Hi and welcome back in this section, we'll take a look at single shot detectors, commonly known as
SS cities.
So let's get started.
So firstly, we just discussed to our CNN family and you've seen how they can be very successful.
However, they have a weakness, and their weakness is still the inference time is not optimal in hardware
and the best hardware back in 2017 2016.
It typically achieved roughly seven frames per second, and that was on a very powerful, expensive
hardware.
So it was infeasible to use that in real time systems.
So Estes aimed to improve the speed by eliminating one of two stages in typical object detectors, and
that's the region proposal network stage, hence to name a single shot.
So SS, these are one stage optical detector, whereas our CNN's with two stage and you can see summary
here how well SSD is performed in terms of map into view c 2007 image dataset.
It's getting seventy nine percent, beating our CNN's Here Faster CNN's dealers and getting roughly
more than twice their fees.
So you can see it's quite good and can see this is in input resolution that we use.
So even even use a much smaller input resolution, which allows it to be much faster and achieved a
much better score.
So that means that it's a very positive sign versus these.
So how do you assess these improve speed?
Well, these use multi scale features and default boxes, as well as dropping the resolution of the
images, all of which improve speed.
No cities do not resemble pixel pixels or features for bounding box hypotheses.
And and it's still as accurate as it pushes that do so.
Doesn't have any loss.
Doesn't take any loss in doing that in accuracy.
This allows these achieve real time speed, which is perfect for when doing video analytics with almost
no drop and sometimes even improved accuracy.
So let's take a look at the SSD structure.
But so SS.
These are composed of two main parts the feature map extractor and the researchers initially used.
VIDEO 16.
Although in future iterations of resonant mobile, not dense, not depending on application, they got
better results or faster results with mobile that.
And the second part is the convolutional filter for object detection.
So let's take a look at this now.
So SSD, these do something called the disguising feature maps, so it uses two biggies is con four
trillion and it makes four to six, which we can set it in.
So use a set parameter.
It makes 5:56 object predictions for each.
So.
So imagine the images split up into sounds like this and each so it makes tree was four to six different
predictions.
Here it predicts class scores and adds one extra one extra class if no object was found.
So if we were to use fewer sounds, as you can imagine here in this image, in the middle image here,
you'd be able to take a lot more granular objects.
However, you want to use larger cells, you would probably just detect bigger objects like a dog as
well here.
So it's a compromise you have to do depending on what performance requirements you have application.
So what we did?
So what researchers did was use PGD to extract feature maps, and for each bounding box, we obtain
the probabilities of all classes within that region.
This allows us to produce overlapping boxes where multiple objects occur.
So if you had a box like this table, there was a box puzzle here.
But now, for instance, there was a book proposal and the next box.
Here you can wear a cat with a dog was.
You can have boxes that overlap each other there.
So that's a good feature that's called a multi box.
So making multiple predictions for funding this possibly bounding that boundary, bounding boxes and
confident school is called multiverse.
So estes and training we use a few different lost functions.
We firstly use to class predictions for class collections.
We use a categorical cross entropy and for localization.
That means the funding box lost.
We effectively the localization loss.
We use something called smoothed L1 loss.
So Estes only penalizes predictions of positive matches and ignores the negative matches as we want
to get the best kind of positives as close to the ground truth as possible.
So let's take a look at assess these performance back in 2017.
You can see assesses with the red box here is doing probably the best in terms of overall map score
back on this dataset is beating you a little bit and two which was here, it's beating our CNN fast
RC events as well.
And Foster rc and here are the models, so it's doing quite well.
And you can see your time here.
This is of inference time.
It was pretty good as well.
So this probably used a bigger input image here to achieve this score.
So this is more of a demonstration of assess these potential and being a very accurate model as opposed
to being a fast model.
But you can see, given that its requirements were here in damages, its performance, its accuracy
was here.
It's quite good.
So the key takeaways from this lesson is that since these are faster than our afternoons but oftentimes
less accurate than detecting the smaller objects, accuracy increases if we increase the number of default
boxes so we can increase the density of the grid, as well as having more proposals or better design
boxes instead of four, six or even eight.
Multi-Skilled feature maps improved detection of varying skills.
It's faster than you'll listen to back in 2017 and as accurate as faster afternoons, perhaps even more,
actually.
It predicts categories of books offsets uses small convolutional filters applied to future maps, makes
predictions using the feature maps of different skills and for training requires that the ground data
is assigned to specific outputs in the fixed set of detector outputs.
It is slower but more accurate than you would typically, but that a lot has changed since then, and
it's faster but less accurate and faster.
RCA notes that when it actually is a bit debatable to be fair, but it's just one of the key takeaways
we have from this lesson.
So now we can stop there and we can move on to my favorite object detector, which is yellow, and I
have a few sections in yellow because it's quite cool and very useful, and we'll also be doing some
projects in Europe as well.
So stay tuned for our introduction to yellow object detectors.
Thank you.