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What is Prodigy?

Groundbreaking machine teaching has arrived, featuring an incredibly effective annotation tool powered by active learning. Prodigy stands out as a customizable annotation platform so proficient that data scientists can take charge of the annotation process themselves, facilitating quick iterations. The progress seen in current transfer learning technologies enables the creation of high-quality models with minimal examples. By adopting Prodigy, you can fully harness modern machine learning strategies, engaging in a more adaptable approach to data collection. This capability not only speeds up your workflow but also grants you increased independence, resulting in a significant boost in project success rates. Prodigy combines state-of-the-art insights from both machine learning and user experience design, making it exceptionally versatile. Its continuous active learning framework ensures that you only annotate cases where the model exhibits uncertainty, optimizing your time and effort. The web application is not only robust and adaptable but also complies with the most up-to-date user experience standards. What makes Prodigy truly remarkable is its intuitive design: it allows you to focus on one decision at a time, keeping you actively involved—similar to a swipe-right method for data. Furthermore, this streamlined approach enhances the overall enjoyment and effectiveness of the annotation process, making it an invaluable tool for data scientists. As a result, users can expect not just efficiency but also a more satisfying experience while navigating through their annotation tasks.

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.

Media

Media

Integrations Supported

ZenML

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

$490 one-time fee
Per seat, available in packs of 5 seats

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
Linux

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

Explosion

Date Founded

2016

Company Location

Germany

Company Website

prodi.gy/

Company Facts

Organization Name

LabelMe

Company Website

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

Categories and Features

Data Annotation

Not specified

Data Labeling

Labeling Automation
Task Management

Image Annotation

Not specified

Machine Learning

Not specified

Video Annotation

Not specified

Categories and Features

Image Annotation

Not specified

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