notebooks/image-classification-quantization/README.md

Accelerate Inference of MobileNet V2 Image Classification Model with NNCF in OpenVINO™

This tutorial demonstrates how to apply INT8 quantization to the MobileNet V2 Image Classification model, using the NNCF Post-Training Quantization API. The tutorial uses MobileNetV2 and Cifar10 dataset. The code of the tutorial is designed to be extendable to custom models and datasets.

Notebook Contents

The tutorial consists of the following steps:

• Prepare the model for quantization.
• Define a data loading functionality.
• Perform quantization.
• Compare accuracy of the original and quantized models.
• Compare performance of the original and quantized models.
• Compare 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.