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

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

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.

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

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

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

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.

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".

YOLO ModelCompression MultidatasetTraining

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.

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

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

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

Filter Grafting for Deep Neural Networks(CVPR 2020)

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

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

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

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

PyTorch and Keras model training and layer saturation monitor

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

Prunable nn layers for pytorch.