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https://github.com/eric-erki/awesome-pytorch

Awesome PyTorch
https://github.com/eric-erki/awesome-pytorch

List: awesome-pytorch

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Awesome PyTorch

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# Awesome PyTorch



## Paper Implementations



- [A PyTorch Implementation of DenseNet](https://github.com/bamos/densenet.pytorch): Densely Connected Convolutional Networks, [1608.06993](https://arxiv.org/abs/1608.06993)

- [attention-is-all-you-need-pytorch](https://github.com/jadore801120/attention-is-all-you-need-pytorch): Attention Is All You Need, [1706.03762](https://arxiv.org/abs/1706.03762)

- [Attention Transfer](https://github.com/szagoruyko/attention-transfer): Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, [1612.03928](https://arxiv.org/abs/1612.03928)

- [BEGAN in PyTorch](https://github.com/carpedm20/BEGAN-pytorch): BEGAN: Boundary Equilibrium Generative Adversarial Networks, [1703.10717](https://arxiv.org/abs/1703.10717)

- [CycleGAN and pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix): CycleGAN and pix2pix in PyTorch, [1703.10593](https://arxiv.org/abs/1703.10593), [1611.07004](https://arxiv.org/abs/1611.07004)

- [Deformable Convolution](https://github.com/oeway/pytorch-deform-conv): Deformable Convolution, [1703.06211](http://arxiv.org/abs/1703.06211)

- [DiscoGAN(1)](https://github.com/SKTBrain/DiscoGAN), [DiscoGAN(2)](https://github.com/carpedm20/DiscoGAN-pytorch): Learning to Discover Cross-Domain Relations with Generative Adversarial Networks, [1703.05192](https://arxiv.org/abs/1703.05192)

- [diracnets](https://github.com/szagoruyko/diracnets): DiracNets: Training Very Deep Neural Networks Without Skip-Connections, [1706.00388](https://arxiv.org/abs/1706.00388)

- [dragan-pytorch](https://github.com/jfsantos/dragan-pytorch), How to Train Your DRAGAN, [1705.07215](https://arxiv.org/abs/1705.07215)

- [Evolution Strategies](https://github.com/atgambardella/pytorch-es): Evolution Strategies as a Scalable Alternative to Reinforcement Learning ,[1703.03864](https://arxiv.org/abs/1703.03864)

- [Grad-CAM](https://github.com/jacobgil/pytorch-grad-cam): Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, [1610.02391](https://arxiv.org/abs/1610.02391)

- [Grammar Variational Autoencoder](https://github.com/episodeyang/grammar_variational_autoencoder): Grammar Variational Autoencoder, [1703.01925](https://arxiv.org/abs/1703.01925)

- [pytorch-mobilenet](https://github.com/marvis/pytorch-mobilenet): PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", [1704.04861](https://arxiv.org/abs/1704.04861)

- [neural-combinatorial-rl-pytorch](https://github.com/pemami4911/neural-combinatorial-rl-pytorch): Neural Combinatorial Optimization with Reinforcement Learning, [1611.09940](https://arxiv.org/abs/1611.09940)

- [Neural Message Passing](https://github.com/priba/nmp_qc): Neural Message Passing for Quantum Chemistry, [1704.01212](https://arxiv.org/abs/1704.01212)

- [ODIN: Out-of-Distribution Detector for Neural Networks](https://github.com/shiyuliang/odin-pytorch): Principled Detection of Out-of-Distribution Examples in Neural Networks, [1706.02690](https://arxiv.org/abs/1706.02690)

- [pytorch-fcn](https://github.com/wkentaro/pytorch-fcn): PyTorch Implementation of Fully Convolutional Networks, [1605.06211](https://arxiv.org/abs/1605.06211)

- [PyTorch-Style-Transfer](https://github.com/zhanghang1989/PyTorch-Style-Transfer): Multi-style Generative Network for Real-time Transfer, [1703.06953](https://arxiv.org/pdf/1703.06953.pdf) and Image Style Transfer Using Convolutional Neural Networks, [pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf)

- [PyScatWave](https://github.com/edouardoyallon/pyscatwave): Scaling the Scattering Transform, [1703.08961](https://arxiv.org/abs/1703.08961)

- [pytorch-pruning](https://github.com/jacobgil/pytorch-pruning): Pruning Convolutional Neural Networks for Resource Efficient Inference, [1611.06440](https://arxiv.org/abs/1611.06440)

- [relational-networks](https://github.com/kimhc6028/relational-networks): A simple neural network module for relational reasoning, [1706.01427](https://arxiv.org/abs/1706.01427)

