Showing 8 open source projects for "facebook tools"

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  • 1
    scalajs-react

    scalajs-react

    Facebook's React on Scala.JS

    Scala.js React wraps Facebook React for Scala.js with a strong emphasis on type safety and functional idioms. It provides a typed virtual DOM interface, reusable components, hooks, and utilities for routing, testing, SSR, and performance profiling, all aligned with Cats, Cats Effect, and Monocle ecosystems.
    Downloads: 3 This Week
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  • 2
    Graphene

    Graphene

    GraphQL in Python Made Easy

    ...Instead of writing GraphQL Schema Definition Langauge (SDL), Python code is written to describe the data provided by your server. Graphene helps you use GraphQL effortlessly in Python, but what is GraphQL? GraphQL is a data query language developed internally by Facebook as an alternative to REST and ad-hoc webservice architectures. With Graphene you have all the tools you need to implement a GraphQL API in Python, with multiple integrations with different frameworks including Django, SQLAlchemy and Google App Engine.
    Downloads: 0 This Week
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  • 3
    Material Tailwind

    Material Tailwind

    Easy-to-use components library for Tailwind CSS and Material Design

    Material Tailwind is an open-source library that uses the power of Tailwind CSS and React to help you build unique web projects faster and easier. The stunning design inspired by Material Design is a bonus. Enhance your workflow with seamless integrations for your favorite tools using Material Tailwind.
    Downloads: 0 This Week
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  • 4
    fvcore

    fvcore

    Collection of common code shared among different research projects

    fvcore is a lightweight utility library that factors out common performance-minded components used across Facebook/Meta computer-vision codebases. It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A...
    Downloads: 1 This Week
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  • 5
    Shumai

    Shumai

    Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun

    Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers.
    Downloads: 2 This Week
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  • 6
    fastMRI

    fastMRI

    A large open dataset + tools to speed up MRI scans using ML

    fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data...
    Downloads: 1 This Week
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  • 7
    Flashlight library

    Flashlight library

    A C++ standalone library for machine learning

    ...Both the CUDA and CPU backends are supported with vcpkg. For either backend, first, install Intel MKL. Flashlight app binaries are also built for the selected features and are installed into the vcpkg install tree's tools directory.
    Downloads: 1 This Week
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  • 8
    SVoice (Speech Voice Separation)

    SVoice (Speech Voice Separation)

    We provide a PyTorch implementation of the paper Voice Separation

    SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple...
    Downloads: 1 This Week
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