Awesome-Pytorch-list

Contents

• Pytorch & related libraries
  • NLP & Speech Processing
  • Computer Vision
  • Probabilistic/Generative Libraries
  • Other libraries
• Tutorials, books & examples
• Paper implementations
• Talks & Conferences
• Pytorch elsewhere

Pytorch & related libraries

1. pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration.
2. Captum: Model interpretability and understanding for PyTorch.

NLP & Speech Processing:

1. pytorch text: Torch text related contents.
2. pytorch-seq2seq: A framework for sequence-to-sequence (seq2seq) models implemented in PyTorch.
3. anuvada: Interpretable Models for NLP using PyTorch.
4. audio: simple audio I/O for pytorch.
5. loop: A method to generate speech across multiple speakers
6. fairseq-py: Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
7. speech: PyTorch ASR Implementation.
8. OpenNMT-py: Open-Source Neural Machine Translation in PyTorch http://opennmt.net
9. neuralcoref: State-of-the-art coreference resolution based on neural nets and spaCy huggingface.co/coref
10. sentiment-discovery: Unsupervised Language Modeling at scale for robust sentiment classification.
11. MUSE: A library for Multilingual Unsupervised or Supervised word Embeddings
12. nmtpytorch: Neural Machine Translation Framework in PyTorch.
13. pytorch-wavenet: An implementation of WaveNet with fast generation
14. Tacotron-pytorch: Tacotron: Towards End-to-End Speech Synthesis.
15. AllenNLP: An open-source NLP research library, built on PyTorch.
16. PyTorch-NLP: Text utilities and datasets for PyTorch pytorchnlp.readthedocs.io
17. quick-nlp: Pytorch NLP library based on FastAI.
18. TTS: Deep learning for Text2Speech
19. LASER: Language-Agnostic SEntence Representations
20. pyannote-audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, speaker embedding
21. gensen: Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning.
22. translate: Translate - a PyTorch Language Library.
23. espnet: End-to-End Speech Processing Toolkit espnet.github.io/espnet
24. pythia: A software suite for Visual Question Answering
25. UnsupervisedMT: Phrase-Based & Neural Unsupervised Machine Translation.
26. jiant: The jiant sentence representation learning toolkit.
27. BERT-PyTorch: Pytorch implementation of Google AI's 2018 BERT, with simple annotation
28. InferSent: Sentence embeddings (InferSent) and training code for NLI.
29. uis-rnn:This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization. arxiv.org/abs/1810.04719
30. flair: A very simple framework for state-of-the-art Natural Language Processing (NLP)
31. pytext: A natural language modeling framework based on PyTorch fb.me/pytextdocs
32. voicefilter: Unofficial PyTorch implementation of Google AI's VoiceFilter system http://swpark.me/voicefilter
33. BERT-NER: Pytorch-Named-Entity-Recognition-with-BERT.
34. transfer-nlp: NLP library designed for flexible research and development
35. texar-pytorch: Toolkit for Machine Learning and Text Generation, in PyTorch texar.io
36. pytorch-kaldi: pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
37. NeMo: Neural Modules: a toolkit for conversational AI nvidia.github.io/NeMo
38. pytorch-struct: A library of vectorized implementations of core structured prediction algorithms (HMM, Dep Trees, CKY, ..,)
39. espresso: Espresso: A Fast End-to-End Neural Speech Recognition Toolkit
40. transformers: huggingface Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch. huggingface.co/transformers
41. reformer-pytorch: Reformer, the efficient Transformer, in Pytorch
42. torch-metrics: Metrics for model evaluation in pytorch
43. speechbrain: SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.
44. Backprop: Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

CV:

