This project is the final project of this course on tensorflowjs.

Description

It captures images of rock/paper/scissors from webcam(you have to press corresponding buttons), then does training on these images and finally predicts one of rock/paper/scissors from your life webcam video stream. The more examples you show the better you get results.

Usage

Run main.html using a web server (for example, Web Server for Chrome)

Files

• main.html - the main file we run on browser: it loads tfjs, draws buttons/UI and runs js files
• webcam.js - a helper js file, which let’s us use webcam video stream
• dataset.js - a dataset class, which preprocesses and loads captured images into a dataset
• scripts.js - handles button functions, builds and trains the model, makes predictions.

Key insights:

• With tensorflowjs we can build deeplearing models, train and inference them right in the browser without sending requests to a server, which is cool for mostly speed and privacy reasons.

• Importing tensorflowjs is easy as adding one line in your html file:

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

• Defining and building the model is almost like in python:
  ```const model = tf.sequential();

   model.add(tf.layers.dense({units: 1, inputShape: [1]}));
   model.compile({loss:'meanSquaredError', 
                  optimizer:'sgd'});
   model.summary();

  ```

• Training and predicting are also intuitively “fit-predict”.

• To convert your python-trained model into tfjs model, assuming you have saved your model trained with python: model.save(saved_model_path), you just convert it using python tensorflowjs library(this will create model.json file and several binary files storing the weights of neural network):

  !tensorflowjs_converter --input_format=keras {saved_model_path} ./

• Use your saved model in js:

   const MODEL_URL = 'http://127.0.0.1:8887//path-to-you-newly-converted-model.json';
   const model = await tf.loadLayersModel(MODEL_URL)
   console.log(model.summary());
   const result = model.predict(input);

• To use pre-trained models(ex. mobilenet) we just import and use it:

  <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@1.0.0"> </script>  
  mobilenet.load().then(model => {
       model.classify(img).then(predictions => {
           console.log(predictions);})}

• There are a lot of cool pre-trained models: link