MobileNet

MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.

MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

This TensorFlow.js model does not require you to know about machine learning. It can take as input any browser-based image elements (<img>, <video>, <canvas> elements, for example) and returns an array of most likely predictions and their confidences.

For more information about MobileNet, check out this readme in tensorflow/models.

Usage

There are two main ways to get this model in your JavaScript project: via script tags or by installing it from NPM and using a build tool like Parcel, WebPack, or Rollup.

via Script Tag

<!-- Load TensorFlow.js. This is required to use MobileNet. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.1"> </script>
<!-- Load the MobileNet model. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@1.0.0"> </script>

<!-- Replace this with your image. Make sure CORS settings allow reading the image! -->
<img id="img" src="cat.jpg"></img>

<!-- Place your code in the script tag below. You can also use an external .js file -->
<script>
  // Notice there is no 'import' statement. 'mobilenet' and 'tf' is
  // available on the index-page because of the script tag above.

  const img = document.getElementById('img');

  // Load the model.
  mobilenet.load().then(model => {
    // Classify the image.
    model.classify(img).then(predictions => {
      console.log('Predictions: ');
      console.log(predictions);
    });
  });
</script>

via NPM

// Note: you do not need to import @tensorflow/tfjs here.

const mobilenet = require('@tensorflow-models/mobilenet');

const img = document.getElementById('img');

// Load the model.
const model = await mobilenet.load();

// Classify the image.
const predictions = await model.classify(img);

console.log('Predictions: ');
console.log(predictions);

API

Loading the model

mobilenet is the module name, which is automatically included when you use the <script src> method. When using ES6 imports, mobilenet is the module.

mobilenet.load({
    version: 1,
    alpha?: 0.25 | .50 | .75 | 1.0,
    modelUrl?: string
    inputRange?: [number, number]
  }
)

For users of previous versions (1.0.x), the API is:

mobilenet.load(
    version?: 1,
    alpha?: 0.25 | .50 | .75 | 1.0
)

Args:

• version: The MobileNet version number. Use 1 for MobileNetV1, and 2 for MobileNetV2. Defaults to 1.
• alpha: Controls the width of the network, trading accuracy for performance. A smaller alpha decreases accuracy and increases performance. 0.25 is only available for V1. Defaults to 1.0.
• modelUrl: Optional param for specifying the custom model url or tf.io.IOHandler object. Returns a model object.
• inputRange: Optional param specifying the pixel value range expected by the trained model hosted at the modelUrl. This is typically [0, 1] or [-1, 1].

mobilenet is the module name, which is automatically included when you use the