1. pytorch vision: Datasets, Transforms and Models specific to Computer Vision.
2. pt-styletransfer: Neural style transfer as a class in PyTorch.
3. OpenFacePytorch: PyTorch module to use OpenFace's nn4.small2.v1.t7 model
4. img_classification_pk_pytorch: Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)
5. SparseConvNet: Submanifold sparse convolutional networks.
6. Convolution_LSTM_pytorch: A multi-layer convolution LSTM module
7. face-alignment: :fire: 2D and 3D Face alignment library build using pytorch adrianbulat.com
8. pytorch-semantic-segmentation: PyTorch for Semantic Segmentation.
9. RoIAlign.pytorch: This is a PyTorch version of RoIAlign. This implementation is based on crop_and_resize and supports both forward and backward on CPU and GPU.
10. pytorch-cnn-finetune: Fine-tune pretrained Convolutional Neural Networks with PyTorch.
11. detectorch: Detectorch - detectron for PyTorch
12. Augmentor: Image augmentation library in Python for machine learning. http://augmentor.readthedocs.io
13. s2cnn: This library contains a PyTorch implementation of the SO(3) equivariant CNNs for spherical signals (e.g. omnidirectional cameras, signals on the globe)
14. TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.
15. maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
16. image-classification-mobile: Collection of classification models pretrained on the ImageNet-1K.
17. medicaltorch: A medical imaging framework for Pytorch http://medicaltorch.readthedocs.io
18. albumentations: Fast image augmentation library.
19. kornia: Differentiable computer vision library.
20. pytorch-text-recognition: Text recognition combo - CRAFT + CRNN.
21. facenet-pytorch: Pretrained Pytorch face detection and recognition models ported from davidsandberg/facenet.
22. detectron2: Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
23. vedaseg: A semantic segmentation framework by pyotrch
24. ClassyVision: An end-to-end PyTorch framework for image and video classification.
25. detecto:Computer vision in Python with less than 10 lines of code
26. pytorch3d: PyTorch3D is FAIR's library of reusable components for deep learning with 3D data pytorch3d.org
27. MMDetection: MMDetection is an open source object detection toolbox, a part of the OpenMMLab project.
28. neural-dream: A PyTorch implementation of the DeepDream algorithm. Creates dream-like hallucinogenic visuals.
29. FlashTorch: Visualization toolkit for neural networks in PyTorch!
30. Lucent: Tensorflow and OpenAI Clarity's Lucid adapted for PyTorch.
31. MMDetection3D: MMDetection3D is OpenMMLab's next-generation platform for general 3D object detection, a part of the OpenMMLab project.
32. MMSegmentation: MMSegmentation is a semantic segmentation toolbox and benchmark, a part of the OpenMMLab project.
33. MMEditing: MMEditing is a image and video editing toolbox, a part of the OpenMMLab project.
34. MMAction2: MMAction2 is OpenMMLab's next generation action understanding toolbox and benchmark, a part of the OpenMMLab project.
35. MMPose: MMPose is a pose estimation toolbox and benchmark, a part of the OpenMMLab project.
36. lightly - Lightly is a computer vision framework for self-supervised learning.
37. RoMa: a lightweight and efficient library to deal with 3D rotations.

Probabilistic/Generative Libraries:

1. ptstat: Probabilistic Programming and Statistical Inference in PyTorch
2. pyro: Deep universal probabilistic programming with Python and PyTorch http://pyro.ai
3. probtorch: Probabilistic Torch is library for deep generative models that extends PyTorch.
4. paysage: Unsupervised learning and generative models in python/pytorch.
5. pyvarinf: Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch.
6. pyprob: A PyTorch-based library for probabilistic programming and inference compilation.
7. mia: A library for running membership inference attacks against ML models.
8. pro_gan_pytorch: ProGAN package implemented as an extension of PyTorch nn.Module.
9. botorch: Bayesian optimization in PyTorch

Other libraries:

