Search Results for "image processing framework"

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference,...

PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly...

Quantum Mechanical Toolkit And 3D Viewer for C++. Allows Data Visualisation via Images, Surfaces and Volume plots using OpenGL, as well as rapid development Quantum Mechanical Simulations. Uses the Blitz++, VTK Visualisation and Qt Libraries.

A Simple and Efficient Framework for Infinite Resolution Simulation; provides a Java Thread-like class that executes over simulated time. Time units are user-definable, no time quanta, and neither changes to the VM nor pre-processing are required.

The MuDiSP3 is a set of C++ classes acting as a framework for the execution of Digital Signal Processing (DSP) simulations. The entire simulated system is represented by a "System" class which inherits his properties from a general purpose DSP. The user c

Runes is a plugin-based framework that reverses the way of thinking about data processing: specify data accesses, and Runes will automatically choose an efficient representation for the data and execute the plugins to integrate it from different sources.