A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

An Open-Source Library for Training Binarized Neural Networks

QKeras: a quantization deep learning library for Tensorflow Keras

Generate a quantization parameter file for ncnn framework int8 inference

The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment

Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

[CVPR'20] ZeroQ: A Novel Zero Shot Quantization Framework

QONNX: Arbitrary-Precision Quantized Neural Networks in ONNX

Some recent Quantizing techniques on PyTorch

BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

This repository containts the pytorch scripts to train mixed-precision networks for microcontroller deployment, based on the memory contraints of the target device.

[NeurIPS 2023 Spotlight] This project is the official implementation of our accepted NeurIPS 2023 (spotlight) paper QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution.

Low Precision(quantized) Yolov5

Binary neural networks developed by Huawei Noah's Ark Lab

a CSK serial based train tools， rely on pytorch

Contains code for Binary, Ternary, N-bit Quantized and Hybrid CNNs for low precision experiments.

Input scripts for securely evaluating quantized ImageNet models with mp-spdz

Code implementation of our AISTATS'21 paper "Mirror Descent View for Neural Network Quantization"

Efficient Neural Architecture Search coupled with Quantized CNNs to search for resource efficient and accurate architectures.

Modeling stuck-at faults for RRAM inference on popular neural networks after quantization