This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.
[CVPR 2024 Highlight & TPAMI 2025] This is the official PyTorch implementation of "TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models".
#计算机科学#Notes on quantization in neural networks
Post-training static quantization using ResNet18 architecture
Post-training quantization on Nvidia Nemo ASR model
Introductory Guide where we will talk about Different Techniques of Fine Tuning LLMs
Comprehensive study on the quantization of various CNN models, employing techniques such as Post-Training Quantization and Quantization Aware Training (QAT).
A framework to train a ResUNet architecture, quantize, compile and execute it on an FPGA.
EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.
Post-Training quantization perfomed on the model trained with CLIC dataset.