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What is LabelMe?

LabelMe is designed as a web-based platform that enables users to annotate images, thereby assisting in the development of image databases vital for research in the field of computer vision. Through its annotation tool, users can play an integral role in expanding this growing database. Images can be arranged into organized collections, and users have the option to create nested collections similar to traditional folder structures. When downloading their database, users will find that the arrangement of collections mirrors this folder organization. Additionally, users can upload their images to these collections and annotate them using the LabelMe tool. There are also unlisted collections that can be accessed by anyone with the specific URL, even though they remain hidden from public view. Ultimately, LabelMe strives to make both images and annotations freely available to the research community, promoting collaboration and fostering innovation. This dedication to open access underscores the significance of shared resources in propelling advancements in computer vision research, while also encouraging diverse contributions from various users.

What is Alegion?

An advanced labeling platform designed for various stages and types of machine learning development is at your service. By utilizing a collection of top-tier computer vision algorithms, we can swiftly identify and categorize the content within your images and videos. Traditionally, creating thorough segmentation data has been a labor-intensive endeavor; however, our machine assistance can enhance productivity by up to 70%, ultimately conserving both time and financial resources. We harness machine learning to suggest labels that facilitate and expedite human labeling processes, employing computer vision models that can automatically detect, localize, and classify elements in your images and videos before passing the task to our skilled workforce. This approach to automatic labeling not only decreases labor costs but also allows annotators to focus on the more intricate aspects of the annotation process. Furthermore, our video annotation tool is engineered to natively support 4K resolution and lengthy videos, incorporating cutting-edge features such as interpolation, object proposal, and entity resolution, ensuring a comprehensive and efficient annotation experience. With our platform, you can achieve higher accuracy and efficiency in your machine learning projects.

Media

Media

Integrations Supported

Integrations Supported

Amazon S3
Azure Storage
Google Cloud Storage

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$5000
Hourly rates for Control SaaS and Alegion Managed Platform offerings. Hourly, per project, and per annotation pricing model options for Fully Managed Labeling Services.
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training

Company Facts

Organization Name

LabelMe

Company Website

labelme.csail.mit.edu/Release3.0/

Company Facts

Organization Name

Alegion

Date Founded

2014

Company Location

United States

Company Website

www.alegion.com

Categories and Features

Image Annotation

Not specified

Categories and Features

Computer Vision

Blob Detection & Analysis
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration

Data Annotation

Not specified

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Data Preparation

Data Transformation
Machine Learning
Visual User Interface

Image Annotation

Not specified

Machine Learning

Natural Language Processing (NLP)

Natural Language Processing

Named Entity Recognition
Part-of-Speech Tagging
Sentence Segmentation

Video Annotation

Not specified

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