The Daily Dispatch #6, Let's clean this up, Part 2
Einstein Vision includes these APIs:
Einstein Image Classification—Train deep learning models to recognize and classify images at scale.
Einstein Object Detection—Train models to recognize and count multiple distinct objects within an image, providing granular details like the size and location of each object.
Einstein OCR (Optical Character Recognition)—Use OCR models to detect alphanumeric text in an image or PDF.
It seems I will need the first two.
Image Classification: To work off of a sufficiently large store of sample images - of plastic bottles, furniture, cans, cigarette buds, etc.
Einstein Object Detection: From a pile of trash, be able to identify the distinct items
You can use pre-trained classifiers or train your own custom classifiers to solve unique use cases.
Image Classification works off of a combination of machine learning and deep learning.
I worked on re-factoring code for a very interesting application back in my University days. We ended up publishing a paper here.
Anyway, I remember a nice example to explain the different terms to you.
Imagine a conveyer belt which has a camera attached to it. Its function is to automatically characterise the different types of fish that are put inside it.
How can you train a machine learning algorithm for this classification?
Step 1: Training a model
Say, you would like the machine to identify 3 different types of fish - salmon, mackerel and atlantic bluefin tuna.
You put a salmon in the belt, let the camera analyse its different features and also tell the camera that this is a salmon.
Then, you put a mackerel in the belt, let the camera analyse its features and tell the camera that this is a mackerel.
Then, follow the same thing with the tuna.
You expect the camera to analyse the three different types of fish and create an internal picture of a generic salmon, a generic mackerel and a generic tuna.
This process is called training a machine learning model.
Step 2: Testing a model
Next, you expect to have trained the model enough with enough test data that now, when you put in a tuna, say, you now expect the camera to tell you that it is a tuna.
Hopefully that was a simple explanation.
Let’s come back to Salesforce Einstein. Next, we continue with some setup steps:
The first step to interact with the Einstein intelligence is to create an API key.
After generating an API key, we move on to creating a token.
Install XCode. (for the Mac)
Clicking preview on the VF page shows us an image of a frog and the result.
So, the stock code works!
As you can see, the model identifies 5 possibilities as to what this image could be, but the highest probability , with 82.6% - is a tree frog.