STT-tensorflow/tensorflow/lite/g3doc/tutorials/_index.yaml

book_path: /lite/_book.yaml
project_path: /lite/_project.yaml
title: Tutorials
landing_page:
  custom_css_path: /site-assets/css/style.css
  nav: left
  meta_tags:
  - name: description
    content: >
      TensorFlow Lite tutorials to help you get started with machine learning on Android, iOS,
      Raspberry Pi and IoT devices.      

  rows:
  # Pre-trained models
  - classname: devsite-landing-row-100
    items:
    - description: >
        <h2 class="tfo-landing-page-heading no-link">Getting Started</h2>
        TensorFlow Lite is an open-source deep learning framework to run TensorFlow models
        on-device. If you are new to TensorFlow Lite, we recommend that you first explore the
        <a href="/lite/models">pre-trained models</a> and run the example
        apps below on a real device to see what TensorFlow Lite can do.        

  - classname: devsite-landing-row-100
    items:
    - classname: tfo-landing-page-card
      description: >
        <a href="/lite/models/object_detection/overview">
          <h3 class="no-link">Object Detection</h3>
        </a>
        Detect objects in real time from a camera feed with a MobileNet model.        
      path: /lite/models/object_detection/overview
    - classname: tfo-landing-page-card
      description: >
        <a href="/lite/models/bert_qa/overview">
          <h3 class="no-link">Question and Answer</h3>
        </a>
        Answer any questions related to a given text with a MobileBERT model.        
      path: /lite/models/bert_qa/overview

  # Mobile developers
  - classname: devsite-landing-row-100
    items:
    - description: >
        <h3 class="tfo-landing-page-heading no-link">For mobile developers</h3>
        If you are a mobile developer without much experience with machine learning and
        TensorFlow, you can start by learning how to train a model and deploy to a
        mobile app with TensorFlow Lite Model Maker.        

  - classname: devsite-landing-row-100
    items:
    - classname: tfo-landing-page-card
      description: >
        <a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">
          <h3 class="no-link">Recognize flowers on Android</h3>
        </a>
        A quick start tutorial for Android. Train a flower classification model and deploy it to an
        Android application.        
      path: https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0
    - classname: tfo-landing-page-card
      description: >
        <a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-ios/#0">
          <h3 class="no-link">Recognize flowers on iOS</h3>
        </a>
        A quick start tutorial for iOS. Train a flower classification model and deploy it to an iOS
        application.        
      path: https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-ios/#0

  # Model creators
  - classname: devsite-landing-row-100
    items:
    - description: >
        <h3 class="tfo-landing-page-heading no-link">For model creators</h3>
        If you are already familiar with TensorFlow and interested in deploying to edge devices,
        then you can start with the below tutorial to learn how to convert a TensorFlow model to
        TensorFlow Lite format and optimize it for on-device inference.        

  - classname: devsite-landing-row-100
    items:
    - classname: tfo-landing-page-card
      description: >
        <a href="https://codelabs.developers.google.com/codelabs/digit-classifier-tflite/#0">
          <h3 class="no-link">Recognize handwritten digits</h3>
        </a>
        A quick start end-to-end tutorial on converting and optimizing a TensorFlow model for
        on-device inference, then deploy it to an Android app.        
      path: https://codelabs.developers.google.com/codelabs/digit-classifier-tflite/#0
    - classname: tfo-landing-page-card
      description: >
        <a href="/lite/tutorials/model_maker_image_classification">
          <h3 class="no-link">Transfer learning for image classification</h3>
        </a>
        Learn how to use TensorFlow Lite Model Maker to quickly create image classification models.        
      path: /lite/tutorials/model_maker_image_classification

  # IoT developers
  ## Linux-based IoT devices
  - classname: devsite-landing-row-100
    items:
    - description: >
        <h3 class="tfo-landing-page-heading no-link">For IoT developers</h3>
        If you are interested in deploying a TensorFlow model to Linux-based IoT devices such as
        Raspberry Pi, then you can try out these tutorials on how to implement computer vision tasks
        on IoT devices.        

