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| |
| # MXNet Examples |
| |
| This page contains a curated list of awesome MXNet examples, tutorials and blogs. It is inspired by [awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning). See also [Awesome-MXNet](https://github.com/chinakook/Awesome-MXNet) for a similar list. |
| |
| - [Contributing](#contributing) |
| - [List of examples](#list-of-examples) |
| - [Languages Binding Examples](#language-binding-examples) |
| - [Deep Learning Examples in the MXNet Project Repository](#deep-learning-examples-mxnet) |
| - [Other Deep Learning Examples with MXNet](#deep-learning-examples-other) |
| - [IPython Notebooks](#ipython-notebooks) |
| - [Mobile App Examples](#mobile-apps-examples) |
| - [Web Predictive Services](#web-predictive-services) |
| - [List of tutorials](#list-of-tutorials) |
| - [GPU Technology Conference 2016 Hands-on session](#gtc2016-hands-on) |
| - [Deep learning for hackers with MXnet](#deep-learning-for-hackers) |
| - [MXnet setup on AWS](#mxnet-aws) |
| - [Kaggle tutorials](#kaggle-tutorials) |
| - [Learning Note](#learning-note) |
| - [Machine Learning Challenge Winning Solutions](#winning-solutions) |
| - [Tools with MXnet](#tools-with-mxnet) |
| |
| ## <a name="Contributing"></a>Contributing |
| |
| If you want to contribute to this list and the examples, please open a new pull request. |
| |
| |
| ### Examples |
| |
| Example applications or scripts should be submitted in this `example` folder. |
| |
| |
| ### Tutorials |
| |
| If you have a tutorial idea for the website, download the [Jupyter notebook tutorial template](https://github.com/apache/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb). |
| |
| #### Tutorial location |
| |
| Notebook tutorials should be submitted in the `docs/tutorials` folder, so that they maybe rendered in the [web site's tutorial section](https://mxnet.apache.org/tutorials/index.html). |
| |
| Do not forget to udpdate the `docs/tutorials/index.md` for your tutorial to show up on the website. |
| |
| #### Tutorial formatting |
| |
| The site expects the format to be markdown, so export your notebook as a .md via the Jupyter web interface menu (File > Download As > Markdown). Then, to enable the download notebook button in the web site's UI ([example](https://mxnet.apache.org/tutorials/python/linear-regression.html)), add the following as the last line of the file ([example](https://github.com/apache/mxnet/blame/master/docs/tutorials/python/linear-regression.md#L194)): |
| |
| ``` |
| <!-- INSERT SOURCE DOWNLOAD BUTTONS --> |
| ``` |
| |
| If you want some lines to show-up in the markdown but not in the generated notebooks, add this comment `<!--notebook-skip-line-->` after your ``. Like this: |
| |
| ``` |
| <!--notebook-skip-line--> |
| ``` |
| |
| Typically when you have a `plt.imshow()` you want the image tag `[png](img.png)` in the `.md` but not in the downloaded notebook as the user will re-generate the plot at run-time. |
| |
| #### Tutorial tests |
| |
| As part of making sure all our tutorials are running correctly with the latest version of MXNet, each tutorial is run automatically through a python2 and python3 jupyter notebook kernel in the CI, in a GPU environment, checking for errors and warnings. |
| |
| Add your own test here `tests/tutorials/test_tutorials.py`. (If you forget, don't worry your PR will not pass the sanity check). |
| |
| If your tutorial depends on specific packages, simply add them to this provisioning script: `ci/docker/install/ubuntu_tutorials.sh` |
| |
| ## <a name="list-of-examples"></a>List of examples |
| |
| ### <a name="language-binding-examples"></a>Languages Binding Examples |
| ------------------ |
| * [MXNet C++ API](https://mxnet.apache.org/api/c++/index.html) |
| - [C++ examples](https://github.com/apache/mxnet/tree/master/example/image-classification/predict-cpp) - Example code for using C++ interface, including NDArray, symbolic layer and models. |
| * [MXNet Python API](https://mxnet.apache.org/api/python/index.html) |