1. pytorch extras: Some extra features for pytorch.
2. functional zoo: PyTorch, unlike lua torch, has autograd in it's core, so using modular structure of torch.nn modules is not necessary, one can easily allocate needed Variables and write a function that utilizes them, which is sometimes more convenient. This repo contains model definitions in this functional way, with pretrained weights for some models.
3. torch-sampling: This package provides a set of transforms and data structures for sampling from in-memory or out-of-memory data.
4. torchcraft-py: Python wrapper for TorchCraft, a bridge between Torch and StarCraft for AI research.
5. aorun: Aorun intend to be a Keras with PyTorch as backend.
6. logger: A simple logger for experiments.
7. PyTorch-docset: PyTorch docset! use with Dash, Zeal, Velocity, or LovelyDocs.
8. convert_torch_to_pytorch: Convert torch t7 model to pytorch model and source.
9. pretrained-models.pytorch: The goal of this repo is to help to reproduce research papers results.
10. pytorch_fft: PyTorch wrapper for FFTs
11. caffe_to_torch_to_pytorch
12. pytorch-extension: This is a CUDA extension for PyTorch which computes the Hadamard product of two tensors.
13. tensorboard-pytorch: This module saves PyTorch tensors in tensorboard format for inspection. Currently supports scalar, image, audio, histogram features in tensorboard.
14. gpytorch: GPyTorch is a Gaussian Process library, implemented using PyTorch. It is designed for creating flexible and modular Gaussian Process models with ease, so that you don't have to be an expert to use GPs.
15. spotlight: Deep recommender models using PyTorch.
16. pytorch-cns: Compressed Network Search with PyTorch
17. pyinn: CuPy fused PyTorch neural networks ops
18. inferno: A utility library around PyTorch
19. pytorch-fitmodule: Super simple fit method for PyTorch modules
20. inferno-sklearn: A scikit-learn compatible neural network library that wraps pytorch.
21. pytorch-caffe-darknet-convert: convert between pytorch, caffe prototxt/weights and darknet cfg/weights
22. pytorch2caffe: Convert PyTorch model to Caffemodel
23. pytorch-tools: Tools for PyTorch
24. sru: Training RNNs as Fast as CNNs (arxiv.org/abs/1709.02755)
25. torch2coreml: Torch7 -> CoreML
26. PyTorch-Encoding: PyTorch Deep Texture Encoding Network http://hangzh.com/PyTorch-Encoding
27. pytorch-ctc: PyTorch-CTC is an implementation of CTC (Connectionist Temporal Classification) beam search decoding for PyTorch. C++ code borrowed liberally from TensorFlow with some improvements to increase flexibility.
28. candlegp: Gaussian Processes in Pytorch.
29. dpwa: Distributed Learning by Pair-Wise Averaging.
30. dni-pytorch: Decoupled Neural Interfaces using Synthetic Gradients for PyTorch.
31. skorch: A scikit-learn compatible neural network library that wraps pytorch
32. ignite: Ignite is a high-level library to help with training neural networks in PyTorch.
33. Arnold: Arnold - DOOM Agent
34. pytorch-mcn: Convert models from MatConvNet to PyTorch
35. simple-faster-rcnn-pytorch: A simplified implemention of Faster R-CNN with competitive performance.
36. generative_zoo: generative_zoo is a repository that provides working implementations of some generative models in PyTorch.
37. pytorchviz: A small package to create visualizations of PyTorch execution graphs.
38. cogitare: Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework in Python.
39. pydlt: PyTorch based Deep Learning Toolbox
40. semi-supervised-pytorch: Implementations of different VAE-based semi-supervised and generative models in PyTorch.
41. pytorch_cluster: PyTorch Extension Library of Optimised Graph Cluster Algorithms.
42. neural-assembly-compiler: A neural assembly compiler for pyTorch based on adaptive-neural-compilation.
43. caffemodel2pytorch: Convert Caffe models to PyTorch.
44. extension-cpp: C++ extensions in PyTorch
45. pytoune: A Keras-like framework and utilities for PyTorch
46. jetson-reinforcement: Deep reinforcement learning libraries for NVIDIA Jetson TX1/TX2 with PyTorch, OpenAI Gym, and Gazebo robotics simulator.
47. matchbox: Write PyTorch code at the level of individual examples, then run it efficiently on minibatches.
48. torch-two-sample: A PyTorch library for two-sample tests
49. pytorch-summary: Model summary in PyTorch similar to model.summary() in Keras
50. mpl.pytorch: Pytorch implementation of MaxPoolingLoss.
51. scVI-dev: Development branch of the scVI project in PyTorch
52. apex: An Experimental PyTorch Extension(will be deprecated at a later point)
53. ELF: ELF: a platform for game research.
