Link:  https://bagisto.com/en/how-bagisto-utilized-tensorflow-js-in-production/

Traditionally users would need to use text searches to find products they desire, even if they had an image of the item they wanted to hand.

laravel bagisto

Now we introduce a wonderful catalog search in Bagisto by providing native support for ML features to the customers through a seamless search experience powered by TensorFlow.js.

Our purpose of building this functionality is to provide a more seamless search experience to our customers without using text or writing product names and simply by uploading an image.

A good image match can even increase your chances of successfully converting a prospective buyer. The main focus of our development was to maximize the accuracy of the result to capture maximum user interest with less computing power.

For this, we are using the MobileNetV2 which is a convolutional neural network that is 53 layers deep and we greatly reduced the number of operations in the network at only a slight performance cost.

With a strong neural network in place, we provide more than 80% accuracy to the users.

Even better, by combining this with TensorFlow.js, the customer will also find some automatically extracted keywords from the product description.

Tensorflow.js is an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API.