project_path: /lite/_project.yaml
book_path: /lite/_book.yaml
description: <!--no description-->
landing_page:
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  rows:
  - heading: TensorFlow Lite is for mobile and embedded devices.
    description: >
      <p style="max-width: 75%;">
        TensorFlow Lite is the official solution for running machine learning
        models on mobile and embedded devices. It enables on&#8209;device machine
        learning inference with low latency and a small binary size on Android,
        iOS, and other operating systems.
      </p>
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  - classname: tfo-landing-row-heading tfo-landing-row-heading-list
    heading: Many benefits
    description: >
      On-device ML inference is difficult because of the many constraints—TensorFlow Lite can solve these:
    items:
    - list:
      - heading: Performance
        description: >
          TF Lite is fast with no noticeable accuracy loss—see the <a href="./performance">metrics</a>.
        icon:
          icon_name: lens
          foreground: theme
      - heading: Portability
        description: >
          <a href="https://developer.android.com/ndk/guides/neuralnetworks/" class="external">Android</a>,
          iOS, and more specialized IoT devices.
        icon:
          icon_name: lens
          foreground: theme
    - list:
      - heading: Low latency
        description: >
          Optimized float- and fixed-point CPU kernels, op&#8209;fusing, and more.
        icon:
          icon_name: lens
          foreground: theme
      - heading: Acceleration
        description: >
          Integration with GPU and internal/external accelerators.
        icon:
          icon_name: lens
          foreground: theme
    - list:
      - heading: Small model size
        description: >
          Controlled dependencies, <a href="https://medium.com/tensorflow/introducing-the-model-optimization-toolkit-for-tensorflow-254aca1ba0a3" class="external">quantization</a>,
          and op&nbsp;registration.
        icon:
          icon_name: lens
          foreground: theme
      - heading: Tooling
        description: >
          Conversion, compression, benchmarking, power-consumption, and more.
        icon:
          icon_name: lens
          foreground: theme

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    heading: Companies using TensorFlow Lite
    items:
    - custom_image:
        path: ./images/landing-page/photos_logo.png
      path: https://www.photos.google.com
    - custom_image:
        path: ./images/landing-page/gboard_logo.png
      path: https://play.google.com/store/apps/details?id=com.google.android.inputmethod.latin&hl=en_US
    - custom_image:
        path: ./images/landing-page/gmail_logo.png
      path: https://www.google.com/gmail/
    - custom_image:
        path: ./images/landing-page/assistant_logo.png
      path: https://assistant.google.com/

  - classname: devsite-landing-row-logos
    items:
    - custom_image:
        path: ./images/landing-page/vsco_logo.png
      path: https://vsco.co
    - custom_image:
        path: ./images/landing-page/shazam_logo.png
      path: https://www.shazam.com/
    - custom_image:
        path: ./images/landing-page/nest_logo.png
      path: https://nest.com/    
    - custom_image:
        path: ./images/landing-page/loseit_logo.png
      path: https://www.loseit.com/

  - classname: devsite-landing-row-no-image-background devsite-landing-row-67
    background: grey
    items:
    - description: >
        <em>“TensorFlow Lite helped us introduce machine learning and AI into our
        app in an easy and streamlined way. We could reduce the size of our
        models while keeping the accuracy high. This helped us create an amazing
        fishing experience for our users by allowing them to identify any fish
        species with just a photo.”</em>
      image_path: ./images/landing-page/fishbrain_logo_big.png

  - heading: How it works
    items:
    - heading: Build
      icon:
        icon_name: build
      description: >
        Build a new model or retrain an existing one, such as using transfer learning.
      buttons:
      - label: Read the developer guide
        path: /lite/devguide
        classname: button button-primary tfo-button-primary
    - heading: Convert
      icon:
        icon_name: autorenew
      description: >
        Convert a TensorFlow model into a compressed flat buffer with the
        TensorFlow Lite Optimizing Converter (TOCO).
      buttons:
      - label: Read the TOCO guide
        path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/g3doc/python_api.md
        classname: button button-primary tfo-button-primary
    - heading: Deploy
      icon:
        icon_name: bolt
      description: >
        Take the compressed <code>.tflite</code> file and load it into a mobile
        or embedded device.<br/>
        See the <a href="#build-your-first-tensorflow-lite-app">tutorials below</a> to build an app.