54. Torchlite: A high level library on top of(not only) Pytorch
55. joint-vae: Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation star2
56. SLM-Lab: Modular Deep Reinforcement Learning framework in PyTorch.
57. bindsnet: A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch
58. pro_gan_pytorch: ProGAN package implemented as an extension of PyTorch nn.Module
59. pytorch_geometric: Geometric Deep Learning Extension Library for PyTorch
60. torchplus: Implements the + operator on PyTorch modules, returning sequences.
61. lagom: lagom: A light PyTorch infrastructure to quickly prototype reinforcement learning algorithms.
62. torchbearer: torchbearer: A model training library for researchers using PyTorch.
63. pytorch-maml-rl: Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch.
64. NALU: Basic pytorch implementation of NAC/NALU from Neural Arithmetic Logic Units paper by trask et.al arxiv.org/pdf/1808.00508.pdf
65. QuCumber: Neural Network Many-Body Wavefunction Reconstruction
66. magnet: Deep Learning Projects that Build Themselves http://magnet-dl.readthedocs.io/
67. opencv_transforms: OpenCV implementation of Torchvision's image augmentations
68. fastai: The fast.ai deep learning library, lessons, and tutorials
69. pytorch-dense-correspondence: Code for "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation" arxiv.org/pdf/1806.08756.pdf
70. colorization-pytorch: PyTorch reimplementation of Interactive Deep Colorization richzhang.github.io/ideepcolor
71. beauty-net: A simple, flexible, and extensible template for PyTorch. It's beautiful.
72. OpenChem: OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research mariewelt.github.io/OpenChem
73. torchani: Accurate Neural Network Potential on PyTorch aiqm.github.io/torchani
74. PyTorch-LBFGS: A PyTorch implementation of L-BFGS.
75. gpytorch: A highly efficient and modular implementation of Gaussian Processes in PyTorch.
76. hessian: hessian in pytorch.
77. vel: Velocity in deep-learning research.
78. nonechucks: Skip bad items in your PyTorch DataLoader, use Transforms as Filters, and more!
79. torchstat: Model analyzer in PyTorch.
80. QNNPACK: Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators.
81. torchdiffeq: Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
82. redner: A differentiable Monte Carlo path tracer
83. pixyz: a library for developing deep generative models in a more concise, intuitive and extendable way.
84. euclidesdb: A multi-model machine learning feature embedding database http://euclidesdb.readthedocs.io
85. pytorch2keras: Convert PyTorch dynamic graph to Keras model.
86. salad: Semi-Supervised Learning and Domain Adaptation.
87. netharn: Parameterized fit and prediction harnesses for pytorch.
88. dgl: Python package built to ease deep learning on graph, on top of existing DL frameworks. http://dgl.ai.
89. gandissect: Pytorch-based tools for visualizing and understanding the neurons of a GAN. gandissect.csail.mit.edu
90. delira: Lightweight framework for fast prototyping and training deep neural networks in medical imaging delira.rtfd.io
91. mushroom: Python library for Reinforcement Learning experiments.
92. Xlearn: Transfer Learning Library
93. geoopt: Riemannian Adaptive Optimization Methods with pytorch optim
94. vegans: A library providing various existing GANs in PyTorch.
95. torchgeometry: TGM: PyTorch Geometry
96. AdverTorch: A Toolbox for Adversarial Robustness (attack/defense/training) Research
97. AdaBound: An optimizer that trains as fast as Adam and as good as SGD.a
98. fenchel-young-losses: Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses
99. pytorch-OpCounter: Count the FLOPs of your PyTorch model.
100. Tor10: A Generic Tensor-Network library that is designed for quantum simulation, base on the pytorch.
101. Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.
102. Ax: Adaptive Experimentation Platform
103. pywick: High-level batteries-included neural network training library for Pytorch
104. torchgpipe: A GPipe implementation in PyTorch torchgpipe.readthedocs.io
105. hub: Pytorch Hub is a pre-trained model repository designed to facilitate research reproducibility.
106. pytorch-lightning: Rapid research framework for Pytorch. The researcher's version of keras.
107. Tor10: A Generic Tensor-Network library that is designed for quantum simulation, base on the pytorch.
108. tensorwatch: Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning from Microsoft Research.
109. wavetorch: Numerically solving and backpropagating through the wave equation arxiv.org/abs/1904.12831