| * [MXNet Java API](https://mxnet.apache.org/api/java/index.html) |
| * [MXNet Scala API](https://mxnet.apache.org/api/scala/index.html) |
| * [MXNet R API](https://mxnet.apache.org/api/r/index.html) |
| * [MXNet Julia API](https://mxnet.apache.org/api/julia/index.html) |
| * [MXNet Perl API](https://mxnet.apache.org/api/perl/index.html) |
| * [go-mxnet-predictor](https://github.com/songtianyi/go-mxnet-predictor) - Go binding for inference |
| * [MXNet Javascript](https://github.com/dmlc/mxnet.js/) - MXNetJS: Javascript Package for Deep Learning in Browser (without server) |
| |
| ### <a name="deep-learning-examples-mxnet"></a>Deep Learning Examples in the MXNet Project Repository |
| -------------- |
| * [Autoencoder](autoencoder) - unsupervised feature learning |
| * [Gluon Examples](gluon) - several examples using the Gluon API |
| * [Style Transfer](gluon/style_transfer) - a style transfer example using gluon |
| * [Word Language Model](gluon/word_language_model) - an example that trains a multi-layer RNN on the Penn Treebank language modeling benchmark |
| * [SN-GAN](gluon/sn_gan) - an example that utilizes spectral normalization to train GAN(Generative adversarial network) using Gluon API |
| * [Image Classification with R](image-classification) - image classification on MNIST,CIFAR,ImageNet-1k,ImageNet-Full, with multiple GPU and distributed training. |
| * [Kaggle 2nd national data science bowl](kaggle-ndsb2) - a tutorial for Kaggle Second Nation Data Science Bowl |
| * [Multi-task Learning](multi-task) - how to use MXNet for multi-task learning |
| * [Profiling](profiler) - generate profiling results in json files |
| * [Quantization and Calibration Examples](quantization) - examples of quantizing a FP32 model to INT8 and performing low-precision inference with oneDNN on CPU or cuDNN on GPU |
| * [Recommender Systems](recommenders) - examples of how to build various kinds of recommender systems |
| * [Restricted Boltzmann Machine](restricted-boltzmann-machine) - an example of the binary restricted Boltzmann machine learning MNIST |
| * [Single Shot MultiBox Detector](ssd) - SSD object recognition example |
| |
| ### <a name="deep-learning-examples-other"></a>Other Deep Learning Examples with MXNet |
| |
| * [Face Recognition with ArcFace](https://github.com/onnx/models/tree/master/vision/body_analysis/arcface) - ONNX model for face recognition with notebooks for training, validating and running inference in MXNet by [abhinavs95](https://github.com/abhinavs95) |
| * [Chinese plate recognition](https://github.com/imistyrain/mxnet-mr) - Recognize Chinese vehicle plate, by [imistyrain](https://github.com/imistyrain) |
| * [Fast R-CNN](https://github.com/precedenceguo/mx-rcnn) by [Jian Guo](https://github.com/precedenceguo) |
| * "End2End Captcha Recognition (OCR)" by [xlvector](https://github.com/xlvector) [github link](https://github.com/xlvector/learning-dl/tree/master/mxnet/ocr) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/) |
| * "Prediction step of xlvector's lstm ocr" by [melody-rain](https://github.com/melody-rain) [github link](https://github.com/melody-rain/mxnet/commit/46002e31fc34c746c01bcaa7ade999187068ad3c) [Blog in Chinese](https://zhuanlan.zhihu.com/p/22698511) |
| * "Solving classification + regression with MXnet in Multi Input + Multi Obj" by [xlvector](https://github.com/xlvector) [github link](https://gist.github.com/xlvector/c304d74f9dd6a3b68a3387985482baac) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-regression-classification-for-concret-continuous-features/) |
| * "Learn to sort by LSTM" by [xlvector](https://github.com/xlvector) [github link](https://github.com/xlvector/learning-dl/tree/master/mxnet/lstm_sort) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-lstm-example/) |
| * [Neural Art using extremely lightweight (<500K) neural network](https://github.com/pavelgonchar/neural-art-mini) Lightweight version of mxnet neural art implementation by [Pavel Gonchar](https://github.com/pavelgonchar) |
| * [Neural Art with generative networks](https://github.com/zhaw/neural_style) by [zhaw](https://github.com/zhaw) |
