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What is T-Rex Label?

T-Rex Label serves as an advanced annotation tool designed for complex scenario labeling across various industries. It has gained popularity among users aiming to optimize their workflows and effortlessly create high-quality datasets. By leveraging visual prompts, T-Rex allows for the quick prediction of multiple bounding boxes at once, which is particularly advantageous for annotating intricate and densely populated scenes. Its impressive zero-shot detection capability enables the tool to handle detailed scenes across different sectors without requiring fine-tuning, making it applicable in fields ranging from agriculture to logistics. This tool significantly aids numerous algorithm engineers and researchers in speeding up their annotation tasks, which in turn promotes the creation of superior datasets. Additionally, T-Rex2 represents a significant leap towards more flexible and adaptable object detection, integrating the combined strengths of language and visual inputs to broaden its applicability. The ongoing development of T-Rex not only boosts efficiency but also establishes a new benchmark in the data annotation technology landscape. As a result, professionals can expect enhanced capabilities and innovative solutions to meet their specific annotation needs.

What is DataSeeds.AI?

DataSeeds.ai excels in offering a vast array of ethically sourced, high-quality datasets comprising images and videos specifically crafted for AI training, with options for both standard collections and custom solutions. Their comprehensive libraries contain millions of fully annotated images, which include diverse data such as EXIF metadata, content labels, bounding boxes, expert evaluations of aesthetics, contextual information about scenes, and pixel-level segmentation masks. These datasets are particularly effective for tasks involving object and scene detection, as they benefit from global coverage and a peer-ranking system to verify labeling precision. Additionally, custom datasets can be swiftly created through a wide network of contributors from over 160 nations, allowing for the acquisition of images tailored to unique technical or thematic requirements. Beyond the extensive image collections, the annotations provided feature detailed titles, thorough scene descriptions, camera specifications—including type, model, lens, exposure, and ISO—as well as environmental characteristics and optional geo/contextual tags to further improve data usability. This unwavering dedication to quality and detail positions DataSeeds.ai as an indispensable asset for AI developers in need of trustworthy training resources, enhancing their projects with reliable and diverse datasets. Furthermore, the company’s focus on ethical sourcing ensures that users can develop AI systems with integrity and responsibility.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

T-Rex Label

Company Location

United States

Company Website

trexlabel.com

Company Facts

Organization Name

DataSeeds.AI

Company Location

United States

Company Website

www.dataseeds.ai/

Categories and Features

Data Labeling

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

Categories and Features

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