110. diffdist: diffdist is a python library for pytorch. It extends the default functionality of torch.autograd and adds support for differentiable communication between processes.
111. torchprof: A minimal dependency library for layer-by-layer profiling of Pytorch models.
112. osqpth: The differentiable OSQP solver layer for PyTorch.
113. mctorch: A manifold optimization library for deep learning.
114. pytorch-hessian-eigenthings: Efficient PyTorch Hessian eigendecomposition using the Hessian-vector product and stochastic power iteration.
115. MinkowskiEngine: Minkowski Engine is an auto-diff library for generalized sparse convolutions and high-dimensional sparse tensors.
116. pytorch-cpp-rl: PyTorch C++ Reinforcement Learning
117. pytorch-toolbelt: PyTorch extensions for fast R&D prototyping and Kaggle farming
118. argus-tensor-stream: A library for real-time video stream decoding to CUDA memory tensorstream.argus-ai.com
119. macarico: learning to search in pytorch
120. rlpyt: Reinforcement Learning in PyTorch
121. pywarm: A cleaner way to build neural networks for PyTorch. blue-season.github.io/pywarm
122. learn2learn: PyTorch Meta-learning Framework for Researchers http://learn2learn.net
123. torchbeast: A PyTorch Platform for Distributed RL
124. higher: higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.
125. Torchelie: Torchélie is a set of utility functions, layers, losses, models, trainers and other things for PyTorch. torchelie.readthedocs.org
126. CrypTen: CrypTen is a Privacy Preserving Machine Learning framework written using PyTorch that allows researchers and developers to train models using encrypted data. CrypTen currently supports Secure multi-party computation as its encryption mechanism.
127. cvxpylayers: cvxpylayers is a Python library for constructing differentiable convex optimization layers in PyTorch
128. RepDistiller: Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods
129. kaolin: PyTorch library aimed at accelerating 3D deep learning research
130. PySNN: Efficient Spiking Neural Network framework, built on top of PyTorch for GPU acceleration.
131. sparktorch: Train and run Pytorch models on Apache Spark.
132. pytorch-metric-learning: The easiest way to use metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
133. autonomous-learning-library: A PyTorch library for building deep reinforcement learning agents.
134. flambe: An ML framework to accelerate research and its path to production. flambe.ai
135. pytorch-optimizer: Collections of modern optimization algorithms for PyTorch, includes: AccSGD, AdaBound, AdaMod, DiffGrad, Lamb, RAdam, RAdam, Yogi.
136. PyTorch-VAE: A Collection of Variational Autoencoders (VAE) in PyTorch.
137. ray: A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. ray.io
138. Pytorch Geometric Temporal: A temporal extension library for PyTorch Geometric
139. Poutyne: A Keras-like framework for PyTorch that handles much of the boilerplating code needed to train neural networks.
140. Pytorch-Toolbox: This is toolbox project for Pytorch. Aiming to make you write Pytorch code more easier, readable and concise.
141. Pytorch-contrib: It contains reviewed implementations of ideas from recent machine learning papers.
142. EfficientNet PyTorch: It contains an op-for-op PyTorch reimplementation of EfficientNet, along with pre-trained models and examples.
143. PyTorch/XLA: PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs.
144. webdataset: WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives.
145. volksdep: volksdep is an open-source toolbox for deploying and accelerating PyTorch, Onnx and Tensorflow models with TensorRT.
146. PyTorch-StudioGAN: StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea.
147. torchdrift: drift detection library
148. accelerate : A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision
149. lightning-transformers: Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.
150. Flower A unified approach to federated learning, analytics, and evaluation. It allows to federated any machine learning workload.
151. lightning-flash: Flash is a collection of tasks for fast prototyping, baselining and fine-tuning scalable Deep Learning models, built on PyTorch Lightning.
152. Pytorch Geometric Signed Directed: A signed and directed extension library for PyTorch Geometric.
153. Koila: A simple wrapper around pytorch that prevents CUDA out of memory issues.
154. Renate: A library for real-world continual learning.
155. ANEE – Adaptive Neural Execution Engine for PyTorch transformers. Provides per-token dynamic layer skipping, profiler-based gating, and KV-cache-safe sparse inference.

