Skip to content

Latest commit

 

History

History
206 lines (159 loc) · 6.66 KB

README.md

File metadata and controls

206 lines (159 loc) · 6.66 KB
Raw

AutoML Edge API

This packages provides a set of APIs to load and run models produced by AutoML Edge.

Installation

If you are using npm/yarn

npm i @tensorflow/tfjs-automl

If you are using CDN:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-automl"></script>

We support the following types of AutoML Edge models:

  1. Image classification
  2. Object detection

Image classification

AutoML Image classification model will output the following set of files:

  • model.json, the model topology
  • dict.txt, a newline-separated list of labels
  • One or more of *.bin files which hold the weights

Make sure you can access those files as static assets from your web app by serving them locally or on Google Cloud Storage.

Demo

The image classification demo lives in demo/img_classification. To run it:

cd demo/img_classification
yarn
yarn watch

This will start a local HTTP server on port 1234 that serves the demo.

Loading the model

import * as automl from '@tensorflow/tfjs-automl';
const modelUrl = 'model.json'; // URL to the model.json file.
const model = await automl.loadImageClassification(modelUrl);

If you do not want (or cannot) load the model over HTTP you can also load the model separately and directly use the constructor. This is particularly relevant for non-browser platforms.

The following pseudocode demonstrates this approach:

import * as automl from '@tensorflow/tfjs-automl';
import * as tf from '@tensorflow/tfjs';
// You can load the graph model using any IO handler
const graphModel = await tf.loadGraphModel(string|io.IOHandler); // a url or ioHandler instance
// You can load the dictionary using any api available to the platform
const dict = loadDictionary("path/to/dict.txt");
const model = new automl.ImageClassificationModel(graphModel, dict);

Making a prediction

The AutoML library takes care of any image preprocessing (normalize, resize, crop). The input img you provide can be HTMLImageElement, HTMLCanvasElement, HTMLVideoElement, ImageData or a 3D Tensor:

<img id="img" src="PATH_TO_IMAGE" />
const img = document.getElementById('img');
const options = {centerCrop: true};
const predictions = await model.classify(img, options);

options is optional and has the following properties:

  • centerCrop - Defaults to true. Since the ML model expects a square image, we need to resize. If true, the image will be cropped first to the center before resizing.

The result predictions is a sorted list of predicted labels and their probabilities:

[
  {label: "daisy", prob: 0.931},
  {label: "dandelion", prob: 0.027},
  {label: "roses", prob: 0.013},
  ...
]

Advanced usage

Advanced users can access the underlying GraphModel via model.graphModel. The GraphModel allows users to call lower level methods such as predict(), execute() and executeAsync() which return tensors.

model.dictionary gives you access to the ordered list of labels.

Object detection

AutoML Object detection model will output the following set of files:

  • model.json, the model topology
  • dict.txt, a newline-separated list of labels
  • One or more of *.bin files which hold the weights

Make sure you can access those files as static assets from your web app by serving them locally or on Google Cloud Storage.

Demo

The object detection demo lives in demo/object_detection. To run it:

cd demo/object_detection
yarn
yarn watch

This will start a local HTTP server on port 1234 that serves the demo.

Loading the model

import * as automl from '@tensorflow/tfjs-automl';
const modelUrl = 'model.json'; // URL to the model.json file.
const model = await automl.loadObjectDetection(modelUrl);

If you do not want (or cannot) load the model over HTTP you can also load the model separately and directly use the constructor. This is particularly relevant for non-browser platforms.

The following pseudocode demonstrates this approach:

import * as automl from '@tensorflow/tfjs-automl';
import * as tf from '@tensorflow/tfjs';
// You can load the graph model using any IO handler
const graphModel = await tf.loadGraphModel(string|io.IOHandler); // a url or ioHandler instance
// You can load the dictionary using any api available to the platform
const dict = readDictionary("path/to/dict.txt");
const model = new automl.ObjectDetectionModel(graphModel, dict);

Making a prediction

The AutoML library takes care of any image preprocessing (normalize, resize, crop). The input img you provide can be HTMLImageElement, HTMLCanvasElement, HTMLVideoElement, ImageData or a 3D Tensor:

<img id="img" src="PATH_TO_IMAGE" />
const img = document.getElementById('img');
const options = {score: 0.5, iou: 0.5, topk: 20};
const predictions = await model.detect(img, options);

options is optional and has the following properties:

  • score - Probability score between 0 and 1. Defaults to 0.5. Boxes with score lower than this threshold will be ignored.
  • topk - Only the topk most likely objects are returned. The actual number of objects might be less than this number.
  • iou - Intersection over union threshold. IoU is a metric between 0 and 1 used to measure the overlap of two boxes. The predicted boxes will not overlap more than the specified threshold.

The result predictions is a sorted list of predicted objects:

[
  {
    box: {
      left: 105.1,
      top: 22.2,
      width: 70.6,
      height: 55.7
    },
    label: "Tomato",
    score: 0.972
  },
  ...
]

Advanced usage

Advanced users can access the underlying GraphModel via model.graphModel. The GraphModel allows users to call lower level methods such as predict(), execute() and executeAsync() which return tensors.

model.dictionary gives you access to the ordered list of labels.