Hello developers, and welcome back.
In this section we're going to build a very complex application called Custom Object Detection with
YOLO.
We're going to introduce YOLO to detect batteries.
We're going to train our model using Google Colab.
And we're going to download that data set from Roboflow.
We're going to introduce the Roboflow.
We're going to train our model, upload the images of our um of our object and label it, and then export
this, uh, this into our model.
And then using this data set with Google Colab in order to train our model and export our model as Tflite,
then we're going to move into the Android Studio and import our model and the required dependencies.
And it's this application is similar to what we've done before in the previous section.
So we're going to, uh, see the differences between the SSD mobile.
Net model and YOLO detection model.
Okay.
So we're going to do the updates for the codes.
We're going to make some difference and comparison between the both models.
We're going to learn more about YOLO object detection and we're going to introduce ultra Lytics.
The main idea behind this application is to pay attention for the model any model input and output shapes.
So we're going to understand the YOLO shapes, the input which is Float32 one 640 640 and three, and
the output is one five 8400.
And by the way, since we are only detecting one class, this is number five.
If you have more and more this number will be changed.
Okay.
So I want from you to focus with me and understand the main idea behind this application, which is
understanding and mastering input and output shapes for every model you get.
Okay, so this is the main point behind this application.
I promise you it will be a very, very interesting application that will increase your coding skills
and machine learning concepts.
Let's start building this amazing app.