  - heading: Build your first TensorFlow Lite app
    background: grey
    items:
    - classname: tfo-landing-row-item-inset-white
      heading: Get started
      description: >
        <ul>
          <li>Beginner: <a href="https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/" class="external">TensorFlow for Poets</a></li>
          <li>Beginner: <a href="https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/" class="external">TensorFlow for Poets 2: Android</a></li>
          <li>Beginner: <a href="https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-ios/" class="external">TensorFlow for Poets 2: iOS </a></li>
          <li>Intermediate: <a href="https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193" class="external">Object detection tutorial</a>
        </ul>
    - classname: tfo-landing-row-item-inset-white
      heading: Share your TensorFlow Lite story
      description: >
        We love to hear what you're working on—it may even get highlighted on
        our social media! <a href="https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss" class="external">Tell us</a>.

  - classname: devsite-landing-row-no-image-background devsite-landing-row-67
    items:
    - description: >
        <p>
          <em>“The release of TensorFlow Lite has allowed us to deploy an engaging
          real-time experience to our users that eliminates the requirement
          for a data connection. TensorFlow Lite’s ability to compress and
          optimize the TensorFlow graph for mobile deployment has been
          transformative in expanding the capabilities of Snap It.</em>
        </p>
        <p>
          <em>Through TensorFlow Lite, our users can now enjoy a state of the
          art, computer-vision-based food logging experience without worrying
          about signal strength. We look forward to future collaborations
          with the TensorFlow Lite team.”</em>
        </p>
      image_path: ./images/landing-page/loseit_logo_big.png

  - classname: devsite-landing-row-cards
    background: grey
    heading: Updates
    items:
    - heading: Introducing the Model Optimization Toolkit
      image_path: /ecosystem/images/tf-logo-card-16x9.png
      path: https://medium.com/tensorflow/introducing-the-model-optimization-toolkit-for-tensorflow-254aca1ba0a3
      buttons:
      - label: Read on TensorFlow blog
        path: https://medium.com/tensorflow/introducing-the-model-optimization-toolkit-for-tensorflow-254aca1ba0a3
    - heading: East Africa Cassava App
      image_path: ./images/landing-page/detect_crop_disease_in_africa.png
      path: https://heartbeat.fritz.ai/community-spotlight-nuru-a-mobile-app-by-plantvillage-to-detect-crop-disease-in-africa-28d142bf63d5
      buttons:
      - label: Read more
        path: https://heartbeat.fritz.ai/community-spotlight-nuru-a-mobile-app-by-plantvillage-to-detect-crop-disease-in-africa-28d142bf63d5
    - heading: Using TensorFlow Lite on Android
      image_path: /ecosystem/images/tf-logo-card-16x9.png
      path: https://medium.com/tensorflow/using-tensorflow-lite-on-android-9bbc9cb7d69d
      buttons:
      - label: Read on TensorFlow blog
        path: https://medium.com/tensorflow/using-tensorflow-lite-on-android-9bbc9cb7d69d

  - classname: devsite-landing-row-cards
    background: grey
    items:
    - heading: TensorFlow Lite at the Dev Summit
      youtube_id: FAMfy7izB6A
      buttons:
      - label: Watch the video
        path: https://www.youtube.com/watch?v=FAMfy7izB6A
    - heading: TensorFlow Lite on GitHub
      image_path: /ecosystem/images/github-card-16x9.png
      path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite
      buttons:
      - label: View on GitHub
        path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite
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