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T-Rex Label logo

T-Rex Label

T-Rex Label is a comprehensive data labeling platform that helps AI/ML teams annotate images, videos, text, and 3D data for model training.

Price: Premium

Categories:AI Image Segmentation ToolsAI Image Recognition ToolsAI Tools for DevelopersAI Data Mining ToolsAI Workflow Automation
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Description
T-Rex Label provides a robust platform for efficient and accurate data labeling, which is crucial for training machine learning models. It supports various data types, including images (object detection, segmentation), videos (tracking), text (NER, classification), and 3D point clouds. The platform offers powerful annotation tools, quality control features, and project management capabilities, enabling teams to scale their labeling operations. It is designed for data scientists, ML engineers, and AI development teams who need high-quality labeled datasets for computer vision, natural language processing, and other AI applications. T-Rex Label distinguishes itself with its multi-modal data support and advanced annotation features tailored for complex AI projects.

T-Rex Label screenshot 1
How to Use
1.Create a project and upload your raw data (images, videos, text, 3D files).
2.Define your annotation tasks and label schemas within the platform.
3.Assign labeling tasks to your team or external annotators.
4.Annotators use T-Rex Label's tools to label the data according to instructions.
5.Implement quality control checks and review labeled data.
6.Export the high-quality labeled dataset for model training.
Use Cases
Object detection labelingImage segmentationVideo tracking annotationText classificationNamed Entity Recognition (NER)D point cloud annotationTraining computer vision modelsTraining NLP models
Pros & Cons

Pros

  • Supports a wide range of data types (images, video, text, 3D).
  • Offers advanced annotation tools for complex tasks.
  • Includes robust quality control and project management features.
  • Scalable for large-scale data labeling operations.
  • Designed for collaborative team environments.

Cons

  • Can have a learning curve for new users due to its comprehensive features.
  • Pricing may be higher for small teams or individual researchers.
  • Requires a clear understanding of data labeling best practices for optimal use.
Pricing
Free trial: Available upon request or for a limited period
Starter Plan: Details available upon contact, usually project-based or per-user
Enterprise Plan: Custom pricing for large organizations with specific needs.
FAQs

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