Welcome back.
Ultralytic supports a wide range of YOLO models, from early versions like YOLO version three to the
latest YOLO version 11 and 12.
Okay, so there are a lot of ways you can use YOLO for detecting segmentation, classification, pose
detection, object detection, and tracking.
Okay, there are a lot of types and uh, there are a lot of models featuring 80 object classes.
So this is that pre-trained model.
The more these models are trained on the Coco data set, you remember that Coco data set that we talked
about in the previous videos.
And this Coco dataset contains 80 objects from person from uh, a car, truck and others.
Okay.
But we are interested in those models.
We have YOLO, YOLO 11 and N, S, M, L and x.
This is nano.
This is small size and can detect up to um.
If the speed is milliseconds and the parameters is point to point 6 million, the YOLO 11 s, which
is small, the size in pixels 640 by 640.
This is the image size.
And with the height, the width and height and medium is uh, bigger, large is is larger.
And Xlarge is the largest model you can use for Android.
It's recommended to use one of those N or S.
Okay.
So those are the models that we're going to use in our object detection with androids with Android applications.
Okay.
Now let's go back to our YOLO custom object detection file.
And here from Roboflow import or overflow.
This is the roboflow works or um repository can name it as repository and passing the API key and creating
an object from this roboflow.
Passing the API key the project get from Roboflow the workspace object detection.
By the way, don't share this API key with anyone.
Keep it secret with your.
But this is for used for demonstration, so don't worry.
You can, um, you can uh.
This is why I'm sharing with you the object detection.
And by the way, this is the API is different from yours.
Okay?
So don't use this API key.
Use your own API key.
As I showed you in the previous videos, the flow dot workspace object detection and the project is
this version.
Get from the project version number one and data set equals to version dot download.
You see guys I'm using YOLO version eight okay now click.
Run.
Open the folder.
You see we have only sample data.
Wait for collecting and downloading all the data set from here and you will see the newly created folder.
And here we go.
This is the newly created folder called Object Detection Android one.
If we go to our roboflow you see guys that um, that our object object detection, object detection
Android, this is our project.
It's named as object detection okay.
And the version if we go to versions, you see version one.
For this we get the, uh, this, um, this naming object detection Android one.
If we open it, we have many files data, dot, YAML, ui, ml.
This is the most important file that we're going to use.
And here we have three folders test train and valid Inside every folder there are two folders images
and labels.
If we go to the images, we see images, and if we go to the labels, we see the labeled ones and the
labels.
Okay dot, txt open the txt.
You see those are the bounding boxes of the detected object.
And if we go to the photos you see we have this photo for example.
This is another photo.
This is the uh the the images that we uploaded to our Roboflow images.
Those are the test.
You can see the train and labels also you see valid.
We have images and labels okay.
Those are very important.
We're going to use those images to train our model using Python and Google Colab with the help of YOLO
object detections okay.