| * [Faster R-CNN in MXNet with distributed implementation and data parallelization](https://github.com/apache/mxnet/tree/master/example/rcnn) |
| * [Asynchronous Methods for Deep Reinforcement Learning in MXNet](https://github.com/zmonoid/Asyn-RL-MXNet/blob/master/mx_asyn.py) by [zmonoid](https://github.com/zmonoid) |
| * [Deep Q-learning in MXNet](https://github.com/zmonoid/DQN-MXNet) by [zmonoid](https://github.com/zmonoid) |
| * [Face Detection with End-to-End Integration of a ConvNet and a 3D Model (ECCV16)](https://github.com/tfwu/FaceDetection-ConvNet-3D) by [tfwu](https://github.com/tfwu), source code for paper Yunzhu Li, Benyuan Sun, Tianfu Wu and Yizhou Wang, "Face Detection with End-to-End Integration of a ConvNet and a 3D Model", ECCV 2016 <https://arxiv.org/abs/1606.00850> |
| * [End-to-End Chinese plate recognition base on MXNet](https://github.com/szad670401/end-to-end-for-chinese-plate-recognition) by [szad670401](https://github.com/szad670401) |
| * [Reproduce ResNet-v2 (Identity Mappings in Deep Residual Networks) using MXNet](https://github.com/tornadomeet/ResNet) by [tornadomeet](https://github.com/tornadomeet) |
| * [Learning similarity among images in MXNet](http://www.jianshu.com/p/70a66c8f73d3) by xlvector in Chinese. Github [link](https://github.com/xlvector/learning-dl/tree/master/mxnet/triple-loss) |
| * [Matrix decomposition (SVD) with MXNet](http://www.jianshu.com/p/ebf7bf53ed3e) by xlvector in Chinese. Github [link](https://github.com/xlvector/mxnet/blob/svd/example/svd/svd.py) |
| * [MultiGPU enabled image generative models (GAN and DCGAN)](https://github.com/tqchen/mxnet-gan) by [Tianqi Chen](https://github.com/tqchen) |
| * [Deep reinforcement learning for playing flappybird by mxnet](https://github.com/li-haoran/DRL-FlappyBird) by LIHaoran |
| * [Neural Style in Markov Random Field (MRF) and Perceptual Losses Realtime transfer](https://github.com/zhaw/neural_style) by [zhaw](https://github.com/zhaw) |
| * [MTCNN Face keypoints detection and alignment](https://github.com/YYuanAnyVision/mxnet_mtcnn_face_detection) by [yuanyang](https://github.com/YYuanAnyVision), source code for [paper](https://kpzhang93.github.io/papers/spl.pdf) "Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks", [Kaipeng Zhang](https://github.com/kpzhang93), Zhanpeng Zhang, Zhifeng Li and Yu Qiao, IEEE Signal Processing Letters, 23(10), 2016 |
| * [SSD: Single Shot MultiBox Object Detector](https://github.com/zhreshold/mxnet-ssd) by [zhreshold](https://github.com/zhreshold) |
| * [Fast Neural Style in Scala](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/FastNeuralStyle) by [Ldpe2G](https://github.com/Ldpe2G) |
| * [LSTM Human Activity Recognition](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/HumanActivityRecognition) by [Ldpe2G](https://github.com/Ldpe2G) |
| * [Visual Question Answering](https://github.com/liuzhi136/Visual-Question-Answering) by [liuzhi136](https://github.com/liuzhi136) |
| * [Deformable ConvNets](https://arxiv.org/abs/1703.06211) ([github](https://github.com/msracver/Deformable-ConvNets)) by [MSRACVer](https://github.com/msracver) |
| * [OCR with bi-LSTM and CTC Loss in Gluon](https://github.com/ThomasDelteil/Gluon_OCR_LSTM_CTC) by [ThomasDelteil](https://github.com/ThomasDelteil) |
| * [Visual Search with Gluon and HNSWlib](https://github.com/ThomasDelteil/VisualSearch_MXNet), by [ThomasDelteil](https://github.com/ThomasDelteil), online demo [here](https://thomasdelteil.github.io/VisualSearch_MXNet/) |
| * [MXNet-face](https://github.com/tornadomeet/mxnet-face) - Using MXNet for a face-related algorithm by [tornadomeet](https://github.com/tornadomeet) where the single model gets 97.13%+-0.88% accuracy on LFW, and with only 20MB size |
| |
| ### <a name="ipython-notebooks"></a>IPython Notebooks |
| ----------------- |
| * [Predict with Pre-trained model](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/predict-with-pretrained-model.ipynb) - Notebook on how to predict with pretrained model. |
