MCT Tutorials

Explore the Model Compression Toolkit (MCT) through our tutorials, covering compression techniques for Keras and PyTorch models. Access interactive Jupyter notebooks for hands-on learning.

Getting started

Learn how to quickly quantize pre-trained models using MCT’s post-training quantization technique for both Keras and PyTorch models.

• Post training quantization with Keras
• Post training quantization with PyTorch

MCT Features

This set of tutorials covers all the quantization tools provided by MCT. The notebooks in this section demonstrate how to configure and run simple and advanced post-training quantization methods. This includes fine-tuning PTQ (Post-Training Quantization) configurations, exporting models, and exploring advanced compression techniques. These techniques are essential for further optimizing models and achieving superior performance in deployment scenarios.

• MCT Features notebooks

Quantization for Sony-IMX500 deployment

This section provides several guides on quantizing pre-trained models to meet specific constraints for deployment on the Sony-IMX500 processing platform. We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal deployment performance.

• MCT IMX500 notebooks