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.
torch-sampling : This package provides a set of transforms and data structures for sampling from in-memory or out-of-memory data.
torchcraft-py : Python wrapper for TorchCraft, a bridge between Torch and StarCraft for AI research.
aorun : Aorun intend to be a Keras with PyTorch as backend.
ptstat: Probabilistic Programming and Statistical Inference in PyTorch
pytorch-extension: This is a CUDA extension for PyTorch which computes the Hadamard product of two tensors.
tensorboard-pytorch: This module saves PyTorch tensors in tensorboard format for inspection. Currently supports scalar, image, audio, histogram features in tensorboard.
pytorch-seq2seq: A framework for sequence-to-sequence (seq2seq) models implemented in PyTorch.
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.
img_classification_pk_pytorch: Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)
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.
convnet : This is a complete training example for Deep Convolutional Networks on various datasets (ImageNet, Cifar10, Cifar100, MNIST).
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.
pytorch_tutoria-quick: Quick PyTorch introduction and tutorial. Targets computer vision, graphics and machine learning researchers eager to try a new framework.
pytorch-NeuCom : Pytorch implementation of DeepMind's differentiable neural computer paper.
captionGen : Generate captions for an image using PyTorch.
AnimeGAN : A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.
Cnn-text classification : This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch.
deepspeech2 : Implementation of DeepSpeech2 using Baidu Warp-CTC. Creates a network based on the DeepSpeech2 architecture, trained with the CTC activation function.
seq2seq : This repository contains implementations of Sequence to Sequence (Seq2Seq) models in PyTorch
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.
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.
nninit : Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin.
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.
doomnet : PyTorch's version of Doom-net implementing some RL models in ViZDoom environment.
flownet : Pytorch implementation of FlowNet by Dosovitskiy et al.
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.
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.
qp solver : A fast and differentiable QP solver for PyTorch. Crafted by Brandon Amos and J. Zico Kolter.
GAN-weight-norm: Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"
lgamma: Implementations of polygamma, lgamma, and beta functions for PyTorch
bigBatch : Code used to generate the results appearing in "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"
rl_a3c_pytorch: Reinforcement learning with implementation of A3C LSTM for Atari 2600.
pytorch-retraining: Transfer Learning Shootout for PyTorch's model zoo (torchvision)
nmp_qc: Neural Message Passing for Computer Vision
OpenFacePytorch: PyTorch module to use OpenFace's nn4.small2.v1.t7 model
neural-combinatorial-rl-pytorch: PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning.
95.pytorch-nec: PyTorch Implementation of Neural Episodic Control (NEC)
seq2seq.pytorch: Sequence-to-Sequence learning using PyTorch
Pytorch-Sketch-RNN: a pytorch implementation of arxiv.org/abs/1704.03477
pytorch-pruning: PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference
DrQA : A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.