| * [composite symbol](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/composite_symbol.ipynb) - A demo of how to composite a symbolic Inception-BatchNorm Network |
| * [cifar-10 recipe](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/cifar10-recipe.ipynb) - A step by step demo of how to use MXNet |
| * [cifar-100](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/cifar-100.ipynb) - A demo of how to train a 75.68% accuracy CIFAR-100 model |
| * [simple bind](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/simple_bind.ipynb) - A demo of low level training API. |
| * [Multi task tutorial](https://github.com/haria/mxnet-multi-task-example/blob/master/multi-task.ipynb) - A demo of how to train and predict multi-task network on both MNIST and your own dataset. |
| * [class active maps](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/class_active_maps.ipynb) - A demo of how to localize the discriminative regions in an image using global average pooling (GAP) in CNNs. |
| * [DMLC MXNet Notebooks](https://github.com/dmlc/mxnet-notebooks) DMLC's repo for various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications. |
| * [AWS Seoul Summit 2017 Demos](https://github.com/sxjscience/aws-summit-2017-seoul) The demo codes and ipython notebooks in AWS Seoul Summit 2017. |
| * [Character-level CNN for text classification](https://github.com/ThomasDelteil/CNN_NLP_MXNet) Performing category classification on Amazon reviews using Gluon and character-level Convolutional Neural Networks. Online demo [here](https://thomasdelteil.github.io/CNN_NLP_MXNet/) |
| |
| ### <a name="mobile-apps-examples"></a>Mobile App Examples |
| ------------------- |
| * [MXNet Android Classification App](https://github.com/Leliana/WhatsThis) - Image classification on Android with MXNet. |
| * [MXNet iOS Classification App](https://github.com/pppoe/WhatsThis-iOS) - Image classification on iOS with MXNet. |
| * [Compile MXnet on Xcode (in Chinese)](http://www.liuxiao.org/2015/12/ios-mxnet-%E7%9A%84-ios-%E7%89%88%E6%9C%AC%E7%BC%96%E8%AF%91/) - a step-by-step tutorial of compiling MXnet on Xcode for iOS app |
| |
| ### <a name="web-predictive-services"></a>Web Predictive Services |
| ----------------------- |
| * [MXNet Shinny](https://github.com/thirdwing/mxnet_shiny) - Source code for quickly creating a Shiny R app to host online image classification. |
| * [Machine Eye](http://rupeshs.github.io/machineye/) - Web service for local image file/image URL classification without uploading. |
| |
| ## <a name="list-of-tutorials"></a>List of tutorials |
| |
| ### <a name="gtc2016-hands-on"></a>GPU Technology Conference 2016 Hands-on session |
| |
| * [Video on GTC 2016 site](http://on-demand.gputechconf.com/gtc/2016/video/L6143.html) |
| * [Video backup in Mainland China](http://pan.baidu.com/s/1eS58Gue) |
| * [iPython Notebook](https://github.com/dmlc/mxnet-gtc-tutorial) |
| |
| ### <a name="deep-learning-for-hackers"></a>Deep learning for hackers with MXNet |
| |
| * Deep learning for hackers with MXNet (1) GPU installation and MNIST [English](https://no2147483647.wordpress.com/2015/12/07/deep-learning-for-hackers-with-mxnet-1/) [Chinese](http://phunter.farbox.com/post/mxnet-tutorial1) - a tutorial of installing MXnet with GPU and introduction to deep learning by MNIST example. |
| * Deep learning for hackers with MXNet (2): Neural art [English](https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/) [Chinese](http://phunter.farbox.com/post/mxnet-tutorial2) - a tutorial of generating Van Gogh style cat paintings. |
| |
| ### <a name="mxnet-aws"></a>MXNet on the cloud |
| * [Setup Amazon AWS GPU instance with MXnet](https://no2147483647.wordpress.com/2016/01/16/setup-amazon-aws-gpu-instance-with-mxnet/) - AWS GPU instance setup with GPU (CUDA with latest cuDNN and S3 support) |
| * [Intro Guide to AWS (MXNet with Julia)](http://www.datasciencebowl.com/aws_guide/) - A step-by-step guide of using spot instances with Amazon Web Services (AWS) to help you save money when training DSB models on MXNet by [Mike Kim](http://www.datasciencebowl.com/author/mikekim/) |
