1900+pytorch-optimizer: Collections of modern optimization algorithms for PyTorch, includes: AccSGD, AdaBound, AdaMod, DiffGrad, Lamb, RAdam, RAdam, Yogi.
2200+PyTorch-VAE: A Collection of Variational Autoencoders (VAE) in PyTorch.
16700+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
1000-Poutyne: A Keras-like framework for PyTorch that handles much of the boilerplating code needed to train neural networks.
1000-Pytorch-Toolbox: This is toolbox project for Pytorch. Aiming to make you write Pytorch code more easier, readable and concise.
1000-Pytorch-contrib: It contains reviewed implementations of ideas from recent machine learning papers.
6200+EfficientNet PyTorch: It contains an op-for-op PyTorch reimplementation of EfficientNet, along with pre-trained models and examples.
1300+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.
1000-webdataset: WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives.
1000-volksdep: volksdep is an open-source toolbox for deploying and accelerating PyTorch, Onnx and Tensorflow models with TensorRT.
1700+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.
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.
1000-skip-gram-pytorch: A complete pytorch implementation of skipgram model (with subsampling and negative sampling). The embedding result is tested with Spearman's rank correlation.
1000-stackGAN-v2: Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks by Han Zhang*, Tao Xu*, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas.
1000-yolo2-pytorch: The YOLOv2 is one of the most popular one-stage object detector. This project adopts PyTorch as the developing framework to increase productivity, and utilize ONNX to convert models into Caffe 2 to benifit engineering deployment.
2800+Detectron.pytorch: A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.
1000-R2Plus1D-PyTorch: PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"
1000-StackNN: A PyTorch implementation of differentiable stacks for use in neural networks.
1000-translagent: Code for Emergent Translation in Multi-Agent Communication.
1000-ban-vqa: Bilinear attention networks for visual question answering.
1200+pytorch-openai-transformer-lm: This is a PyTorch implementation of the TensorFlow code provided with OpenAI's paper "Improving Language Understanding by Generative Pre-Training" by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
1000-deepfloat: This repository contains the SystemVerilog RTL, C++, HLS (Intel FPGA OpenCL to wrap RTL code) and Python needed to reproduce the numerical results in "Rethinking floating point for deep learning"