Top 66 pruning open source projects

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".

Caffe for Sparse Convolutional Neural Network

Infrastructures™ for Machine Learning Training/Inference in Production.

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

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

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

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

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.

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

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.

PyTorch Model Compression

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

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

Filter Grafting for Deep Neural Networks(CVPR 2020)

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

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

Repository to track the progress in model compression and acceleration

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

reference pytorch code for named entity tagging

PyTorch and Keras model training and layer saturation monitor

A curated list of neural network pruning resources.

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

Keras model convolutional filter pruning package

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

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

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

Embedded and mobile deep learning research resources

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

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.

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

yolov3 network slimming剪枝的一种实现

YOLO ModelCompression MultidatasetTraining

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

One Person, One Model, One World: Learning Continual User Representation without Forgetting

A PyTorch-based model pruning toolkit for pre-trained language models

Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint

[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

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

Pruning and quantization for SSD. Model compression.

🔌⚡ Nuxt module to prune html before sending it to the browser (it removes elements matching CSS selector(s)), useful for boosting performance showing a different HTML for bots/audits by removing all the scripts with dynamic rendering

Attempting feature map prunning on a CNN trained for Japanese OCR

Unofficial Pytorch implementation of Deep Compression in CIFAR10

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

OpenMMLab Model Compression Toolbox and Benchmark.

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers https://arxiv.org/abs/1802.00124

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"

Your local Flux surgeon

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

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

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

Group Fisher Pruning for Practical Network Compression(ICML2021)

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

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 package to make do Network Slimming a little easier

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

Implement of Dynamic Model Pruning with Feedback with pytorch

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