Displaying 1 to 18 from 18 results

imaging - Simple Go image processing package

  •    Go

Package imaging provides basic image manipulation functions (resize, rotate, flip, crop, etc.). This package is based on the standard Go image package and works best along with it.Image manipulation functions provided by the package take any image type that implements image.Image interface as an input, and return a new image of *image.NRGBA type (32bit RGBA colors, not premultiplied by alpha).

TuSimple-DUC - Understanding Convolution for Semantic Segmentation

  •    Python

by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. This repository is for Understanding Convolution for Semantic Segmentation (WACV 2018), which achieved state-of-the-art result on the CityScapes, PASCAL VOC 2012, and Kitti Road benchmark.

Conditional-PixelCNN-decoder - Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network

  •    Python

This is a Tensorflow implementation of Conditional Image Generation with PixelCNN Decoders which introduces the Gated PixelCNN model based on PixelCNN architecture originally mentioned in Pixel Recurrent Neural Networks. The model can be conditioned on latent representation of labels or images to generate images accordingly. Images can also be modelled unconditionally. It can also act as a powerful decoder and can replace deconvolution (transposed convolution) in Autoencoders and GANs. A detailed summary of the paper can be found here. The gating accounts for remembering the context and model more complex interactions, like in LSTM. The network stack on the left is the Vertical stack that takes care of blind spots that occure while convolution due to the masking layer (Refer the Pixel RNN paper to know more about masking). Use of residual connection significantly improves the model performance.

SOTA-Py - A discrete-time Python-based solver for the Stochastic On-Time Arrival routing problem

  •    Python

SOTA-Py is a Python-based solver for the policy- and path-based "SOTA" problems, using the algorithm(s) described in Tractable Pathfinding for the Stochastic On-Time Arrival Problem (also in the corresponding arXiv preprint) and previous works referenced therein. What is the SOTA problem? Read on...




ConvNetSharp - Deep Learning in C#

  •    CSharp

Started initially as C# port of ConvNetJS. You can use ConvNetSharp to train and evaluate convolutional neural networks (CNN). You must have CUDA version 8 and Cudnn version 6.0 (April 27, 2017) installed. Cudnn bin path should be referenced in the PATH environment variable.

ndarray-fft - FFT for ndarrays

  •    Javascript

A fast Fourier transform implementation for ndarrays. You can use this to do image processing operations on big, higher dimensional typed arrays in JavaScript.Executes a fast Fourier transform on the complex valued array x/y.

etl - Expression Templates Library (ETL) with GPU support

  •    C++

ETL is a header only library for C++ that provides vector and matrix classes with support for Expression Templates to perform very efficient operations on them. At this time, the library support compile-time sized matrix and vector and runtime-sized matrix and vector with all element-wise operations implemented. It also supports 1D and 2D convolution, matrix multiplication (naive algorithm and Strassen) and FFT.

deep-scite - :rowboat: A simple recommendation engine (by way of convolutions and embeddings) written in TensorFlow

  •    HTML

DeepScite takes in papers (titles, abstracts) and emits recommendations on whether or not they should be scited by the particular users whose data we've used for training (in the case of this repo, it is me). As output, it also gives a "goodness" score for each word; when this number is high, it has contributed strongly to the paper being (recommended) for sciting, when it is negative, it has contributed strongly to the paper not being recommended.


massiv - Efficient Haskell Arrays featuring Parallel computation

  •    Haskell

massiv is a Haskell library for array manipulation. Performance is one of its main goals, thus it is able to run effortlessly almost all operations in parallel as well as sequentially. The name for this library comes from the Russian word Massiv (Масси́в), which means an Array.

SkimCaffe - Caffe for Sparse Convolutional Neural Network

  •    C++

SkimCaffe has been only tested with bvlc_reference_caffenet, bvlc_googlenet, and resnet, and there could be places where things do not work if you use other networks. Please let us know if you encounter such issues and share .prototxt of the network you are using. We will try our best to support it as well. Eventually, our sparse CNN implementation should be general enough to handle all kinds of networks. We assume you have a recent Intel compiler and MKL installed. Tested environments: Intel compiler version 15.0.3.187 or newer. boost 1.59.0 . MKL 2017 or newer to use MKL-DNN. Direct sparse convolution and sparse fully-connected layers is only tested for AlexNet and GoogLeNet.

PYNQ-DL - Xilinx Deep Learning IP

  •    VHDL

After the setup, new Jupyter notebooks will be added under the pynqDL folder, ready to try out, no additional steps are needed.

pySpeechRev - This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of acoustic impulse responses

  •    Python

This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of acoustic impulse responses. where x[n] is the clean signal and * is the convolutional operator.

Image_Filter - Commonly used image filters. :earth_americas: 包罗常见的图像滤波器。

  •    Python

Update 13-05-2017: It's an image filter entirely written by myself. MIT license. Contributions welcome.

tensorflow-convolution-models

  •    Jupyter

This repo also contains a notebook that shows the result of the different steps in the convolutional architectures.