  - classname: devsite-landing-row-100
    items:
    - classname: tfo-landing-page-card
      description: >
        <a href="https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/raspberry_pi/">
          <h3 class="no-link">Image classification on Raspberry Pi</h3>
        </a>
        Perform real-time image classification using images streamed from the Pi Camera.        
      path: https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/raspberry_pi/
    - classname: tfo-landing-page-card
      description: >
        <a href="https://github.com/tensorflow/examples/blob/master/lite/examples/object_detection/raspberry_pi/">
          <h3 class="no-link">Object Detection on Raspberry Pi</h3>
        </a>
        Perform real-time object detection using images streamed from the Pi Camera.        
      path: https://github.com/tensorflow/examples/blob/master/lite/examples/object_detection/raspberry_pi/
  ## Microcontrollers
  - classname: devsite-landing-row-100
    items:
    - description: >
        If you are interested in deploying a TensorFlow model to microcontrollers which are much
        more resource constrained, then you can start with these tutorials that demonstrate an
        end-to-end workflow from developing a TensorFlow model to converting to a TensorFlow Lite
        format and deploying to a microcontroller with TensorFlow Lite Micro.        

  - classname: devsite-landing-row-100
    items:
    - classname: tfo-landing-page-card
      description: >
        <a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">
          <h3 class="no-link">Hotword detection</h3>
        </a>
        Train a tiny speech model that can detect simple hotwords.        
      path: https://github.com/tensorflow/examples/blob/master/lite/examples/object_detection/raspberry_pi/
    - classname: tfo-landing-page-card
      description: >
        <a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">
          <h3 class="no-link">Gesture recognition</h3>
        </a>
        Train a model that can recognize different gestures using accelerometer data.        
      path: https://github.com/tensorflow/examples/blob/master/lite/examples/object_detection/raspberry_pi/


  # Next steps
  - classname: devsite-landing-row-100
    items:
    - description: >
        <h2 class="tfo-landing-page-heading no-link">Next steps</h2>
        <p>After you have familiarized yourself with the workflow of training a TensorFlow model,
        converting it to a TensorFlow Lite format, and deploying it to mobile apps, you can learn
        more about TensorFlow Lite with the below materials:</p>
        <ul>
          <li>
            Try out the different domain tutorials (e.g. vision, speech) from the left navigation
            bar. They show you how to train a model for a specific machine learning task, such as
            <a href="https://blog.tensorflow.org/2018/07/training-and-serving-realtime-mobile-object-detector-cloud-tpus.html">object detection</a>
            or
            <a href="/lite/tutorials/model_maker_text_classification">sentiment analysis</a>.
          </li>
          <li>
            Learn more about the development workflow in the TensorFlow Lite
            <a href="https://www.tensorflow.org/lite/guide">Guide</a>.
            You can find in-depth information about TensorFlow Lite features, such as
            <a href="https://www.tensorflow.org/lite/convert">model conversion</a>
            or
            <a href="https://www.tensorflow.org/lite/performance/model_optimization">model optimization</a>.
          </li>
          <li>
            Check out this free
            <a href="https://www.udacity.com/course/intro-to-tensorflow-lite--ud190">e-learning course</a>
            on TensorFlow Lite.
          </li>
        </ul>        

  # Blogs and videos
  - classname: devsite-landing-row-100
    items:
    - description: >
        <h2 class="tfo-landing-page-heading no-link">Blogs and videos</h2>
        <p>Subscribe to the
          <a href="https://blog.tensorflow.org/search?label=TensorFlow+Lite&amp;max-results=20">TensorFlow blog</a>,
          <a href="https://www.youtube.com/tensorflow">YouTube channel</a>,
          and <a href="https://twitter.com/tensorflow">Twitter</a> for the latest updates.
        </p>