quantization-aware-training

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Neural Network Compression Framework for enhanced OpenVINO™ inference

YOLO ModelCompression MultidatasetTraining

Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.

This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.

FrostNet: Towards Quantization-Aware Network Architecture Search

针对pytorch模型的自动化模型结构分析和修改工具集，包含自动分析模型结构的模型压缩算法库

OpenVINO Training Extensions Object Detection

3rd place solution for NeurIPS 2019 MicroNet challenge

Train neural networks with joint quantization and pruning on both weights and activations using any pytorch modules

Code for paper: 'Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware'

FakeQuantize with Learned Step Size(LSQ+) as Observer in PyTorch

A tutorial of model quantization using TensorFlow

Quantization-aware training with spiking neural networks

Image classification done with Mindspore technology

Submission name: QualcommAI-EfficientNet. MicroNet Challenge (NeurIPS 2019) submission - Qualcomm AI Research

all methods of pytorch quantization based on resnet50

Training neural nets with quantized weights on arbitrarily specified bit-depth

One Bit at a Time: Impact of Quantisation on Neural Machine Translation

Transformer quantization and binarization exploration

Quantization for Object Detection in Tensorflow 2.x

Our work implements novel L2-Norm gradient (L2Grad) and variance of the weight distrbution (VarianceNorm) regularizers for quantization-aware training such that the distribution of weights are more compatible with post-training quantization especially for low bit-widths. We provide a theoretical basis that directly relates L2-Grad with post quantization test accuracy through a first order Taylor Series expansion followed by the reduction to an adversary with an L2 budget, in which we apply the Cauchy-Schwarz inequality to provide the desired bounds. We empirically show that L2Grad and VarianceNorm can both match the performance of L1Grad and outperform it on certain bit-widths. We also show that a regularization scheme that combines L2Grad and VarianceNorm in a novel "regularization scheduling" methodology can give even better results in terms of post-quantization accuracy, tested on uniform and piecewise linear quantization.

CNN quantization

Experimental Adversarial Attack notebooks on CV models

A simple formula supports eight types of quantization

8 bit quantizated Transformer for neural machine translation.

Autoencoder model for FPGA implementation using hls4ml. Repository for Applied Electronics Project.