README.md

MCT Features

This tutorial set introduces the various quantization tools offered by MCT. The notebooks included here illustrate the setup and usage of both basic and advanced post-training quantization methods. You'll learn how to refine PTQ (Post-Training Quantization) settings, export models, and explore advanced compression techniques such as GPTQ (Gradient-Based Post-Training Quantization), Mixed precision quantization and more. These techniques are essential for further optimizing models and achieving superior performance in deployment scenarios.

Keras Tutorials

Post-Training Quantization (PTQ)

Tutorial	Included Features
Basic Post-Training Quantization (PTQ)	✅ PTQ
Mixed-Precision MobileNetV2	✅ PTQ ✅ Mixed-Precision

Gradient-Based Post-Training Quantization (GPTQ)

Tutorial	Included Features
MobileNetV2	✅ GPTQ

Quantization-Aware Training (QAT)

Tutorial	Included Features
QAT on MNIST	✅ QAT

Structured Pruning

Tutorial	Included Features
Fully-Connected Model Pruning	✅ Pruning

Export Quantized Models

Tutorial	Included Features
Exporter Usage	✅ Export

Debug Tools

Tutorial	Included Features
Network Editor Usage	✅ Network Editor

Pytorch Tutorials

Post-Training Quantization (PTQ)

Tutorial	Included Features
Basic Post-Training Quantization (PTQ)	✅ PTQ
Mixed-Precision Post-Training Quantization	✅ PTQ ✅ Mixed-Precision
Advanced Gradient-Based Post-Training Quantization (GPTQ)	✅ GPTQ

Structured Pruning

Tutorial	Included Features
Fully-Connected Model Pruning	✅ Pruning

Data Generation

Tutorial	Included Features
Zero-Shot Quantization (ZSQ) using Data Generation	✅ PTQ ✅ ZSQ ✅ Data-Free Quantization ✅ Data Generation

Export Quantized Models

Tutorial	Included Features
Exporter Usage	✅ Export

Quantization Troubleshooting

Tutorial	Included Features
Quantization Troubleshooting using the Xquant Feature	✅ Debug