Nice results on the compression ratio. One thing worth measuring alongside file size is actual on-device inference behavior after quantization. We ran FP32 and INT8 variants of a similar-scale model on Snapdragon 8 Gen 3 through Qualcomm AI Hub. Inference barely changed (0.176ms vs 0.187ms) but peak memory actually went up slightly (121MB vs 124MB) which was counterintuitive. The file was 70% smaller but runtime characteristics didn't follow the same pattern. Would be interesting to see TFLite inference timings on actual mobile hardware alongside the size numbers. Compression without on-device profiling can be misleading.

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...