Hi, welcome back to the course in this lesson, we'll take a look at creating an object detector using
drone maritime shot footage.
This is taking pictures of boats near the coast and we're going to train.
Let's see the model.
It's the RCN and the faster our CNN model using the TensorFlow object detection API.
So open that book 48 here and let's get started so you can take a look closely at what the actual images
look like and the results you can see.
It's labelled dark, labeled bought accordingly.
Although this is probably not a boat, this is probably just as a dock.
This is an example of the detection on a sample image, and it gets it a bit wrong, at least according
to me, because I don't think this is a boat here.
I could be wrong.
So nevertheless, this is again another notable flaw notebook.
So let's begin.
So firstly, let's run this notebook.
We have to install TensorFlow Vision one, 1.1 five because apparently it has some incompatibilities
with the new TensorFlow version two point seven we believe we're on.
However, the major fixed on my note, I know some of it works with efficient detect.
So maybe this it's been fixed by our standards as well.
So we'll just wait for that to finish.
OK, so it's finished, and it took just under a minute.
Don't worry about these errors.
Everything will still work fine.
So no, just set up some config parameters here.
We're going to be training a faster our CNN on the inception of Vision two models.
Let's let that model here.
So let's do this now.
Let's clone the repo and set some parts there.
Now we have to install some of the required packages to support this framework.
A lot of these, I know, do seem a bit difficult to setup.
However, you don't always I mean, you have to do it every time you run a call up notebook, unfortunately.
However, if you were to do this in the local system, it's a one time thing and then you can just drop
in your data sets and run Python Tree the UI and you can you can manage with that.
Anyway, let's wait for this to finish install.
OK, so we're finished now.
So now we can prepare our ETF record files here, so let's just navigate into a directory, unzip our
dataset and download a nonsupport dataset, I should say.
There we go.
That's finished.
Let's take a look at the list of files there.
So yet still, check out the tree in the directory and stick with the test directory and let's set the
parts.
Now we done a little base model.
That's a pre-trained model, and now we just do some checks.
Everything is running.
Now we can start configuring the training pipeline, so let's run these blocks of crude and take a look
at our pipeline as well.
This is like a configuration or training schedule or scheme scheme.
I think if you want to think about it like that and know we can almost begin training this have to run
all intensive water.
You don't have to do this, so we can skip that for now.
And now we can just run the model.
Actually, let's install the pensively just because I know it does work, and it's quite good to visualize
the results while we're training.
Install this there.
And now finally, we are ready to train this model.
So let's wait.
And there we go so we can start training now.
So let's take a look at the output so you can double check that you're getting everything I'm getting
here.
A lot of warnings, lots of hair.
OK, there we go.
So it's finally starting to train, you can see it's OK, but the loss in and disturbed equals zero.
It's the first one so we can see it.
It'll take a while.
I don't know how long this one takes a train, but generally will take maybe one or two, maybe maybe
more training.
Actually, this is a small dataset, so it shouldn't take more than an hour to train this model.
So anyway, why latrines?
We can take a look and see how the inference looks with this model.
So let's see if we can get a final output somewhere here.
Yep, there we go.
So these are some final image outputs of the detections from a model you can see it's not that good,
although some of them are right.
It's not always this.
It is a key category is that it's different categories, so you can see it that maybe the analysis needs
to be set at a higher threshold here, but generally consists of looking fairly well, just not too
low.
So that's it for this lesson in the next lesson will be training a chest piece detector using a PyTorch
implementation of your low inflation tree.
So stay tuned for that lesson, and I'll tell you why you will want to train sometime.
All the models such as your vision tree as well.
Thank you.
But.