| * [Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server](https://blogs.technet.microsoft.com/machinelearning/2016/09/15/building-deep-neural-networks-in-the-cloud-with-azure-gpu-vms-mxnet-and-microsoft-r-server/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Applying Deep Learning at Cloud Scale, with Microsoft R Server & Azure Data Lake](https://blogs.technet.microsoft.com/machinelearning/2016/10/31/applying-cloud-deep-learning-at-scale-with-microsoft-r-server-azure-data-lake/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Training Deep Neural Neural Networks on ImageNet Using Microsoft R Server and Azure GPU VMs](https://blogs.technet.microsoft.com/machinelearning/2016/11/15/imagenet-deep-neural-network-training-using-microsoft-r-server-and-azure-gpu-vms/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Cloud-Scale Text Classification with Convolutional Neural Networks on Microsoft Azure](https://blogs.technet.microsoft.com/machinelearning/2017/02/13/cloud-scale-text-classification-with-convolutional-neural-networks-on-microsoft-azure/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Distributed Deep Learning Made Easy](https://aws.amazon.com/blogs/compute/distributed-deep-learning-made-easy/) at AWS/Amazon for deploying deep learning clusters using MXNet |
| |
| ### <a name="kaggle-tutorials"></a>Kaggle tutorials |
| * [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (Python)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18079/end-to-end-deep-learning-tutorial-0-0392) - an end-to-end python tutorial for Kaggle heart disease diagnose competition (public leaderboard score 0.0392) |
| * [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (R)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18122/deep-learning-model-in-r) - an end-to-end R tutorial for Kaggle heart disease diagnose competition |
| * [Dogs vs. Cats classification with mxnet and R](https://statist-bhfz.github.io/cats_dogs_finetune) - end-to-end (not winning) tutorial with an example of fine-tuning in R |
| |
| ### <a name="learning-note"></a>Learning Note |
| * [Learning Note in Chinese](https://github.com/zhubuntu/MXNet-Learning-Note) - MXNet learning note in Chinese. |
| * [Getting Started with MXNet](https://indico.io/blog/getting-started-with-mxnet/) by [indico.io](https://indico.io) (Chinese Translation [MXNet实践](http://www.infoq.com/cn/articles/practise-of-mxnet) by [侠天](http://www.infoq.com/cn/author/%E4%BE%A0%E5%A4%A9) ) |
| * [{mxnet} R package from MXnet, an intuitive Deep Learning framework including CNN & RNN](http://tjo-en.hatenablog.com/entry/2016/03/30/233848) by [TJO](http://tjo-en.hatenablog.com/) |
| * [MXnet with R: combined power of deep learning](http://cos.name/2016/04/mxnet-r/) in Chinese by Tong He |
| * [Understand MXNet dependency engine](http://yuyang0.github.io/articles/mxnet-engine.html) in Chinese by [Yu Yang](https://github.com/yuyang0) |
| |
| ## <a name="winning-solutions"></a>Machine Learning Challenge Winning Solutions |
| |
| * Dmitrii Tsybulevskii, 1st place of the [Yelp Restaurant Photo Classification](https://www.kaggle.com/c/yelp-restaurant-photo-classification). Link to [the Kaggle interview](http://blog.kaggle.com/2016/04/28/yelp-restaurant-photo-classification-winners-interview-1st-place-dmitrii-tsybulevskii/). |
| |
| ## <a name="tools-with-mxnet"></a>Tools with MXnet |
| * [TensorFuse](https://github.com/dementrock/tensorfuse) - Common interface for Theano, CGT, TensorFlow, and mxnet (experimental) by [dementrock](https://github.com/dementrock) |
| * [MXnet-face](https://github.com/tornadomeet/mxnet-face) - Using mxnet for face-related algorithm by [tornadomeet](https://github.com/tornadomeet) where the single model get 97.13%+-0.88% accuracy on LFW, and with only 20MB size. |
| * [MinPy](https://github.com/dmlc/minpy) - Pure numpy practice with third party operator Integration and MXnet as backend for GPU computing |
| * [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) - a flexible and easy to use tool for serving Deep Learning models |
