This is an attempt at implementing Sequence to Sequence Learning with Neural Networks (seq2seq) and reproducing the results in A Neural Conversational Model (aka the Google chatbot). Human: What is the purpose of living? Machine: To live forever.
seq2seq torch machine-learning deep-learning neural-conversation-modelsDIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, Torch, and Tensorflow. Once you have installed DIGITS, visit docs/GettingStarted.md for an introductory walkthrough.
deep-learning machine-learning gpu caffe torchImage Super-Resolution for Anime-style art using Deep Convolutional Neural Networks. And it supports photo. The demo application can be found at http://waifu2x.udp.jp/ .
waifu2x torch super-resolutionNote: 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 caffe2Alpha 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-rcnnThis package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning. The code was written by Jun-Yan Zhu and Taesung Park. Note: Please check out PyTorch implementation for CycleGAN and pix2pix. The PyTorch version is under active development and can produce results comparable or better than this Torch version.
gan generative-adversarial-network deep-learning image-generation image-manipulation cyclegan pix2pix gans computer-vision computer-graphics torchOpenNMT is a full-featured, open-source (MIT) neural machine translation system utilizing the Torch mathematical toolkit. OpenNMT only requires a Torch installation with few dependencies.
neural-machine-translation torch opennmt machine-translation deep-learningHydrogen is an interactive coding environment that supports Python, R, JavaScript and other Jupyter kernels. Checkout our Documentation and Medium blog post to see what you can do with Hydrogen.
data-science jupyter ipython repl hydrogen atom jupyter-kernels nteract execute run julia torch ijulia irkernel itorch plot imageIn the paper Texture Networks: Feed-forward Synthesis of Textures and Stylized Images we describe a faster way to generate textures and stylize images. It requires learning a feedforward generator with a loss function proposed by Gatys et al.. When the model is trained, a texture sample or stylized image of any size can be generated instantly. Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis presents a better architectural design for the generator network. By switching batch_norm to Instance Norm we facilitate the learning process resulting in much better quality.
texture-networks torch neural-style style-transferIf you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g. This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.
deep-learning jupyter lasagne caffe tensorflow sonnet keras theano chainer torch pytorch mxnet cntk dockerfile-generator docker-image caffe2 onnxA bridge between Torch and StarCraft. Synnaeve, G., Nardelli, N., Auvolat, A., Chintala, S., Lacroix, T., Lin, Z., Richoux, F. and Usunier, N., 2016. TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games - arXiv:1611.00625.
starcraft torchcraft bwapi torch reinforcement-learning deep-learning machine-learningThis is an experimental Torch implementation of the VIS + LSTM visual question answering model from the paper Exploring Models and Data for Image Question Answering by Mengye Ren, Ryan Kiros & Richard Zemel. Download the MSCOCO train+val images and VQA data using sh data/download_data.sh. Extract all the downloaded zip files inside the data folder.
torch deep-learning computer-vision natural-language-processingA curated list of awesome Torch tutorials, projects and communities. Codes and related articles. (#) means authors of code and paper are different.
awsome torchA comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-networkWe manually converted the original torch models into caffe format from https://github.com/liuzhuang13/DenseNet. Update (July 27, 2017): for your convenience, we also provide a link to these models on Baidu Disk.
densenet caffe torch imagenet deep-learningCode (only for the convolutional neural network) and dataset for mine and Mindy Yang's final project for Stanford's CS 229: Machine Learning class. Our paper can be found here. The convolutional neural network results on the poster are dated since we continued working after the end of the quarter and were able to achieve around 75% test accuracy (with 70/13/17 train/val/test split) after changing the weight initialization to the Kaiming method. The pictures were taken by placing the object on a white posterboard and using sunlight and/or room lighting. The pictures have been resized down to 512 x 384, which can be changed in data/constants.py (resizing them involves going through step 1 in usage). The devices used were Apple iPhone 7 Plus, Apple iPhone 5S, and Apple iPhone SE.
deep-learning torch trash dataset image-classification convolutional-neural-networks garbageIn this work, we demonstrate a strong baseline two-stream ConvNet using ResNet-101. We use this baseline to thoroughly examine the use of both RNNs and Temporal-ConvNets for extracting spatiotemporal information. Building upon our experimental results, we then propose and investigate two different networks to further integrate spatiotemporal information: 1) temporal segment RNN and 2) Inception-style Temporal-ConvNet. Our analysis identifies specific limitations for each method that could form the basis of future work. Our experimental results on UCF101 and HMDB51 datasets achieve state-of-the-art performances, 94.1% and 69.0%, respectively, without requiring extensive temporal augmentation.
activity-recognition video-understanding torch lstm-neural-networks convolutional-neural-networksThis tool helps convert Torch7 models into Apple CoreML format which can then be run on Apple devices. If you want to run tests, you need MacOS High Sierra 10.13 installed.
ai torch coreml neural-style ios ios11 deep-learningThis is my try on drawing with neural networks, which is faster than Alex J. Champandard's version, and similar in quality. This approach is based on neural artistic style method (L. Gatys), whereas Alex's version uses CNN+MRF approach of Chuan Li. It takes several minutes to redraw Renoir example using GPU and it will easily fit in 4GB GPUs. If you were able to work with Justin Johnson's code for artistic style then this code should work for you too.
neural-doodle neural-style torchA simple React Native plugin to switch a flashlight on/off.Currently supports both iOS (>= 8.0) and Android (all versions).
react-native react ios android flashlight torch react-component device
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