Welcome back.
Congratulations.
We created our data set.
You can train the model.
Customer training.
Quick training.
Upload custom weights roboflow instant model.
But we are not going to use that.
The models that are are being created from Roboflow.
We need to create our custom and train our model using Python.
So I'm going to show you how to custom, how to create your custom object and how to create your custom
training, and how to download the data set from Roboflow and import it into our Google Colab and use
this data set to train our model.
And we're going to train it with YOLO models.
Okay, so go to virgins and download data set.
We had to select a format.
You can select a JSON, Coco, XML, Pascal, VOC if you are training with XML, txt yolo version 11,
12, nine, and so on CSV, others and ultra Linux Hub and other Microsoft Azure.
Okay, we in this section are interested in using YOLO version eight.
Nine.
11.
12.
And so on.
So we're going to train our model using YOLO.
Okay.
So what is YOLO.
We're going to discover in the next couple of minutes.
But here select YOLO version eight.
Don't use download zip to computer use.
Show download code because we're going to use Google Colab to import and download our our data set into
our Google Drive.
Okay.
So this is the code.
This is our data set.
We can copy this code and paste it in Google Colab to import and download our data set.
Let's go to Google Colab.
Create a new notebook.
And by the way, you can download or download those notebooks from the resources folder under this section.
Okay, so I'm gonna name it as YOLO Custom Object Detection okay.
Click enter and click save.
And as I told you you can find them in Python training and I'll name it as nine nine YOLO Custom Object
Training in order to distinguish it.
Okay.
So the file name is called YOLO Custom Object detection okay.
Now the first step is to download the data set from Roboflow.
In order to download it we need to install pip install Roboflow and from roboflow import Roboflow.
And here we need to paste this code okay.
So copy this code.
And by the way we have the code includes the pip install roboflow.
So there's no need to write this okay.
And you see that dataset is downloaded as version eight.
Before running this code, let's learn about YOLO.
YOLO stands for You Only Look Once.
It's a revolutionary object detection system that that treats detection as a single unified regression
problem.
Unlike older systems that look an image an image multiple times in different regions, YOLO looks at
the entire image just once to predict what objects are present and where they are.
It's very fast.
Think of it like like traditional methods.
For example, sliding window like scanning a page with a magnifying glass, checking every small spot
one by one to see if it contains an object.
This is slow and inefficient.
But when using YOLO, YOLO like glancing at the entire page of a split second of a split for a split
second and immediately telling you all the words and their locations.
This is incredibly fast.
Okay, so YOLO is very fast.
The input image is divided into a grid, for example 19 by 19 or s times s predict.
Each grid cell is responsible for predicting bounding boxes, confidence score and class probabilities.
Don't worry, we're going to learn about those in the next in the next couple of videos.
But here I want from you to understand that YOLO is very fast.
Looking at the image once and discover all the objects that are possible to be detected.
So YOLO is extremely fast because it's a single stage detector.
It can process videos in real time, 30 frames per second, enabling live object detection.
We're going to see a quick example about this, a high accuracy global context.
By looking at the whole image at once, it makes prediction with a better understanding of all of the
other overall scene, leading to a fewer false positives in the background.
Okay, YOLO is not a single model, but a family of models.
It has been continuously improved by the research community.
Versions like YOLO version.
Version two, three, four and so on have pushed the boundaries of speed and accuracy.
In short, YOLO look once approach made real time object detection practices, and it's very simple
and very fast for object detection, powering countless applications from self-driving cars to medical
imaging.
okay to use the the ultra lytics.
And and by the way, the ultra analytics is the company that invented the YOLO.
And here you can search on Google YOLO, go to Doc's dot com.
Or you can simply go to GitHub and search for ultra Lytics YOLO.
Okay, so this is the GitHub GitHub Ultralytic Ultralytic slash actions.
And you see that the YOLO okay.
So this is the ultra Lytics.
You can import it using pip install ultra Lytics.
And in order to use YOLO you can use from ultra import YOLO and load the pre-trained model.
This is a quick introduction about YOLO.
In the next video, we're gonna learn about how to use YOLO in order to train our data set from Roboflow
and start creating the model.
The custom model that will detect the batteries.