🚀 Project Overview

This project explores the fundamentals of on-device AI deployment with the following key components:

Reduced Latency & Enhanced Privacy: Learn how on-device inference minimizes delays and ensures user data stays private.

On-Device Deployment Concepts: Dive into graph capture, on-device compilation, and hardware acceleration techniques.

Model Conversion: Convert pretrained TensorFlow models for seamless on-device compatibility.

Real-Time Image Segmentation: Deploy a real-time image segmentation model with minimal code.

Performance Validation: Test model performance and validate numerical accuracy for on-device environments.

Model Optimization: Use quantization to make models up to 4x faster and smaller for superior performance.

Integration into Mobile Apps: Learn the steps for embedding your model into a functioning Android app.

🛠️ Technologies & Tools Used

TensorFlow: For training and converting pretrained models.

Quantization Techniques: To optimize models for performance and size.

Android Development Tools: For integrating AI models into mobile applications.

Hardware Acceleration: Techniques for utilizing device-specific accelerators like GPUs or NPUs.