-: What is Google Vision?
So, this is one that a lot of people don't know about
because they're really focused on Bard and DALL-E
and all these kind of more famous AI tools.
But the Google Vision is actually
one of the first transformer models that I used,
and it's pretty powerful for image recognition.
It's available as an API in Google Cloud,
and here's an example of it on the right,
as something I did where I took an image
of a woman wearing fashionable clothes
and then just tagged the labels from that image.
It's recognized correctly that image has a hair in it
and it has a picture of her head, shoulder, eye.
It's flash photography, label, her sleeve, dress.
It's really useful for extracting out what's in an image.
And it has more than just the label.
So this is the same image uploaded to their online demo,
and you can see that it extracts the objects.
So it knows that there's a person in the image, for example.
The face, it can detect whether there is a face
and then also what the emotion of that face is.
There's also a few of the other kind of properties,
like dominant colors you can get,
which is pretty useful I think for categorization of images.
Crop hints as well, so it kind of gives you an idea
of if you need to crop that image, what should you focus on?
And then one which I've seen some people use
is the safety thing here.
This isn't tagged as an adult image, which is correct.
There's no violence in here, but it is racy.
It's very likely to be racy, right?
So you can use this to automatically detect
some not safe for work images.
Specific places I've used it, one was product images,
tagging what's in your product images
and then being able to filter as you get
a good sense of what types of product images
are converting, for example, on your e-commerce website.
Ad creative, this is some tagging
I ran across all of the creatives
in their Facebook ad account
so you can see what tags tend to perform better than others
and how many creatives have that tag.
So, with this example, we had 23 creatives that used,
that had the label of communication device
or 29 with electronic device,
so that's interesting in itself
to see what sort of patterns are appearing.
And then you can also use it
for not safe for work detection.
Another use case is just detecting if there are faces
or detecting if there are specific labels in an image
in order to correct for them.
AI is not particularly good with faces,
and you can use this to,
after you've generated an image with DALL-E, for example,
you could check if there's a face in it
and then regenerate if you don't want faces in your image.
Okay, Google Vision I think is really powerful.
It's based on the same transformer architecture as DALL-E,
but it's specifically trained
for this type of labeling and entity extraction,
but really useful for specific use cases.