Tutorials, books, & examples

1. Practical Pytorch: Tutorials explaining different RNN models
2. DeepLearningForNLPInPytorch: An IPython Notebook tutorial on deep learning, with an emphasis on Natural Language Processing.
3. pytorch-tutorial: tutorial for researchers to learn deep learning with pytorch.
4. pytorch-exercises: pytorch-exercises collection.
5. pytorch tutorials: Various pytorch tutorials.
6. pytorch examples: A repository showcasing examples of using pytorch
7. pytorch practice: Some example scripts on pytorch.
8. pytorch mini tutorials: Minimal tutorials for PyTorch adapted from Alec Radford's Theano tutorials.
9. pytorch text classification: A simple implementation of CNN based text classification in Pytorch
10. cats vs dogs: Example of network fine-tuning in pytorch for the kaggle competition Dogs vs. Cats Redux: Kernels Edition. Currently #27 (0.05074) on the leaderboard.
11. convnet: This is a complete training example for Deep Convolutional Networks on various datasets (ImageNet, Cifar10, Cifar100, MNIST).
12. pytorch-generative-adversarial-networks: simple generative adversarial network (GAN) using PyTorch.
13. pytorch containers: This repository aims to help former Torchies more seamlessly transition to the "Containerless" world of PyTorch by providing a list of PyTorch implementations of Torch Table Layers.
14. T-SNE in pytorch: t-SNE experiments in pytorch
15. AAE_pytorch: Adversarial Autoencoders (with Pytorch).
16. Kind_PyTorch_Tutorial: Kind PyTorch Tutorial for beginners.
17. pytorch-poetry-gen: a char-RNN based on pytorch.
18. pytorch-REINFORCE: PyTorch implementation of REINFORCE, This repo supports both continuous and discrete environments in OpenAI gym.
19. PyTorch-Tutorial: Build your neural network easy and fast https://morvanzhou.github.io/tutorials/
20. pytorch-intro: A couple of scripts to illustrate how to do CNNs and RNNs in PyTorch
21. pytorch-classification: A unified framework for the image classification task on CIFAR-10/100 and ImageNet.
22. pytorch_notebooks - hardmaru: Random tutorials created in NumPy and PyTorch.
23. pytorch_tutoria-quick: Quick PyTorch introduction and tutorial. Targets computer vision, graphics and machine learning researchers eager to try a new framework.
24. Pytorch_fine_tuning_Tutorial: A short tutorial on performing fine tuning or transfer learning in PyTorch.
25. pytorch_exercises: pytorch-exercises
26. traffic-sign-detection: nyu-cv-fall-2017 example
27. mss_pytorch: Singing Voice Separation via Recurrent Inference and Skip-Filtering Connections - PyTorch Implementation. Demo: js-mim.github.io/mss_pytorch
28. DeepNLP-models-Pytorch Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ: NLP with Deep Learning)
29. Mila introductory tutorials: Various tutorials given for welcoming new students at MILA.
30. pytorch.rl.learning: for learning reinforcement learning using PyTorch.
31. minimal-seq2seq: Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch
32. tensorly-notebooks: Tensor methods in Python with TensorLy tensorly.github.io/dev
33. pytorch_bits: time-series prediction related examples.
34. skip-thoughts: An implementation of Skip-Thought Vectors in PyTorch.
35. video-caption-pytorch: pytorch code for video captioning.
36. Capsule-Network-Tutorial: Pytorch easy-to-follow Capsule Network tutorial.
37. code-of-learn-deep-learning-with-pytorch: This is code of book "Learn Deep Learning with PyTorch" item.jd.com/17915495606.html
38. RL-Adventure: Pytorch easy-to-follow step-by-step Deep Q Learning tutorial with clean readable code.
39. accelerated_dl_pytorch: Accelerated Deep Learning with PyTorch at Jupyter Day Atlanta II.
40. RL-Adventure-2: PyTorch4 tutorial of: actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
41. Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
42. adversarial-autoencoders-with-pytorch
43. transfer learning using pytorch
