TIGEREYE Announces the Innovative AI Data Annotation System “TigerDataGen”
Achieves Industry-Leading Automated Data Labeling with Qwen-VLM & DeepSeek-VLM
Tokyo, Japan – February 6, 2025 – TIGEREYE Inc. (Headquarters: Chuo-ku, Tokyo; CEO: Manabu Uemura) has announced the launch of TigerDataGen, a next-generation AI system for data annotation powered by state-of-the-art Vision-Language Models (VLMs). This system integrates fine-tuned Qwen-VLM and DeepSeek-VLM, enabling high-precision data labeling with minimal human supervision.
Key Features of TigerDataGen
High-Precision Annotation with Multi-VLM Integration
TigerDataGen maximizes labeling accuracy and efficiency by selecting the optimal VLM for each task, utilizing both Qwen-VLM and DeepSeek-VLM.
AI-Driven Interactive Annotation (Human-in-the-Loop)
The AI performs automatic labeling, requiring only minimal verification and corrections by human annotators.
Self-Learning Annotation Pipeline
A continuous feedback loop enhances the labeling accuracy over time.
Seamless Integration with TIGEREYE AI Models for Object Detection & Recognition
Labeled data from TigerDataGen is directly integrated into TIGEREYE’s AI models, improving real-time learning and recognition accuracy.
Scalable Data Pipeline
Automates data annotation across multiple industries, including:
• Architectural blueprints
• Mechanical drawings
• Circuit diagrams
• Medical imaging
• Security footage
Industry Impact & Future Development
TigerDataGen is expected to revolutionize AI training data generation for developers, enterprises, and research institutions. It will be particularly beneficial for design analysis, video data labeling, and AI training datasets.
Currently, Proof of Concept (PoC) experiments are underway to validate automatic annotation accuracy in fields such as architectural design, mechanical engineering, and security footage analysis. In the future, TIGEREYE plans to expand TigerDataGen into a full-scale enterprise platform.
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