305-tensorflow-quantization-aware-training

Optimizing TensorFlow models with Neural Network Compression Framework of OpenVINO™ by 8-bit quantization.

This tutorial demonstrates how to use NNCF 8-bit quantization to optimize the TensorFlow model for inference with OpenVINO Toolkit. For more advanced usage, refer to these examples.

To speed up download and training, use a ResNet-18 model with the Imagenette dataset. Imagenette is a subset of 10 easily classified classes from the ImageNet dataset.

Notebook Contents

This tutorial consists of the following steps:

• Fine-tuning of FP32 model
• Transforming the original FP32 model to INT8
• Using fine-tuning to restore the accuracy.
• Exporting optimized and original models to Frozen Graph and then to OpenVINO
• Measuring and comparing the performance of the models.

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.