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Tutorials

Table of Contents

Introduction

Dive into the Model-Compression-Toolkit (MCT) with our collection of tutorials, covering a wide range of compression techniques for Keras and Pytorch models. We provide both Python scripts and interactive Jupyter notebooks for an engaging and hands-on experience.

Keras Tutorials

Post-Training Quantization (PTQ)
Tutorial Included Features
MobileNetV2 ✅ PTQ
Mixed-Precision MobileNetV2 ✅ PTQ
✅ Mixed-Precision
Nanodet-Plus ✅ PTQ
YoloV8-nano ✅ PTQ
EfficientDetLite0 ✅ PTQ
sony-custom-layers integration
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

Quick-Start with Torchvision
Tutorial
Quick Start - Torchvision Pretrained Model
Post-Training Quantization (PTQ)
Tutorial Included Features
Training & Quantizing Model on MNIST ✅ PTQ
Mixed-Precision MobileNetV2 on Cifar100 ✅ PTQ
✅ Mixed-Precision
SSDLite MobileNetV3 Quantization ✅ PTQ
Quantization-Aware Training (QAT)
Tutorial Included Features
QAT on MNIST ✅ QAT
Structured Pruning
Tutorial Included Features
Fully-Connected Model Pruning ✅ Pruning
Data Generation
Tutorial Included Features
Data-Free Quantization using Data Generation ✅ PTQ
✅ Data-Free Quantization
✅ Data Generation
Export Quantized Models
Tutorial Included Features
Exporter Usage ✅ Export