Trexlabel

What is T-Rex Label^[1]?

T-Rex Label is an online data annotation^[2] tool designed to facilitate efficient and rapid construction of complex scene datasets. It supports various visual models including Grounding DINO^[3], DINO-X^[4], and T-Rex, and is compatible with COCO and YOLO dataset annotation formats. This tool offers a range of annotation functionalities such as 2D bounding boxes and segmentation, making it an essential asset for users in various industries needing high-quality annotated data.

How to use T-Rex Label?

1. Visit the T-Rex Label website at https://trexlabel.com/.
2. Sign up for an account or log in if you already have one.
3. Choose a project or create a new one where you want to annotate your data.
4. Upload your images that need annotation.
5. Select the appropriate annotation tool (e.g., bounding box, segmentation) based on your requirements.
6. Annotate the images by drawing bounding boxes or segmenting the objects of interest.
7. Use the AI pre-annotation feature to automatically suggest annotations based on your initial input.
8. Review and adjust the annotations as necessary.
9. Export the annotated dataset in your desired format (COCO, YOLO, etc.) for further use.

What are the main features of T-Rex Label?

• Zero-shot Object Detection: T-Rex Label can detect objects without the need for additional training or fine-tuning, allowing for quick and efficient data labeling.
• Batch Annotation: Users can select a target object, and T-Rex Label will automatically identify similar objects across images, enabling batch annotation in a single prompt.
• Browser-Based Tool: Being a web-based annotation tool, T-Rex Label eliminates the need for installation or deployment, reducing maintenance costs and making it easy for teams to get started.
• Support for Multiple Formats: T-Rex Label is compatible with popular dataset formats like COCO and YOLO, ensuring seamless integration into existing AI workflows.

Who is T-Rex Label for?

T-Rex Label is designed for professionals and organizations across various sectors that require efficient data annotation for computer vision applications. This includes industries such as agriculture, livestock monitoring, electronics, construction, retail, e-commerce, healthcare, logistics, and transportation. It caters to data scientists, machine learning engineers, and computer vision researchers who need to create high-quality datasets for training visual AI models.

What are the use cases of T-Rex Label?

1. Agricultural Monitoring: Annotating images of crops and pests to monitor growth patterns and identify issues such as diseases or infestations.
2. Livestock Management: Using T-Rex Label to track animal health and behavior through annotated images.
3. Industrial Applications: Facilitating quality control and safety inspections by annotating images of products and processes.
4. Retail Analytics: Analyzing customer behavior and inventory management through annotated images of store layouts and products.
5. Healthcare Imaging: Supporting medical professionals by annotating images for better diagnosis and treatment planning.