform designed for the entire computer vision workflow enables a transformation from image annotation to accurate neural networks at speeds that can reach ten times faster than traditional methods. With our outstanding data labeling capabilities, you can turn your images, videos, and 3D point clouds into high-quality training datasets. This not only allows you to train your models effectively but also to monitor experiments, visualize outcomes, and continuously refine model predictions, all while developing tailored solutions in a cohesive environment. The self-hosted option we provide guarantees data security, offers extensive customization options, and ensures smooth integration with your current technology infrastructure. This all-encompassing solution for computer vision covers multi-format data annotation and management, extensive quality control, and neural network training within a single platform. Designed by data scientists for their colleagues, our advanced video labeling tool is inspired by professional video editing applications and is specifically crafted for machine learning uses and beyond. Additionally, with our platform, you can optimize your workflow and markedly enhance the productivity of your computer vision initiatives, ultimately leading to more innovative solutions in your projects.

What is Labellerr?

Labellerr serves as a cutting-edge data annotation platform designed to simplify the development of high-quality labeled datasets that are crucial for artificial intelligence and machine learning initiatives. It supports a diverse range of data types, including but not limited to images, videos, text, PDFs, and audio, catering to a variety of annotation needs. By incorporating automated functionalities such as model-assisted labeling and active learning, the platform significantly accelerates the labeling process and boosts efficiency. Additionally, Labellerr integrates advanced analytics and smart quality assurance mechanisms to ensure that the annotations are both accurate and trustworthy. For projects requiring specialized knowledge, it offers expert-in-the-loop services, connecting users with professionals in fields like healthcare and automotive to guarantee exceptional outcomes. This all-encompassing strategy not only streamlines data preparation but also fosters confidence in the accuracy and reliability of the labeled datasets that are generated. Ultimately, Labellerr empowers organizations to harness the full potential of their data through superior annotation solutions.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Version

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Not specified

Company Facts

Organization Name

Supervisely

Date Founded

2017

Company Location

United States

Company Website

supervise.ly/

Company Facts

Organization Name

Labellerr

Date Founded

2022

Company Location

United States

Company Website

www.labellerr.com

Categories and Features

Computer Vision

Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration

Data Annotation

Not specified

Data Labeling

Labeling Automation
Labeling Quality
Supports Audio Files
Task Management
Team Collaboration

Image Annotation

Not specified

Image Recognition

Not specified

Machine Learning

Not specified

Neural Network

Not specified

Video Annotation

Not specified

Categories and Features

AI Fine-Tuning

Not specified

Data Annotation

Not specified

Data Labeling

Not specified

Image Annotation

Not specified

RLHF

Not specified

Video Annotation

Not specified

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