Showing 7 open source projects for "deep"

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  • 1
    MNN

    MNN

    MNN is a blazing fast, lightweight deep learning framework

    MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. 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. ...
    Downloads: 5 This Week
    Last Update:
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  • 2
    React Native Detour

    React Native Detour

    SDK for handling deferred deep links in React Native

    React Native Detour is an SDK for handling deferred deep links in React Native applications. It helps apps understand where users should land after installation, even when the original link was opened before the app existed on the device. This is useful for onboarding flows, referral campaigns, shared content links, invite systems, and marketing attribution. The SDK is designed to connect React Native apps with Detour credentials and platform-level integration.
    Downloads: 0 This Week
    Last Update:
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  • 3
    Avalonia

    Avalonia

    A cross-platform UI framework for .NET

    ...Avalonia is open source, free to use, and always will be. Clone the source today to dig deeper. The free VS extension provides everything needed to start, including a XAML previewer. JetBrains deep understanding of Avalonia shines through with rich Rider and R# support. Deploy to the platforms you care about with ease. Leverage existing knowledge, code and packages to kickstart your project. Get the support your project needs with one of our commercial support agreements. Help you can count on. JetBrains delivers huge productivity gains for Avalonia developers.
    Downloads: 19 This Week
    Last Update:
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  • 4
    pipeless

    pipeless

    A computer vision framework to create and deploy apps in minutes

    Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new...
    Downloads: 0 This Week
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  • 5
    MACE

    MACE

    Deep learning inference framework optimized for mobile platforms

    Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs.
    Downloads: 0 This Week
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  • 6
    TNN

    TNN

    Uniform deep learning inference framework for mobile

    TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's...
    Downloads: 0 This Week
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  • 7
    abu

    abu

    Abu quantitative trading system (stocks, options, futures, bitcoin)

    Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc.
    Downloads: 0 This Week
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