mobilefacenet-mxnet - 基于insightface训练mobilefacenet的相关步骤及ncnn转换流程

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insightface - Face Recognition Project on MXNet

  •    Python

2018.03.14: a new is now more clear after removing experimental code. All experimental and unstable test will be put on and 2018.02.16: We put the MegaFace noise list in this repo. Please refer to [] for detail.

ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform

  •    C

ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployment and uses on mobile phones from the beginning of design. ncnn does not have third party dependencies. it is cross-platform, and runs faster than all known open source frameworks on mobile phone cpu. Developers can easily deploy deep learning algorithm models to the mobile platform by using efficient ncnn implementation, create intelligent APPs, and bring the artificial intelligence to your fingertips. ncnn is currently being used in many Tencent applications, such as QQ, Qzone, WeChat, Pitu and so on.

MXNet - A Deep Learning Framework

  •    C++

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It is highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. MXNet allows you to mix symbolic and imperative programming flavors to maximize both efficiency and productivity.

incubator-mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

  •    C++

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

deep3d - Automatic 2D-to-3D Video Conversion with CNNs

  •    Jupyter

To run this code. Please install MXNet following the official document. Deep3D requires MXNet to be built with Cuda 7.0 and Cudnn 4 or above. Please open mxnet/ and set USE_CUDA and USE_CUDNN to 1. Then, append EXTRA_OPERATORS=path/to/deep3d/operators to path/to/mxnet/ and recompile MXNet. Since the debut of Avatar in 2008, 3D movies has rapidly developed into mainstream technology. Roughly 10 to 20 3D movies are produced each year and the launch of Oculus Rift and other VR head set is only going to drive up the demand.

XLearning - AI on Hadoop

  •    Java

XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.

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  •    Jupyter

This repo contains various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications.The python notebooks are written in Jupyter.

mxnet-the-straight-dope - An interactive book on deep learning. Much easy, so MXNet. Wow.

  •    Jupyter

This repo contains an incremental sequence of notebooks designed to teach deep learning, MXNet, and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code. To our knowledge there's no source out there that teaches either (1) the full breadth of concepts in modern deep learning or (2) interleaves an engaging textbook with runnable code. We'll find out by the end of this venture whether or not that void exists for a good reason. Another unique aspect of this book is its authorship process. We are developing this resource fully in the public view and are making it available for free in its entirety. While the book has a few primary authors to set the tone and shape the content, we welcome contributions from the community and hope to coauthor chapters and entire sections with experts and community members. Already we've received contributions spanning typo corrections through full working examples.

Awesome-MXNet - A curated list of MXNet examples, tutorials and blogs.


A curated list of MXNet examples, tutorials and blogs. If you want to contribute to this list and the examples, please open a new pull request.

cxxnet - move forward to

  •    C++

Thanks for using and supporting cxxnet. Today, we finally make a hard but exciting decision: we decide to deprecate cxxnet and fully move forward to next generation toolkit MXNet.Please check the feature highlights, speed/memory comparation and examples in MXNet.

mxnet.js - MXNetJS: Javascript Package for Deep Learning in Browser (without server)

  •    Javascript

MXNetJS is the Apache MXNet Javascript package. MXNetJS brings state of art deep learning inference API to the browser. It is generated with Emscripten and MXNet Amalgamation. MXNetJS allows you to run prediction of state-of-art deep learning models in any computational graph, and brings the fun of deep learning to the client side.See classify_image.js for how it works.

sockeye - Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

  •    Python

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post (2017): Sockeye: A Toolkit for Neural Machine Translation. In eprint arXiv:cs-CL/1712.05690.If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

  •    Python

Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

cvpr17 - CVPR 2017 Tutorial


MXNet is widely used in production environments owing to its strong reputation for speed. Now with gluon, MXNet’s new imperative interface, doing research in MXNet is easy. In this tutorial, we will walk through how to use gluon to implement various algorithms. We will present every concept in details, no deep learning background is required to attend. We encourage the audience to bring their laptops to have a hands-on experience with gluon.

neural-art-mini - Lightweight version of mxnet neural art implementation

  •    Python

Lightweight version of mxnet neural art implementation

mx-maskrcnn - An MXNet implementation of Mask R-CNN

  •    Python

An MXNet implementation of Mask R-CNN. This repository is based largely on the mx-rcnn implementation of Faster RCNN available here.

mxnet-ssd - MXNet port of SSD: Single Shot MultiBox Object Detector

  •    Python

SSD is an unified framework for object detection with a single network. You can use the code to train/evaluate/test for object detection task.

MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks

  •    Python

A comprehensive, cross-framework solution to convert, visualize and diagnosis deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.Across the industry and academia, there are a number of existing frameworks available for developers and researchers to design a model, where each framework has its own network structure definition and saving model format. The gaps between frameworks impede the inter-operation of the models.

MXNet.jl - MXNet Julia Package - flexible and efficient deep learning in Julia

  •    Julia

For more details, please refer to the documentation and examples.