Related Projects

SparseConvNet - Submanifold sparse convolutional networks

  •    C++

This library brings Spatially-sparse convolutional networks to Torch/PyTorch. Moreover, it introduces Submanifold Sparse Convolutions, that can be used to build computationally efficient sparse VGG/ResNet/DenseNet-style networks. With regular 3x3 convolutions, the set of active (non-zero) sites grows rapidly: With Submanifold Sparse Convolutions, the set of active sites is unchanged. Active sites look at their active neighbors (green); non-active sites (red) have no computational overhead: Stacking Submanifold Sparse Convolutions to build VGG and ResNet type ConvNets, information can flow along lines or surfaces of active points.

XNOR-Net - ImageNet classification using binary Convolutional Neural Networks

  •    Lua

This is the Torch 7.0 implementation of XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. This software is implemented on top of the implementation of ImageNet-multiGPU and has all the same requirements.

2D-and-3D-face-alignment - This repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper

  •    Lua

This repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper. Please visit our webpage or read bellow for instructions on how to run the code and access the dataset. Note: If you are interested in a binarized version, capable of running on devices with limited resources please also check https://github.com/1adrianb/binary-face-alignment for a demo.

NNPACK - Acceleration package for neural networks on multi-core CPUs

  •    C

NNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs. NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives leveraged in leading deep learning frameworks, such as PyTorch, Caffe2, MXNet, tiny-dnn, Caffe, Torch, and Darknet.

PyTorch-Multi-Style-Transfer - Neural Style and MSG-Net

  •    Jupyter

This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included by ModelDepot. We also provide Torch implementation and MXNet implementation. Image Style Transfer Using Convolutional Neural Networks by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.


3D-convolutional-speaker-recognition - :speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

  •    Python

This repository contains the code release for our paper titled as "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks". The link to the paper is provided as well. The code has been developed using TensorFlow. The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.

Activity-Recognition-with-CNN-and-RNN - Temporal Segments LSTM and Temporal-Inception for Activity Recognition

  •    Lua

In 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.

regl-cnn - Digit recognition with Convolutional Neural Networks in WebGL

  •    Javascript

GPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

cnn_graph - Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

  •    Jupyter

Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Neural Information Processing Systems (NIPS), 2016. Clone this repository.

emotion-recognition-neural-networks - Emotion recognition using DNN with tensorflow

  •    Python

This repository is the out project about mood recognition using convolutional neural network for the course Seminar Neural Networks at TU Delft. We use the FER-2013 Faces Database, a set of 28,709 pictures of people displaying 7 emotional expressions (angry, disgusted, fearful, happy, sad, surprised and neutral).

artistic-style-transfer - Convolutional neural networks for artistic style transfer.

  •    Jupyter

This repository contains (TensorFlow and Keras) code that goes along with a related blog post and talk (PDF). Together, they act as a systematic look at convolutional neural networks from theory to practice, using artistic style transfer as a motivating example. The blog post provides context and covers the underlying theory, while working through the Jupyter notebooks in this repository offers a more hands-on learning experience. If you have any questions about any of this stuff, feel free to open an issue or tweet at me: @copingbear.

tensorflow-image-detection - A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception

  •    Python

A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

  •    Swift

Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS.

NiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

  •    Python

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

u-net - U-Net: Convolutional Networks for Biomedical Image Segmentation

  •    Python

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation. This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good staring point for further, more serious approaches.

sudoku - Can Neural Networks Crack Sudoku?

  •    Python

Sudoku is a popular number puzzle that requires you to fill blanks in a 9X9 grid with digits so that each column, each row, and each of the nine 3×3 subgrids contains all of the digits from 1 to 9. There have been various approaches to solving that, including computational ones. In this project, I show that simple convolutional neural networks have the potential to crack Sudoku without any rule-based postprocessing. Number of blanks where the prediction matched the solution / Number of blanks.

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit

  •    Lua

This is fairseq, a sequence-to-sequence learning toolkit for Torch from Facebook AI Research tailored to Neural Machine Translation (NMT). It implements the convolutional NMT models proposed in Convolutional Sequence to Sequence Learning and A Convolutional Encoder Model for Neural Machine Translation as well as a standard LSTM-based model. It 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, English to German and English to Romanian translation. Note, there is now a PyTorch version fairseq-py of this toolkit and new development efforts will focus on it.

crnn - Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.

  •    Lua

This software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR. For details, please refer to our paper http://arxiv.org/abs/1507.05717. UPDATE Mar 14, 2017 A Docker file has been added to the project. Thanks to @varun-suresh.