78 Open source projects that are alternatives of or similar to batchnorm-pruning

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

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

A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)

Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow

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

Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626

A curated list of awesome edge machine learning resources, including research papers, inference engines, challenges, books, meetups and others.

FasterAI: A repository for making smaller and faster models with the FastAI library.

PyTorch and Keras model training and layer saturation monitor

LSTM and QRNN Language Model Toolkit for PyTorch

Infrastructures™ for Machine Learning Training/Inference in Production.

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

A research library for pytorch-based neural network pruning, compression, and more.

mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！

implementation of factorization machine, support classification.

This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.

VGG16 architecture with BatchNorm

Filter Grafting for Deep Neural Networks(CVPR 2020)

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

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

Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent

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

A package to make do Network Slimming a little easier

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

A tour of different optimization algorithms in PyTorch.

Caffe for Sparse Convolutional Neural Network

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

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

PyTorch*-based Neural Network Compression Framework for enhanced OpenVINO™ inference

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

A pytorch pruning toolkit for structured neural network pruning and layer dependency maintaining.

[ICLR 2021] "GANs Can Play Lottery Too" by Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen

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

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

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

Prunable nn layers for pytorch.

PyTorch Model Compression

Group Fisher Pruning for Practical Network Compression(ICML2021)

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

Java based sample code for developing on Android. The demos in this repository are stored on separate branches. To navigate to a demo, please click branches.

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

Your local Flux surgeon

Repository to track the progress in model compression and acceleration

A sentiment classifier on mixed language (and mixed script) reviews in Tamil, Malayalam and English

reference pytorch code for named entity tagging

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.

A curated list of neural network pruning resources.

(CVPR 2021, Oral) Dynamic Slimmable Network

Keras model convolutional filter pruning package

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

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

Distributed Fieldaware Factorization Machines based on Parameter Server

Embedded and mobile deep learning research resources

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

TransE方法的Python实现，解释SGD中TransE的向量更新

This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"

Remove/Backup – settings & cli for macOS (OS X) – DataGrip, AppCode, CLion, Gogland, IntelliJ, PhpStorm, PyCharm, Rider, RubyMine, WebStorm

Implementation of key concepts of neuralnetwork via numpy

Implement of Dynamic Model Pruning with Feedback with pytorch

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".