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pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
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 tensorboardStarGAN - Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
PyTorch implementation of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator.
stargan gan image-to-image-translation pytorch generative-adversarial-network image-manipulation computer-vision neural-networksfairseq-py - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
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-intelligenceskorch - A scikit-learn compatible neural network library that wraps pytorch
A scikit-learn compatible neural network library that wraps PyTorch. To see a more elaborate example, look here.
scikit-learn pytorch machine-learningpix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1 1NVIDIA Corporation, 2UC Berkeley In arxiv, 2017.
gan deep-learning deep-neural-networks pytorch pix2pix image-to-image-translation generative-adversarial-network computer-vision computer-graphicssemantic-segmentation-pytorch - Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
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 ade20kgenerative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.
vae gan pytorch tensorflow generative-model machine-learning rbm restricted-boltzmann-machinedeep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program
This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.
deep-reinforcement-learning reinforcement-learning reinforcement-learning-algorithms neural-networks pytorch pytorch-rl ddpg dqn ppo dynamic-programming cross-entropy hill-climbing ml-agents openai-gym-solutions openai-gym rl-algorithmsdistiller - Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research
Distiller is an open-source Python package for neural network compression research. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic.
pytorch pruning quantization pruning-structures jupyter-notebook network-compression deep-neural-networks regularization group-lassoonnx - Open Neural Network Exchange
Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation). Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.
deep-learning deep-neural-networks neural-network onnx pytorch caffe2 cntkdeep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.
deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorchpolyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes
Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.
deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8sPractical_RL - A course in reinforcement learning in the wild
A course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA and maintained to be friendly to online students (both english and russian). The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.
reinforcement-learning course-materials deep-learning deep-reinforcement-learning git-course mooc theano lasagne tensorflow pytorch pytorch-tutorials kerasdeep-learning-model-convertor - The convertor/conversion of deep learning models for different deep learning frameworks/softwares
Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength. The sheet below is a overview of all convertors in github (not only contain official provided and more are user-self implementations). I just make a little work to collect these convertors. Also, hope everyone can support this project to help more people who're also crazy because of various frameworks.
deep-learning model neural-network convertor model-convertor awesome-list deep-learning-framework tensorflow caffe pytorch mxnet keras torch caffe2AlphaPose - Multi-Person Pose Estimation System
Alpha Pose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset. Note: Please read PoseFlow/README.md for details.
pose-estimation deep-learning iccv2017 posetracking torch computer-vision machine-learning tracking state-of-the-art gpu pytorch faster-rcnnfairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
We also provide pre-trained models for several benchmark translation datasets. Currently fairseq requires PyTorch version >= 0.4.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.
pytorch artificial-intelligencepytorch-CycleGAN-and-pix2pix - Image-to-image translation in PyTorch (e
This is our PyTorch implementation for both unpaired and paired image-to-image translation. It is still under active development. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.
pytorch gan cyclegan pix2pix deep-learning computer-vision computer-graphics image-manipulation image-generation generative-adversarial-network ganspytorch-book - PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation
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-notebookfastai - The fast.ai deep learning library, lessons, and tutorials
The fast.ai deep learning library, lessons, and tutorials. Copyright 2017 onwards, Jeremy Howard. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.
deep-learning machine-learning pytorch
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