quantization

Lossy PNG compressor — pngquant command based on libimagequant library

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

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

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

Trainable models and NN optimization tools

PaddleSlim is an open-source library for deep model compression and architecture search.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Brevitas: quantization-aware training in PyTorch

Embedded and mobile deep learning research resources

A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

Palette quantization library that powers pngquant and other PNG optimizers

An Open-Source Package for Deep Learning to Hash (DeepHash)

Neural network inference engine that delivers GPU-class performance for sparsified models on CPUs

Neural Network Compression Framework for enhanced OpenVINO™ inference

🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

PyTorch Project Specification.

QKeras: a quantization deep learning library for Tensorflow Keras

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.

Must-read papers on deep learning to hash (DeepHash)

[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision

Infrastructures™ for Machine Learning Training/Inference in Production.

⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.

Fast inference engine for Transformer models

Bring Deep Learning to small devices