- [Recurrent Variational Autoencoder](https://github.com/analvikingur/pytorch_RVAE): Recurrent Variational Autoencoder that generates sequential data implemented in pytorch, [1511.06349](https://arxiv.org/abs/1511.06349), [1508.06615](https://arxiv.org/abs/1508.06615)

- [TreeLSTM](https://gist.github.com/wolet/1b49c03968b2c83897a4a15c78980b18): Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks, [1503.00075](https://arxiv.org/abs/1503.00075)

- [VAE with a VampPrior](https://github.com/jmtomczak/vae_vampprior): Variational Mixture of Posteriors, [1705.07120](https://arxiv.org/abs/1705.07120)

- [Visual Question Answering in Pytorch](https://github.com/Cadene/vqa.pytorch): MUTAN: Multimodal Tucker Fusion for Visual Question Answering, [1705.06676](https://arxiv.org/abs/1705.06676)

- [Wasserstein GAN](https://github.com/martinarjovsky/WassersteinGAN): Wasserstein GAN, [1701.07875](https://arxiv.org/abs/1701.07875)

- [Weight Normalized GAN](https://github.com/stormraiser/GAN-weight-norm): On the Effects of Batch and Weight Normalization in Generative Adversarial Networks, [1704.03971](https://arxiv.org/abs/1704.03971)

- [YOLOv2 in PyTorch](https://github.com/longcw/yolo2-pytorch): PyTorch implementation of YOLOv2, [1612.08242](https://arxiv.org/abs/1612.08242)



## Tutorials & Examples

- [Pytorch-Project-Template](https://github.com/moemen95/PyTorch-Project-Template): A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning. 

- [Deep Learning for NLP with PyTorch](https://github.com/rguthrie3/DeepLearningForNLPInPytorch): An IPython Notebook tutorial on deep learning, with an emphasis on Natural Language Processing

- [Practical PyTorch](https://github.com/spro/practical-pytorch): Practical PyTorch tutorials, focused on using RNNs for NLP

- [Pytorch Examples](https://github.com/jcjohnson/pytorch-examples): This repository introduces the fundamental concepts of PyTorch through self-contained examples

- [PyTorch Mini Tutorials](https://github.com/vinhkhuc/PyTorch-Mini-Tutorials): Minimal tutorials for PyTorch adapted from Alec Radford's Theano tutorials.

- [Pytorch Official Examples](https://github.com/pytorch/examples): A repository showcasing examples of using pytorch

- [Pytorch Official Tutorials](https://github.com/pytorch/tutorials): PyTorch Tutorials

- [pytorch-playground](https://github.com/aaron-xichen/pytorch-playground): Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

- [Pytorch Tutorial](https://github.com/yunjey/pytorch-tutorial): PyTorch Tutorials. Most of the models were implemented with less than 30 lines of code.

- [PyTorch Tutorials](https://chsasank.github.io/pytorch-tutorials/index.html): PyTorch tutorials better rendered with readthedocs style

- [t-SNE experiments in pytorch](https://github.com/cemoody/topicsne): A simple t-SNE model implemented in pytorch

- [Welcome tutorials](https://github.com/mila-udem/welcome_tutorials/tree/master/pytorch): MILA's pytorch tutorials

- [image-classification-mobile](https://github.com/osmr/imgclsmob): Examples of reproducible training various classification models for ImageNet-1K



## Useful PyTorch Tools



- [PyTorch to Keras model converter](https://github.com/nerox8664/pytorch2keras)

- [Gluon to PyTorch model converter with code generation](https://github.com/nerox8664/gluon2pytorch)

- [MMdnn](https://github.com/Microsoft/MMdnn)



## Blog & Article



- [A Tour of PyTorch Internals](https://gist.github.com/killeent/4675635b40b61a45cac2f95a285ce3c0)

- [Adversarial Autoencoders (with Pytorch)](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/)

- [Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)](https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f)

- [Matrix Factorization in PyTorchn](http://blog.ethanrosenthal.com/2017/06/20/matrix-factorization-in-pytorch/)

- [Recursive Neural Networks with PyTorch](https://devblogs.nvidia.com/parallelforall/recursive-neural-networks-pytorch/)



## Discussion



- [PyTorch vs TensorFlow](https://www.reddit.com/r/MachineLearning/comments/5w3q74/d_so_pytorch_vs_tensorflow_whats_the_verdict_on/): A reddit post about PyTorch and TensorFlow