This lecture presents an introduction to object segmentation using YOLO V8.
Let's first look at the objectives of this lecture.
We have divided this complete lecture into three parts.
In the first part, we will see how we can implement object segmentation using YOLO V8 in Google CoLab.
In the second part, we will see what are the different steps involved in training the YOLO V8 segmentation
model in your own custom dataset.
We will train the YOLO V8 segmentation model on potholes dataset in this section.
In the third part I will play the output video of the trained YOLO V8 segmentation model testing on
a video.
The YOLO V8 segmentation model is trained on the potholes dataset.
So let's look how to implement object segmentation using YOLO V8.
The YOLO V8 segmentation model is one of the fastest and most accurate models for real time instance
segmentation.
Segmentation goes a step further than object detection and involves identifying individual objects in
an image and segmenting them from the rest of the image.
And in the below you can see the three images which differentiates what is basically classification.
The other is object detection.
And the third one is segmentation.
So if you see these three images, you can know the difference between classification object detection
and the segmentation.
Implement segmentation using YOLO V8.
Just write this two lines of code which you are seeing in front of your screen on any of the ID and
pass input image or the video and the segmentation on the input image and video will be done.
To implement object segmentation using V8 on custom dataset.
Here are the different steps involved.
In the first step, we prepare the dataset.
In the second step, we fine tune the YOLO V8 segmentation on module V8 segmentation model on custom
dataset, so just fine tuning means training.
The YOLO segmentation model on the custom dataset which we have prepared.
In the third step, we validate the fine tuned YOLO V8 model and see what is the mean average precision
we got and how our model performs on random images and videos.
In the fourth step, we test the model on some sample images and videos and display those images and
videos as our outputs.
So here is the output video of the train segmentation model testing on a demo video.
That YOLO V8 segmentation model is trained on the potholes dataset and you can see that the model is
able to detect and do the segmentation of the potholes.
Thank you so much for watching this video tutorial.
See you all in the next video tutorial.
Till then, bye bye.