44. how-to-implement-a-yolo-object-detector-in-pytorch
45. pytorch-for-recommenders-101
46. pytorch-for-numpy-users
47. PyTorch Tutorial: PyTorch Tutorials in Chinese.
48. grokking-pytorch: The Hitchiker's Guide to PyTorch
49. PyTorch-Deep-Learning-Minicourse: Minicourse in Deep Learning with PyTorch.
50. pytorch-custom-dataset-examples: Some custom dataset examples for PyTorch
51. Multiplicative LSTM for sequence-based Recommenders
52. deeplearning.ai-pytorch: PyTorch Implementations of Coursera's Deep Learning(deeplearning.ai) Specialization.
53. MNIST_Pytorch_python_and_capi: This is an example of how to train a MNIST network in Python and run it in c++ with pytorch 1.0
54. torch_light: Tutorials and examples include Reinforcement Training, NLP, CV
55. portrain-gan: torch code to decode (and almost encode) latents from art-DCGAN's Portrait GAN.
56. mri-analysis-pytorch: MRI analysis using PyTorch and MedicalTorch
57. cifar10-fast: Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 seconds as described in this blog series.
58. Intro to Deep Learning with PyTorch: A free course by Udacity and facebook, with a good intro to PyTorch, and an interview with Soumith Chintala, one of the original authors of PyTorch.
59. pytorch-sentiment-analysis: Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
60. pytorch-image-models: PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more.
61. CIFAR-ZOO: Pytorch implementation for multiple CNN architectures and improve methods with state-of-the-art results.
62. d2l-pytorch: This is an attempt to modify Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook's code into PyTorch.
63. thinking-in-tensors-writing-in-pytorch: Thinking in tensors, writing in PyTorch (a hands-on deep learning intro).
64. NER-BERT-pytorch: PyTorch solution of named entity recognition task Using Google AI's pre-trained BERT model.
65. pytorch-sync-batchnorm-example: How to use Cross Replica / Synchronized Batchnorm in Pytorch.
66. SentimentAnalysis: Sentiment analysis neural network trained by fine tuning BERT on the Stanford Sentiment Treebank, thanks to Hugging Face's Transformers library.
67. pytorch-cpp: C++ implementations of PyTorch tutorials for deep learning researchers (based on the Python tutorials from pytorch-tutorial).
68. Deep Learning with PyTorch: Zero to GANs: Interactive and coding-focused tutorial series on introduction to Deep Learning with PyTorch (video).
69. Deep Learning with PyTorch: Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch, the book includes a case study: building an algorithm capable of detecting malignant lung tumors using CT scans.
70. Serverless Machine Learning in Action with PyTorch and AWS: Serverless Machine Learning in Action is a guide to bringing your experimental PyTorch machine learning code to production using serverless capabilities from major cloud providers like AWS, Azure, or GCP.
71. LabML NN: A collection of PyTorch implementations of neural networks architectures and algorithms with side-by-side notes.
72. Run your PyTorch Example Fedarated with Flower: This example demonstrates how an already existing centralized PyTorch machine learning project can be federated with Flower. A Cifar-10 dataset is used together with a convolutional neural network (CNN).
73. The Math Behind Artificial Intelligence: A free FreeCodeCamp book teaching the math behind AI in plain English from an engineering point of view. It covers linear algebra, calculus, probability & statistics, and optimization theory with analogies, real-life applications, and Python code examples.
74. QuiddityML: The Duolingo of machine learning: short lessons, 11 types of hands-on exercises on the skills the job needs, spaced repetition so you don't forget what you learned, and a clear beginner-to-advanced roadmap covering Python, PyTorch, math for ML, ML foundations, NLP, vision, and more.

