Kevan SFDC: March 2021

“According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI."

Last year, I have written post about the OCR module of Einstein Vision, but did you know that other modules exist? Such as Image Classification and Object Detection.

In this blog post, I will talk about Einstein Vision Image Classification module. But wait, what is image classification, How does it works, where can I use it and how to implement it? I shall try to answer each one of them.

What is Image Classification?

Image classification in computer vision refers to a process that can classify an image according to its visual content. In Einstein Vision, you will be the one who will feed the images and for each image, you will also provide a label. It will process the images and try to identify one type of object in each image and assign the label.

How does it works?

For you to be able to use a Image Classification software (an AI is used), you will need a dataset. This is where you will gather several photos of an object and feed the software by telling it what is in the photo using a label.

Once you have sufficient photos and labels in your dataset, you just have to train it (don't worry, the software will perform this for you). The output is called a model.

It is against the model that prediction of an image is performed.

Where can it be used?

An example would be at an automatic Grocery store where user displays the product in front of a camera at checkout. The camera takes a picture of the product and then using Image classification software, it looks up from its trained dataset (model) to predict the product shown and retrieved relevant information such as label and price.

How to implement EV Image Classification?

To implement this module, you have several options:

Use Salesforce Einstein package on AppExchange (https://appexchange.salesforce.com/appxListingDetail?listingId=a0N3A00000Ed1V8UAJ)
Use third party AppExchange like SharinPix (https://appexchange.salesforce.com/appxListingDetail?listingId=a0N3000000Dq45IEAR)
Code everything from scratch
last option, I came up with an open source application which can train a model from scratch

Source code available here: https://github.com/kevanmoothien/einstein-app

Einstein Vision App

I will showcase the last option, which is a desktop application that I have implemented from scratch from my experience with Einstein Vision Image Classification.

Hang tight, thing are getting technical here.

First of all, it is an hybrid application developed on the Nodejs and Electron framework. Meaning, it will work on Linux, Windows and Mac.

I haven't published an executable version of the Application yet. So, you will have to build it by yourself. But don't worry, it isn't difficult at all (Instruction can also be found in the README.md file on the Github repository).

Here are what you need to get started (and as a developer, I believe you have all of them):

git
nodejs

Simply clone the repositoy locally using the following command line:

git clone https://github.com/kevanmoothien/einstein-app.git

Open your favorite terminal inside the einstein-app directory.

Install dependencies with npm:

npm install

Last, start the application:

npm start

Once the application starts, you will need to insert your Einstein Vision credentials. It is composed of an email address and a einstein_platform.pem file. Insert the email address and open the einstein_platform.pem file using your favorite text editor. Copy the content and paste it in the Einstein App. Sign up here if not already done: https://api.einstein.ai/signup

Einstein Credential configuration

You can now start creating projects for classifying images.

Let's create a new project name boardgame

Add a label and start uploading images for this label.

Uploading images for label monopoly

Once you have uploaded images for several labels (minimum 2 labels are required). You can Create a Dataset. When the dataset is created, it will upload all the images. You will see a green icon on each image which has been processed. There is also a progress bar.

Example Create Dataset

When upload has been completed. Click on View Dataset for this project. It will show you the status of the dataset which has been uploaded. From there, you will have a button Train Dataset. Clicking on it will create a model. Then click on View Model

Dataset status and Training

You will see Einstein Model progression. Once succeeded, you can proceed and test your model. Upload an image you want to predict, it will output the probabilities. In the example below, I have tested it against a monopoly image.