And welcome back to Lesson 25, where we take a look at using Estes and open TV now essences and Sun
for a single shot detectors which are a type of object detector that is quite similar to your which
you used before, they're not as good as you all.
In most cases, however, they are quite fast and they can be trained with a TensorFlow object detection
library and a few at a library, so it's quite simple and easy to use sometimes.
So let's look at our images here.
Notebook systems do work quite well on the CPU's as well, which is one advantage they have over you.
In my experience, using them on a video with open CVS, lower down and function here is designed and
function functions.
All right here.
It works quite well.
So the image size is quite small, though it resizes it to be 300 by 200 for the input for the processing.
These are the hardcoded skill factors here, and this is a model we point to the model here that we
downloaded.
So you can see it here.
This is a model fails the inference graph as well as the Peavey text, which does take a look at that
and see what it is.
This here is the model definition.
This is a bit bigger, so it's similar to the euro.
See if she follows just the model description so that when we load, it knows exactly what is what.
So these are things you would learn in the deep learning section of this course.
So for now, to worry about it, this model has been trained on 90 classes, one of the Cooku data sets
with 90 classes.
I believe so.
Yeah, mobile at Koko.
This one here.
So actually, this SSD is a mobile on that backbone SSD.
That's the one we're using here, which is quite fast.
It's one of the few models that works at maybe 10 frames a second on most cell phones.
So sorry for scrolling.
So these here.
So when you look the model, we defined class names here, so you can see all the costumes of the.
We have a dictionary.
It looks up the class name from the number we can image here and it goes into frame.
Then we just followed passage through the network and then we just get the rose cones.
So we get a square input.
So we just get the square center of the image that we pass.
And then we just plot the points here.
So similarly, like in the yellow one, we just get the detections out of it here and we just get the
confidence of the confidence is than a only to find teacher was defined to be at point five in this
case.
And we just loop through.
We do want to play non maximal suppression and mass in this one.
So let's take a look at the outputs.
Let's run this code.
It should be quite fast once we learn all the models in the model.
Sometimes it takes, sometimes takes the longest amount of time, and inference afterwards is just in
the milliseconds.
So this is the original image here.
These are the cops here, so you can definitely see it's not performing as good as usual, and the boxes
do have some weird overlap.
This so that's that.
That's a that's it for that image.
We can try out the images here.
Let's try another one, the shredded elephant, and see how that looks.
So again, we give it some time to run usually takes about 10 seconds.
Most of that time is spent in the models.
If you wanted, you can just loop through the model once and then run the inferences on the images afterward,
like what we did for you.
There we go.
So you can see it actually worked quite well with the elephant and high confidence that it is an elephant
so you can experiment with Estes.
As I said, I would use the yellow in most cases for other object vectors of colors, people, animals
or any of the Cooku Objects SSD, as I would use an SSD like a mobile net backbone SSD when the system
I'm running is on like a Raspberry Pi or some embedded system where inference speed.
Well, the competition.
You want it to be quite fast and efficient because that that was embedded systems on powerful.
So you would I would use those.
So thank you for that lesson.
I will now move on to colorizing black and white photos using a cafe model in Open City.
So stay tuned for that.
Thank you.