Mq-mqi-nodejs - Calling IBM MQ from Node.js - a JavaScript MQI wrapper GoodbyeDPI - GoodbyeDPI-Passive Deep Packet Inspection blocker and Active DPI circumvention utility (for Windows) NBA_Predictions - Reworked NBA Predictions (in Python)
React-sinatra - React on Sinatra Integration, Server Side Rendering The first 90% of the dataset is used for training (171000 patches), while the last 10% is used for validation (19000 patches).ĬoD-SCZ-FoV-Changer - A non-multiplayer field of view changer for various Call of Duty games which also works for online co-op different patches may contain same part of the original images, no further data augmentation is performed. A set of 190000 patches is obtained by randomly extracting 9500 patches in each of the 20 DRIVE training images.
Also the patches partially or completely outside the Field Of View (FOV) are selected, in this way the neural network learns how to discriminate the FOV border from blood vessels. Each patch, of dimension 48x48, is obtained by randomly selecting its center inside the full image. The training of the neural network is performed on sub-images (patches) of the pre-processed full images. Also on the STARE datasets, this method reports one of the best performances. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. The neural network structure is derived from the U-Net architecture, described in this paper. This is a binary classification task: the neural network predicts if each pixel in the fundus image is either a vessel or not. This repository contains the implementation of a convolutional neural network used to segment blood vessels in retina fundus images. Retina-unet - Retina blood vessel segmentation with a convolutional neural network