T-Rex LabelTranslation site

What is T-Rex Label

T-Rex Label is an AI-driven intelligentimage annotationIDEA computer vision tool, developed by IDEA's computer vision team, relies on the T-Rex2 open-set target detection model and focuses on solving the problems of low efficiency and high cost of data annotation in complex scenes. Its core advantage lies in its zero sample detection capability and visual cue interaction technology, which allows it to quickly label various types of targets without additional training, and is suitable for complex scenes such as dense, occlusion, and light changes, which greatly improves the labeling efficiency and accuracy.

T-Rex Label Key Features

1. Zero-Shot Detection (ZSD)
   • Direct labeling without training: Without pre-training or fine-tuning the model, unseen target classes can be directly recognized and labeled, which is particularly suitable for rare or dynamically changing targets (e.g., agricultural pests, industrial defects).
   • Strong generalization ability: excels in cross-domain datasets, supporting multi-industry applications from agriculture to autonomous driving.
2. Visual Prompt interaction (Visual Prompt)
   • Checkboxes are labeled: Users only need to select a target in the image as a hint, the system can automatically identify similar targets and complete the labeling, without the need for verbal description, reducing the threshold of operation.
   • Cross-diagram batch labeling: Tip information can be synchronized to other images to achieve "mark once, batch application", significantly reducing the duplication of efforts.
3. Intelligent labeling with one click
   • Efficient automationSupport one-click target detection, classification and labeling, and improve labeling efficiency compared with traditional tools. 99%The processing time for a single image is only 0.5 seconds.
   • High-precision labeling: In complex scenes (e.g., dense targets, occluded objects), the labeling accuracy is significantly higher than models such as YOLOv8.
4. Multi-format support and compatibility
   • Full coverage of mainstream formatsSupport COCO, YOLO, VOC and other mainstream annotation formats, seamlessly connecting to the existing data pipeline.
   • Browser ready to use: No need to install complex environments that support GitHub One-click account login to lower the threshold of use.
5. Human-assisted corrections
   • AI-assisted calibrationThe AI-assisted correction of mislabeling through AI-assisted correction of mislabeling balances efficiency and accuracy.
   • Undo and Redo: Provide shortcuts (e.g. Ctrl+Z Withdrawn,D (key delete) and selection mode to optimize the annotation experience.

T-Rex Label Usage Scenarios

1. Automated driving and traffic monitoring
   • Road scene labeling: Quickly label vehicles, pedestrians, traffic signs, etc., supporting high-precision map construction and automatic driving model training.
   • Night and complex lighting scenes: Stable marking of targets even under conditions of strong light, shadows and low light.
2. Medical Image Analysis
   • focus annotation: Assist in labeling the tumor, fracture, and other lesion areas in medical images to improve diagnostic efficiency and data consistency.
   • Cell and Tissue Recognition: Support for cell segmentation and classification in microscope images.
3. Agricultural and ecological monitoring
   • Crop Pest and Disease Identification: Labeling pests and diseases on crop leaves to support precision agriculture and pest warning.
   • Wildlife monitoring: Labeling animal behavioral trajectories in field environments to support ecological conservation research.
4. Industrial quality control and logistics
   • Defect Detection: Labeling of scratches, cracks and other defects on the product surface to enhance the efficiency of quality control.
   • parcel sorting: Labeling parcel types and locations in logistics scenarios to optimize the sorting process.
5. Retail and e-commerce
   • Commodity labeling: Quickly label product images, support inventory management, recommender system and advertising.

T-Rex Label User's Guide

1. Data preparation
   • Importing Images: Supports single or multiple image import (up to 200 images) in common formats (e.g. JPEG, PNG).
   • Definition of classification: Press A key to enter the smart annotation mode and create a target category (e.g. head,car).
2. Marking process
   • Intelligent labeling::
     1. Left-click to frame the target and press Enter Acknowledgment;
     2. The system automatically labels similar targets;
     3. Press the left and right arrow keys to switch images and complete the batch labeling.
   • manual amendment::
     • check or refer to R key to enter the manual labeling mode to correct missed or incorrect labels;
     • check or refer to D key to enter selection mode and delete the error labeling box.
3. Labeling Export
   • Format Selection: Supports COCO, YOLO format export;
   • format conversion: Provide scripts (e.g. yolo2voc.py) Converts YOLO format to VOC format.
4. Advanced Features
   • confidence interval (math.): Optimize the annotation results by adjusting the confidence threshold;
   • shortcut operation: Support for undoing (Ctrl+Z), deleted (Del), switching pictures (arrow keys) and other quick operations.

T-Rex Label Recommended Reasons

1. Significant efficiency gains
   • Labeling Speed Improvement 99%T-Rex Label: T-Rex Label takes minutes to complete a task that would take hours with traditional tools.
   • Zero sample testing saves time and effort: No need to collect data, train models, and apply them directly to new scenarios.
2. High marking accuracy
   • Excellent performance in complex scenes: Labeling accuracy far exceeds that of traditional models under conditions such as dense targets, occlusion, and light changes.
   • AI-assisted corrections: Ensure reliable labeling results by combining manual calibration with AI.
3. easy-to-use
   • out-of-the-box: No need to install complex environments, direct browser access, GitHub account login support.
   • easy operation: Visual cue interaction reduces language dependency and allows even novices to get started quickly.
4. Cost-effective
   • Reduced labor costs: Reduce manual labeling time above 80% and save enterprise cost.
   • free of charge: Completely free, suitable for individual developers, SMEs and research teams.
5. Industry universality
   • Multi-disciplinary coverage: From agriculture to healthcare, from industry to transportation, to meet cross-industry labeling needs.
   • Open Source Ecological Support: Support for custom models and plug-ins, adapted to the special needs of vertical domains.