Paper implementations

1. google_evolution: This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al.
2. pyscatwave: Fast Scattering Transform with CuPy/PyTorch,read the paper here
3. scalingscattering: Scaling The Scattering Transform : Deep Hybrid Networks.
4. deep-auto-punctuation: a pytorch implementation of auto-punctuation learned character by character.
5. Realtime_Multi-Person_Pose_Estimation: This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here .
6. PyTorch-value-iteration-networks: PyTorch implementation of the Value Iteration Networks (NIPS '16) paper
7. pytorch_Highway: Highway network implemented in pytorch.
8. pytorch_NEG_loss: NEG loss implemented in pytorch.
9. pytorch_RVAE: Recurrent Variational Autoencoder that generates sequential data implemented in pytorch.
10. pytorch_TDNN: Time Delayed NN implemented in pytorch.
11. eve.pytorch: An implementation of Eve Optimizer, proposed in Imploving Stochastic Gradient Descent with Feedback, Koushik and Hayashi, 2016.
12. e2e-model-learning: Task-based end-to-end model learning.
13. pix2pix-pytorch: PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".
14. Single Shot MultiBox Detector: A PyTorch Implementation of Single Shot MultiBox Detector.
15. DiscoGAN: PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
16. official DiscoGAN implementation: Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks".
17. pytorch-es: This is a PyTorch implementation of Evolution Strategies .
18. piwise: Pixel-wise segmentation on VOC2012 dataset using pytorch.
19. pytorch-dqn: Deep Q-Learning Network in pytorch.
20. neuraltalk2-pytorch: image captioning model in pytorch(finetunable cnn in branch with_finetune)
21. vnet.pytorch: A Pytorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.
22. pytorch-fcn: PyTorch implementation of Fully Convolutional Networks.
23. WideResNets: WideResNets for CIFAR10/100 implemented in PyTorch. This implementation requires less GPU memory than what is required by the official Torch implementation: https://github.com/szagoruyko/wide-residual-networks .
24. pytorch_highway_networks: Highway networks implemented in PyTorch.
25. pytorch-NeuCom: Pytorch implementation of DeepMind's differentiable neural computer paper.
26. captionGen: Generate captions for an image using PyTorch.
27. AnimeGAN: A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.
28. Cnn-text classification: This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch.
29. deepspeech2: Implementation of DeepSpeech2 using Baidu Warp-CTC. Creates a network based on the DeepSpeech2 architecture, trained with the CTC activation function.
30. seq2seq: This repository contains implementations of Sequence to Sequence (Seq2Seq) models in PyTorch
31. Asynchronous Advantage Actor-Critic in PyTorch: This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learning. Since PyTorch has a easy method to control shared memory within multiprocess, we can easily implement asynchronous method like A3C.
32. densenet: This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Convolutional Networks by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. This implementation gets a CIFAR-10+ error rate of 4.77 with a 100-layer DenseNet-BC with a growth rate of 12. Their official implementation and links to many other third-party implementations are available in the liuzhuang13/DenseNet repo on GitHub.
33. nninit: Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin.
34. faster rcnn: This is a PyTorch implementation of Faster RCNN. This project is mainly based on py-faster-rcnn and TFFRCNN.For details about R-CNN please refer to the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
35. doomnet: PyTorch's version of Doom-net implementing some RL models in ViZDoom environment.
36. flownet: Pytorch implementation of FlowNet by Dosovitskiy et al.
37. sqeezenet: Implementation of Squeezenet in pytorch, #### pretrained models on CIFAR10 data to come Plan to train the model on cifar 10 and add block connections too.
38. WassersteinGAN: wassersteinGAN in pytorch.
39. optnet: This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch source code to reproduce the experiments in our paper OptNet: Differentiable Optimization as a Layer in Neural Networks.
40. qp solver: A fast and differentiable QP solver for PyTorch. Crafted by Brandon Amos and J. Zico Kolter.
41. Continuous Deep Q-Learning with Model-based Acceleration : Reimplementation of Continuous Deep Q-Learning with Model-based Acceleration.
42. Learning to learn by gradient descent by gradient descent: PyTorch implementation of Learning to learn by gradient descent by gradient descent.
43. fast-neural-style: pytorch implementation of fast-neural-style, The model uses the method described in Perceptual Losses for Real-Time Style Transfer and Super-Resolution along with Instance Normalization.
44. PytorchNeuralStyleTransfer: Implementation of Neural Style Transfer in Pytorch.
45. Fast Neural Style for Image Style Transform by Pytorch: Fast Neural Style for Image Style Transform by Pytorch .
46. neural style transfer: An introduction to PyTorch through the Neural-Style algorithm (https://arxiv.org/abs/1508.06576) developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.
47. VIN_PyTorch_Visdom: PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.
48. YOLO2: YOLOv2 in PyTorch.
49. attention-transfer: Attention transfer in pytorch, read the paper here.
50. SVHNClassifier: A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks.
51. pytorch-deform-conv: PyTorch implementation of Deformable Convolution.
52. BEGAN-pytorch: PyTorch implementation of BEGAN: Boundary Equilibrium Generative Adversarial Networks.
53. treelstm.pytorch: Tree LSTM implementation in PyTorch.
54. AGE: Code for paper "Adversarial Generator-Encoder Networks" by Dmitry Ulyanov, Andrea Vedaldi and Victor Lempitsky which can be found here
55. ResNeXt.pytorch: Reproduces ResNet-V3 (Aggregated Residual Transformations for Deep Neural Networks) with pytorch.
56. pytorch-rl: Deep Reinforcement Learning with pytorch & visdom
57. Deep-Leafsnap: LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.
58. pytorch-CycleGAN-and-pix2pix: PyTorch implementation for both unpaired and paired image-to-image translation.
59. A3C-PyTorch:PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch
60. pytorch-value-iteration-networks: Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)
61. PyTorch-Style-Transfer: PyTorch Implementation of Multi-style Generative Network for Real-time Transfer
62. pytorch-deeplab-resnet: pytorch-deeplab-resnet-model.
63. pointnet.pytorch: pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
64. pytorch-playground: Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet).
65. pytorch-dnc: Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom.
66. pytorch_image_classifier: Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.
67. mnist-svhn-transfer: PyTorch Implementation of CycleGAN and SGAN for Domain Transfer (Minimal).
68. pytorch-yolo2: pytorch-yolo2
69. dni: Implement