Use Cases

Computer Vision

GLAI enhances computer vision models with advanced AI techniques, enabling efficient, scalable processing of images.

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The Challenge

Frequent Retraining Needs

High Retraining Costs

Data Privacy Concerns

Why Choose GLAI for Computer Vision?

GLAI empowers organizations to unlock the full potential of computer vision by combining efficiency, adaptability, and security. Whether you’re working on image recognition, autonomous systems, or medical imaging, GLAI ensures your AI models remain accurate, cost-effective, and ready for real-world challenges.

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Results: ResNet 152 with GLAI vs. Traditional Methods

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ResNet 152, a residual DNN architecture, has been a stalwart in image classification tasks. Renowned for its depth, ResNet 152 has found applications in various domains, including computer vision, medical image analysis, and more. However, the increasing retraining time associated with ResNet 152 has become a significant challenge in the face of the growing demand for continual model updates.

Deep Neural Network used: ResNet 152

Dataset: ImageNet (99% training + 1% incremental retraining).

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While ResNet 152 required retraining with the entire ImageNet dataset (99% + 1%), GLAI only needed the additional 1%, highlighting its streamlined approach.

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Advantages

GLAI for Computer Vision

GLAI transforms how computer vision models like ResNet 152 are trained and retrained, addressing these challenges with groundbreaking innovations

Streamlined Incremental Retraining

GLAI allows models to be updated using only new data (e.g., 1% of the dataset) while maintaining the same accuracy as full retraining on 100% of the dataset

No Catastrophic Forgetting

GLAI ensures that models retain prior insights while integrating new data, delivering consistent and reliable performance over time

Faster Retraining Time

With GLAI, retraining is up to 1000x faster, enabling real-time model updates in dynamic environments

Key Benefits

Key Benefits of Using GLAI in Computer Vision

Low-Latency Model Updates

Real-time retraining ensures that AI systems stay accurate and responsive in dynamic environments like autonomous vehicles or surveillance systems

Cost and Energy Efficiency

By reducing computational demands, GLAI lowers operational costs and energy consumption, making it ideal for large-scale deployments

Enhanced Privacy Compliance

On-device retraining eliminates the need to transfer sensitive data, ensuring compliance with strict privacy regulations in industries like healthcare and finance

Scalability Across Applications

From medical imaging to smart cities, GLAI’s efficiency enables seamless deployment of computer vision models across diverse use cases