GANotebooks - wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

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wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

wgan wgan-gp infogan lasagne keras pytorch tensorflow pix2pix dcgan dragan cyclegan

Self-Attention-GAN - Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

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Han Zhang, Ian Goodfellow, Dimitris Metaxas and Augustus Odena, "Self-Attention Generative Adversarial Networks." arXiv preprint arXiv:1805.08318 (2018). This repository provides a PyTorch implementation of SAGAN. Both wgan-gp and wgan-hinge loss are ready, but note that wgan-gp is somehow not compatible with the spectral normalization. Remove all the spectral normalization at the model for the adoption of wgan-gp.

wgan-gp - A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

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An pytorch implementation of Paper "Improved Training of Wasserstein GANs". Some Sample Result, you can refer to the results/toy/ folder for details.

wgan-gp pytorch

chainer-gan-lib - Chainer implementation of recent GAN variants

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This repository collects chainer implementation of state-of-the-art GAN algorithms. These codes are evaluated with the inception score on Cifar-10 dataset. Note that our codes are not faithful re-implementation of the original paper. This implementation has been tested with the following versions.

deep-learning generative-adversarial-network dcgan wgan-gp

pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers

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This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

deep-learning pytorch-tutorial neural-networks pytorch tutorial tensorboard

pytorch-caffe-darknet-convert - convert between pytorch, caffe prototxt/weights and darknet cfg/weights

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This repository is specially designed for pytorch-yolo2 to convert pytorch trained model to any platform. It can also be used as a common model converter between pytorch, caffe and darknet. MIT License (see LICENSE file).

caffe darknet yolo yolo2 convert pytorch weight

pytorch-cpp - Pytorch C++ Library

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Pytorch-C++ is a simple C++ 11 library which provides a Pytorch-like interface for building neural networks and inference (so far only forward pass is supported). The library respects the semantics of torch.nn module of PyTorch. Models from pytorch/vision are supported and can be easily converted. We also support all the models from our image segmentation repository (scroll down for the gif with example output of one of our segmentation models). The library heavily relies on an amazing ATen library and was inspired by cunnproduction.

pytorch-book - PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

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PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

pytorch pytorch-tutorials pytorch-tutorials-cn deep-learning neural-style charrnn gan caption neuraltalk image-classification visdom tensorboard nn tensor autograd jupyter-notebook

Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc

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A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

pytorch machine-learning deep-learning tutorials papers awesome awesome-list pytorch-tutorials data-science nlp nlp-library cv computer-vision natural-language-processing facebook probabilistic-programming utility-library neural-network pytorch-model

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

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PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

neural-network autograd gpu numpy deep-learning tensor

fairseq-py - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

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This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French and English to German translation. Currently fairseq-py requires PyTorch version >= 0.3.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.

pytorch artificial-intelligence

pytorch-seq2seq - pytorch-seq2seq is a framework for sequence-to-sequence (seq2seq) models in PyTorch

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This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution. This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).

pytorch seq2seq deeplearning

semantic-segmentation-pytorch - Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

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This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing dataset. This module differs from the built-in PyTorch BatchNorm as the mean and standard-deviation are reduced across all devices during training. The importance of synchronized batch normalization in object detection has been recently proved with a an extensive analysis in the paper MegDet: A Large Mini-Batch Object Detector, and we empirically find that it is also important for segmentation.

pytorch semantic-segmentation scene-recognition ade20k

Amazon-Forest-Computer-Vision - Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

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Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

pytorch data-augmentation kaggle-competition kaggle deep-learning computer-vision keras neural-networks neural-network-example transfer-learning

pytorch-scripts - A few Windows specific scripts for PyTorch

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It is a repo that contains scripts that makes using PyTorch on Windows easier. Update: Starting from 0.4.0, you can go to the official site for installation steps. The packages here will not be updated. If you just want to install PyTorch as soon as possible, you can try this one out. The current version of the conda package for PyTorch is 0.3.1. You'll need Anaconda first. And then type in the following commands.

OpenNMT-py - Open Source Neural Machine Translation in PyTorch

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This is a Pytorch port of OpenNMT, an open-source (MIT) neural machine translation system. It is designed to be research friendly to try out new ideas in translation, summary, image-to-text, morphology, and many other domains. Codebase is relatively stable, but PyTorch is still evolving. We currently only support PyTorch 0.4 and recommend forking if you need to have stable code.

deep-learning pytorch machine-translation neural-machine-translation

pytorch-yolo-v3 - A PyTorch implementation of the YOLO v3 object detection algorithm

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[UPDATE] : This repo serves as a driver code for my research. I just graduated college, and am very busy looking for research internship / fellowship roles before eventually applying for a masters. I won't have the time to look into issues for the time being. Thank you. This repository contains code for a object detector based on YOLOv3: An Incremental Improvement, implementedin PyTorch. The code is based on the official code of YOLO v3, as well as a PyTorch port of the original code, by marvis. One of the goals of this code is to improve upon the original port by removing redundant parts of the code (The official code is basically a fully blown deep learning library, and includes stuff like sequence models, which are not used in YOLO). I've also tried to keep the code minimal, and document it as well as I can.

yolov3 yolo object-detection pytorch

pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras

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Keras has a neat API to view the visualization of the model which is very helpful while debugging your network. Here is a barebone code to try and mimic the same in PyTorch. The aim is to provide information complementary to, what is not provided by print(your_model) in PyTorch.

pytorch keras summary deep-learning

practical-pytorch - PyTorch tutorials demonstrating modern techniques with readable code

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These tutorials have been merged into the official PyTorch tutorials. Please go there for better maintained versions of these tutorials compatible with newer versions of PyTorch. Learn PyTorch with project-based tutorials. These tutorials demonstrate modern techniques with readable code and use regular data from the internet.

natural-language-processing natural-language-generation nlp nlg seq2seq

pytorch-openai-transformer-lm - A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

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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. This implementation comprises a script to load in the PyTorch model the weights pre-trained by the authors with the TensorFlow implementation.

neural-networks pytorch openai language-model transformer