Showing 5 open source projects for "framework-arduinoststm32"

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    MMDeploy

    MMDeploy

    OpenMMLab Model Deployment Framework

    MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN...
    Downloads: 2 This Week
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  • 2
    GPUImage 2

    GPUImage 2

    Framework for GPU-accelerated video and image processing

    GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing...
    Downloads: 0 This Week
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  • 3
    Accord.NET Framework

    Accord.NET Framework

    Machine learning, computer vision, statistics and computing for .NET

    The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and extensive documentation and a wiki help fill in the details. The Accord.NET project provides...
    Downloads: 4 This Week
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  • 4
    HIPAcc

    HIPAcc

    Heterogeneous Image Processing Acceleration (HIPACC) Framework

    HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well...
    Downloads: 0 This Week
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  • 5
    C++ Software Framework based on wxWidgets for Developing Image Processing Algorithms
    Downloads: 1 This Week
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