111 Open source projects that are alternatives of or similar to Torch Pruning

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

(CVPR 2021, Oral) Dynamic Slimmable Network

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

Infrastructures™ for Machine Learning Training/Inference in Production.

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

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

A curated list of neural network pruning resources.

A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.

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

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

[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH

Programmable Neural Network Compression

PyTorch and Keras model training and layer saturation monitor

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

Code for "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019) and "SCOP: Scientific Control for Reliable Neural Network Pruning" (NeurIPS 2020).

MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images

Compressing Representations for Self-Supervised Learning

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

[NeurIPS 2020] Code release for paper "Deep Multimodal Fusion by Channel Exchanging" (In PyTorch)

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

Java interface for fastText

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

Filter Grafting for Deep Neural Networks(CVPR 2020)

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.

Model Compression Based on Geoffery Hinton's Logit Regression Method in Keras applied to MNIST 16x compression over 0.95 percent accuracy.An Implementation of "Distilling the Knowledge in a Neural Network - Geoffery Hinton et. al"

PyTorch code for our CVPR'20 paper "Collaborative Distillation for Ultra-Resolution Universal Style Transfer"

reference pytorch code for named entity tagging

Model Compression CLI Tool for Keras.

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

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

papers about model compression

Keras model convolutional filter pruning package

PyTorch Implementation of Weights Pruning

Pruning and other network surgery for trained TF.Keras models.

An Open Source Deep Learning Inference Engine Based on FPGA

Embedded and mobile deep learning research resources

CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

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.

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

Pytorch implementation of various Knowledge Distillation (KD) methods.

PyTorch Model Compression

[CVPR2020] GhostNet: More Features from Cheap Operations

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

knowledge distillation papers

Data Efficient Model Compression

Pytorch implementation of our CVPR 2020 (Oral) -- HRank: Filter Pruning using High-Rank Feature Map

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

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

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

Pruning and other network surgery for trained Keras models.

yolov3 network slimming剪枝的一种实现

yolov3 by pytorch

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

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