model-compression

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

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

CV backbones including GhostNet, TinyNet and TNT.

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

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

A curated list of neural network pruning resources.

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

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

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

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.

Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.

Pytorch implementation of various Knowledge Distillation (KD) methods.

[CVPR2020] GhostNet: More Features from Cheap Operations

knowledge distillation papers

Data Efficient Model Compression

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Papers for deep neural network compression and acceleration

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Pruning channels for model acceleration

Infrastructures™ for Machine Learning Training/Inference in Production.

⛵️The official PyTorch implementation for "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing" (EMNLP 2020).

Java interface for fastText

Awesome machine learning model compression research papers, tools, and learning material.

Deep Face Model Compression

Code for "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019), "SCOP: Scientific Control for Reliable Neural Network Pruning" (NeurIPS 2020) and “Manifold Regularized Dynamic Network Pruning” (CVPR 2021).