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

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

model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for quantization)

A curated list of neural network pruning resources.

Embedded and mobile deep learning research resources

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

Pruning and other network surgery for trained Keras models.

yolov3 network slimming剪枝的一种实现

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

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Caffe for Sparse Convolutional Neural Network

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

Infrastructures™ for Machine Learning Training/Inference in Production.

YOLOv3 ModelCompression MultidatasetTraining

Reference ImageNet implementation of SelecSLS CNN architecture proposed in "XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera". Also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods.

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

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.

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

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

I demonstrate how to compress a neural network using pruning in tensorflow.

Filter Grafting for Deep Neural Networks(CVPR 2020)

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

Neural Network Compression Framework for PyTorch*

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

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

Prunable nn layers for pytorch.

An implementation of Channel Pruning on face recognition algorithm Sphereface.

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