Search Results for "ofn-extract-objects.py"

s6-overlay is an easy-to-install (just extract a tarball or two!) set of scripts and utilities allowing you to use existing Docker images while using s6 as a pid 1 for your container and process supervisor for your services.

Cross platform Neovim front-end UI, built with F# + Avalonia. Download the latest release package for your system, extract and run FVim! For Windows 7 / Vista / 8.1 / Server 2008 R2 / Server 2012 R2, use the win7-x64 package. For Windows 10, use the win-x64 package -- this version has faster startup. For macOS, it's packaged as an app bundle, unzip and drag it to your applications folder. Use a Windows FVim frontend with a WSL neovim: fvim, wsl. Multi-grid support, try Ctrl-w ge to detach a window into a separate OS window!

...Treat the jenkins_home directory as you would a database, in Docker you would generally put a database on a volume. If your volume is inside a container, you can use a Docker command to extract the data, or other options to find where the volume data is. Note that some symlinks on some OSes may be converted to copies (this can confuse jenkins with lastStableBuild links etc). You can define the number of executors on the Jenkins built-in node using a groovy script. By default it is set to 2 executors, but you can extend the image and change it to your desired number of executors.

COOK is a software build-automation tool made with a goal of letting you, the programmer, to utilize the most elegant and powerful programming language - Common Lisp - for managing your software builds. You write a recipe file which describes what objectives must be constructed. COOK will load and process this file, then produce either a regular Makefile or a Bourne Shell script, which will actually perform the task of building and installing your targets.

Please download the above zip file and extract it to a secret folder and run the quiz.sh script from that folder in your terminal program, as shown in the youtube videos : https://youtu.be/kQi0XlIaT9M and https://youtu.be/prwKN5DiFp0 You may change the contents of the quiz.txt file to your liking, while maintaining the format of it. Note: Press Enter/Return key after typing on your Terminal, to proceed to the next step, while testing the quiz.sh script.

cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. ...

Utility to validate, extract, show and create digital documents included in files with "pk7" and "fp7" extensions such as electronic bills provided by some phone companies in Spain like Movistar and Telefonica.