Big Transfer Image Classification Model Quantization with NNCF in OpenVINO™

This tutorial demonstrates how to apply 'INT8' quantization to the Big Transfer Image Classification model. Here we demonstrate fine-tuning the model, OpenVINO optimization and followed by INT8 quantization processing with NNCF.

Notebook Contents

This tutorial consists of the following steps:

Prepare Dataset.
Plotting data samples.
Model fine-tuning.
Perform model optimization (IR) step.
Compute model accuracy of the TF model.
Compute model accuracy of the optimized model.
Run nncf.Quantize for getting an Optimized model.
Compute model accuracy of the quantized model.
Compare model accuracy of the optimized and quantized models.
Compare inference results on one picture

Installation Instructions

This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.