Showing 4 open source projects for "image search algorithm"

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  • 1
    MNN

    MNN

    MNN is a blazing fast, lightweight deep learning framework

    ...At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices. ...
    Downloads: 1 This Week
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  • 2
    MegEngine

    MegEngine

    Easy-to-use deep learning framework with 3 key features

    MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. ...
    Downloads: 5 This Week
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  • 3
    Evolutionary Computation Framework

    Evolutionary Computation Framework

    C++ framework for application of any type of evolutionary computation.

    ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners.
    Downloads: 0 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 as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages.
    Downloads: 0 This Week
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