How to train a model in nodejs (tensorflow.js)?

First of all, the images needs to be converted to tensors. The first approach would be to create a tensor containing all the features (respectively a tensor containing all the labels). This should the way to go only if the dataset contains few images.

  const imageBuffer = await fs.readFile(feature_file);
  tensorFeature = tfnode.node.decodeImage(imageBuffer) // create a tensor for the image

  // create an array of all the features
  // by iterating over all the images
  tensorFeatures = tf.stack([tensorFeature, tensorFeature2, tensorFeature3])

The labels would be an array indicating the type of each image

 labelArray = [0, 1, 2] // maybe 0 for dog, 1 for cat and 2 for birds

One needs now to create a hot encoding of the labels

 tensorLabels = tf.oneHot(tf.tensor1d(labelArray, 'int32'), 3);

Once there is the tensors, one would need to create the model for training. Here is a simple model.

const model = tf.sequential();
model.add(tf.layers.conv2d({
  inputShape: [height, width, numberOfChannels], // numberOfChannels = 3 for colorful images and one otherwise
  filters: 32,
  kernelSize: 3,
  activation: 'relu',
}));
model.add(tf.layers.flatten());
model.add(tf.layers.dense({units: 3, activation: 'softmax'}));

Then the model can be trained

model.fit(tensorFeatures, tensorLabels)

If the dataset contains a lot of images, one would need to create a tfDataset instead. This answer discusses why.

const genFeatureTensor = image => {
      const imageBuffer = await fs.readFile(feature_file);
      return tfnode.node.decodeImage(imageBuffer)
}

const labelArray = indice => Array.from({length: numberOfClasses}, (_, k) => k === indice ? 1 : 0)

function* dataGenerator() {
  const numElements = numberOfImages;
  let index = 0;
  while (index < numFeatures) {
    const feature = genFeatureTensor(imagePath);
    const label = tf.tensor1d(labelArray(classImageIndex))
    index++;
    yield {xs: feature, ys: label};
  }
}

const ds = tf.data.generator(dataGenerator).batch(1) // specify an appropriate batchsize;

And use model.fitDataset(ds) to train the model

The above is for training in nodejs. To do such a processing in the browser, genFeatureTensor can be written as follow:

function loadImage(url){
  return new Promise((resolve, reject) => {
    const im = new Image()
        im.crossOrigin = 'anonymous'
        im.src="https://stackoverflow.com/questions/58953399/url"
        im.onload = () => {
          resolve(im)
        }
   })
}

genFeatureTensor = image => {
  const img = await loadImage(image);
  return tf.browser.fromPixels(image);
}

One word of caution is that doing heavy processing might block the main thread in the browser. This is where web workers come into play.