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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 Hive Data?

Create training datasets for computer vision models through our all-encompassing management solution, as we recognize that the effectiveness of data labeling is vital for developing successful deep learning applications. Our goal is to position ourselves as the leading data labeling platform within the industry, allowing enterprises to harness the full capabilities of AI technology. To facilitate better organization, categorize your media assets into clear segments. Use one or several bounding boxes to highlight specific areas of interest, thereby improving detection precision. Apply bounding boxes with greater accuracy for more thorough annotations and provide exact measurements of width, depth, and height for a variety of objects. Ensure that every pixel in an image is classified for detailed analysis, and identify individual points to capture particular details within the visuals. Annotate straight lines to aid in geometric evaluations and assess critical characteristics such as yaw, pitch, and roll for relevant items. Monitor timestamps in both video and audio materials for effective synchronization. Furthermore, include annotations of freeform lines in images to represent intricate shapes and designs, thus enriching the quality of your data labeling initiatives. By prioritizing these strategies, you'll enhance the overall effectiveness and usability of your annotated datasets.

Media

Media

Integrations Supported

Integrations Supported

Apache Atlas
Hive AutoML
Quickwork
Style Intelligence
Taxi for Email
Toad Intelligence Central
Wyn Enterprise
bipp

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$25 per 1,000 annotations

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

LabelMe

Company Website

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

Company Facts

Organization Name

Hive

Date Founded

2013

Company Location

United States

Company Website

thehive.ai/data-labeling

Categories and Features

Image Annotation

Not specified

Categories and Features

AI Vision Models

Not specified

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
SDK
Supports Audio Files
Team Collaboration

Image Annotation

Not specified

Image Recognition

Not specified

Video Annotation

Not specified

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