@tensorflow-models/coco-ssd

[![Build Status](https://travis-ci.org/tensorflow/tfjs.svg?branch=master)](https://travis-ci.org/tensorflow/tfjs) # TensorFlow.js TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. **Develop ML in the Browser** <br/> Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API. **Run Existing models** <br/> Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser. **Retrain Existing models** <br/> Retrain pre-existing ML models using sensor data connected to the browser, or other client-side data. ## About this repo This repository contains the logic and scripts that combine two packages: - [TensorFlow.js Core](https://github.com/tensorflow/tfjs-core), a flexible low-level API, formerly known as *deeplearn.js*. - [TensorFlow.js Layers](https://github.com/tensorflow/tfjs-layers), a high-level API which implements functionality similar to [Keras](https://keras.io/). - [TensorFlow.js Converter](https://github.com/tensorflow/tfjs-converter), tools to import a TensorFlow SavedModel to TensorFlow.js If you care about bundle size, you can import those packages individually. If you are looking for Node.js support, check out the [TensorFlow.js Node repository](https://github.com/tensorflow/tfjs-node). ## Examples Check out our [examples repository](https://github.com/tensorflow/tfjs-examples) and our [tutorials](https://js.tensorflow.org/tutorials/). ## Gallery Be sure to check out [the gallery](GALLERY.md) of all projects related to TensorFlow.js. ## Pre-trained models Be sure to also check out our [models repository](https://github.com/tensorflow/tfjs-models) where we host pretrained models on NPM. ## Getting started There are two main ways to get TensorFlow.js in your JavaScript project: via <a href="https://developer.mozilla.org/en-US/docs/Learn/HTML/Howto/Use_JavaScript_within_a_webpage" target="_blank">script tags</a> <strong>or</strong> by installing it from <a href="https://www.npmjs.com/" target="_blank">NPM</a> and using a build tool like <a href="https://parceljs.org/" target="_blank">Parcel</a>, <a href="https://webpack.js.org/" target="_blank">WebPack</a>, or <a href="https://rollupjs.org/guide/en" target="_blank">Rollup</a>. ### via Script Tag Add the following code to an HTML file: ```html <html> <head>  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>  <script> // Notice there is no 'import' statement. 'tf' is available on the index-page // because of the script tag above. // Define a model for linear regression. const model = tf.sequential(); model.add(tf.layers.dense({units: 1, inputShape: [1]})); // Prepare the model for training: Specify the loss and the optimizer. model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); // Generate some synthetic data for training. const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]); const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]); // Train the model using the data. model.fit(xs, ys).then(() => { // Use the model to do inference on a data point the model hasn't seen before: // Open the browser devtools to see the output model.predict(tf.tensor2d([5], [1, 1])).print(); }); </script> </head> <body> </body> </html> ``` Open up that html file in your browser and the code should run! ### via NPM Add TensorFlow.js to your project using <a href="https://yarnpkg.com/en/" target="_blank">yarn</a> <em>or</em> <a href="https://docs.npmjs.com/cli/npm" target="_blank">npm</a>. <b>Note:</b> Because we use ES2017 syntax (such as `import`), this workflow assumes you are using a modern browser or a bundler/transpiler to convert your code to something older browsers understand. See our <a href='https://github.com/tensorflow/tfjs-examples' target="_blank">examples</a> to see how we use <a href="https://parceljs.org/" target="_blank">Parcel</a> to build our code. However you are free to use any build tool that you prefer. ```js import * as tf from '@tensorflow/tfjs'; // Define a model for linear regression. const model = tf.sequential(); model.add(tf.layers.dense({units: 1, inputShape: [1]})); // Prepare the model for training: Specify the loss and the optimizer. model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); // Generate some synthetic data for training. const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]); const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]); // Train the model using the data. model.fit(xs, ys).then(() => { // Use the model to do inference on a data point the model hasn't seen before: model.predict(tf.tensor2d([5], [1, 1])).print(); }); ``` See our <a href="https://js.tensorflow.org/tutorials/" target="_blank">tutorials</a>, <a href="https://github.com/tensorflow/tfjs-examples" target="_blank">examples</a> and <a href="https://js.tensorflow.org/api/latest/">documentation</a> for more details. ## Importing pre-trained models We support porting pre-trained models from: - [TensorFlow SavedModel](https://github.com/tensorflow/tfjs-converter) - [Keras](https://js.tensorflow.org/tutorials/import-keras.html) ## Find out more [TensorFlow.js](https://js.tensorflow.org) is a part of the [TensorFlow](https://www.tensorflow.org) ecosystem. For more info: - For help from the community, use [`tensorflow.js`](https://stackoverflow.com/questions/tagged/tensorflow.js) tag on Stack Overflow. - [js.tensorflow.org](https://js.tensorflow.org) - [Tutorials](https://js.tensorflow.org/tutorials) - [API reference](https://js.tensorflow.org/api/latest/) - [Discussion mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs) Thanks <a href="https://www.browserstack.com/">BrowserStack</a> for providing testing support.