This project implements Quantization-Aware Training (QAT) for MobileNetV2, enabling deployment on resource-constrained edge devices. Built autonomously by [NEO](https://heyneo.so), the system achieves exceptional model compression while maintaining high accuracy.
Solution Highlights:
- 9.08x Model Compression: 23.5 MB → 2.6 MB (far exceeds 4x target)
- 77.2% Test Accuracy: Minimal 3.8% drop from baseline
- Full INT8 Quantization: All weights, activations, and operations
- Edge-Ready: TensorFlow Lite format optimized for deployment
- Single-Command Pipeline: End-to-end automation
Training can be performed on newer Datasets as well.
This project implements Quantization-Aware Training (QAT) for MobileNetV2, enabling deployment on resource-constrained edge devices. Built autonomously by [NEO](https://heyneo.so), the system achieves exceptional model compression while maintaining high accuracy.
Solution Highlights: - 9.08x Model Compression: 23.5 MB → 2.6 MB (far exceeds 4x target) - 77.2% Test Accuracy: Minimal 3.8% drop from baseline - Full INT8 Quantization: All weights, activations, and operations - Edge-Ready: TensorFlow Lite format optimized for deployment - Single-Command Pipeline: End-to-end automation
Training can be performed on newer Datasets as well.
Project is accessible here: https://github.com/dakshjain-1616/Quantisation-Awareness-tra...