Tensorflow Quantization

Here i have illustrated and compared speed and size affeact on quantization and model reduction.

Layer Quantization

It is Post-training quantization conversion technique that can reduce model size while also improving CPU and hardware accelerator latency, with little degradation in model accuracy.

S No.	Name	Train Accuracy	Test Accuracy	Model Size
1	Float Model	0.9966	0.9800	84.8KB
2	Quantized Model	0.9966	0.9732	23.1KB

Model Quantization

Quantization works by reducing the precision of the numbers used to represent a model's parameters, which by default are 32-bit floating point numbers. This results in a smaller model size and faster computation.

This was test on i5 8600K and nivida gtx 1080.

S No.	Name	Test Accuracy	Speed	Model Size
1	Normal Model	0.9823	0.3968 secs	272.8KB
2	Quantized Model	0.9816	1.366 secs	23.4KB
2	TFLite Model	0.9823	0.8510 secs	83.4KB

Speed depends on architecture and devices.