Search Results for "rapid"

PyMesh is a codebase developed by Qingnan Zhou for his PhD research at New York University. It is a rapid prototyping platform focused on geometry processing. PyMesh is written with both C++ and Python, where computational intensive functionalities are realized in C++, and Python is used for creating minimalistic and easy-to-use interfaces. PyMesh is a rapid prototyping platform focused on geometry processing. It provides a set of common mesh processing functionalities and interfaces with a number of state-of-the-art open-source packages to combine their power seamlessly under a single developing environment. ...

PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. ...

reacTIVision is a computer vision framework for the fast and robust tracking of markers attached on physical objects, and the creation of multi-touch surfaces. It was designed for the rapid development of table-based tangible user interfaces.

...The present release features GPU accelerated versions of all the background subtraction methods and morphological post processing of the object blobs with dilation and erosion filters, implemented in OpenCL. The framework was mainly designed as a toolkit for the rapid development of interactive art projects that explore dynamics of complex environments. The Scene GUI runs and compiles under Windows, Linux, and MacOS X, and is available in both 32 bit and 64 bit versions.

Graphene is a real-time dashboard and graphing toolkit built with D3.js and Backbone.js. It was originally designed to sit on top of the Graphite data model and API, enabling teams to quickly build live dashboards that show thousands of datapoints updating over time. The project provides a highly customizable and hackable client-side engine: you define “widgets” such as time series graphs, gauges or labels, hook them to data providers, and the toolkit handles ingestion, rendering and live...