Hi guys.
Welcome to the new video tutorial.
In this video we will discuss is YOLO V8, the real state of the art and will present a comparison between
YOLO V8 and YOLO.
V7 Before we move ahead, just a quick overview of YOLO.
How does YOLO works?
And so Joseph Redmon, Santos, Santosh Daruwala, Ross Girshick and Ali Farhadi introduced YOLO, which
stands for You Only Look Once YOLO gained popularity because of its accuracy while maintaining a small
model size from version 1 to 4, YOLO was maintained in a C code in a custom deep learning framework
written by Redmond called Darknet.
In the last two years, YOLO are YOLO, YOLO, v6, Yolo V7 have emerged around the world out of their
own PyTorch based implementation.
Each model has bought new SOTA techniques that combine to push model accuracy and efficiency.
So let's move towards the objectives part.
Upon reading our objectives.
Or you can say that we have splitted the whole lecture into five major parts.
So the first part is or the first objective is we will see how it is better than previous versions of
YOLO.
In the next part I will present a comparison of YOLO V8 with YOLO.
V7 will see drink and license plate detection problem.
We will do the training time comparison mean average procedure comparison of YOLO V8 with YOLO V7 In
the third part I will do the result analysis which for the problem of license plate detection using
YOLO V8 and YOLO V7 In the next part we will see what improvements are made in the YOLO V8 version than
the previous versions.
And my observations are in the last regarding the YOLO V8 version.
So let's move towards the how YOLO V8 is better than previous versions of YOLO.
Well, you will be.
It introduces a new backbone network, which is Darknet 53, which is significantly faster and more
accurate than the previous backbone used in Yolov7 or in the others versions.
Darknet, 53, is a convolutional neural network that is 53 layers deep and can classify image into
up to 1000 object categories such as keyboard, mouse, pencil and many other categories as well.
Yolo V makes bounding box prediction similar to image segmentation which which is basically pixel wise.
To achieve this they have introduce anchor free detection had the concept of YOLO.
It has introduced the concept of anchor free detection head.
Your lobby.
It is more efficient than the previous version because of its because it uses a larger feature map and
a more efficient convolution neural network.
Okay, so let's move towards the next part.
So, your know, it also uses feature Pyramid networks, which helps to better recognize objects of
different sizes which improve its overall accuracy.
YOLO V8 introduces a user friendly API, allowing users to quickly and easily implement the model in
their applications.
Well, before recording this lecture, I have done a comparison of YOLO V8 with Yolov7 on a custom dataset,
which is license plate detection dataset.
So the dataset is available in roboflow.
I will share the link with as well and the comparison files of YOLO, V7 and YOLO V8 on considering
license plate detection dataset is also attached below this video tutorial.
You can check those files as well.
So the dataset I took is license plate detection dataset and I have done performance comparison of YOLO
V8 with YOLO.
V7 considering this license plate detection dataset.
So the dataset consists of 600 images for training and 64 images for the validation purpose and the
performance metrics, or the how we evaluate with both the models of YOLO, V8 and YOLO.
V7 is based on the training time, how much training time each model take to train for a considering
100 epochs and the performance analysis is done considering the mean average precision with IOU 50 and
IOU 50 to 95.
So the dataset had 601 images for training and 64 images for validation.
The number of epochs was set to 100 to see the performance of YOLO, V8 and Yolov7 model in warmup iteration
means at the starting iterations.
So let's see what the results do we get.
So here are the results I got after training and testing Yolov7 and YOLO V8 models on license plate
detection dataset.
So first I will present a training time comparison.
The training time taken taken by YOLO V8 model to train on 100 epochs is 48.12 minutes, while the training
time taken by YOLO V7 model to train on 100 epochs is 63.06 minutes.
So YOLO V8 trains in lesser time then use yolov7 on the same number of epochs.
So it means usually it takes less training time than YOLO V7, which is a very significant improvement
in YOLO V8.
While the mean average precision which is calculated at a threshold of IOU threshold of 0.5 is with
YOLO v eight gives us a 0.93, which means 93% of mean average precision value, while Yolov7 gives
us the value of 0.817, which is 81.7%.
So in this case as well, you know, we it also performs better than YOLO.
V7 So till now we have seen how the training time as well as for the mean average precision YOLO V8
outperforms YOLO V7.
The mean average precision value obtained when the IOU varies from 0.5 to 0.95.
In the case of zero eight, we got a value of 0.57, while in the case of YOLO V7, we got a value of
0.429.
So in the case of.
In this case as well.
YOLO V8 outperforms Yolov7.
So in all three cases which we have seen now.
YOLO V8 performs better than YOLO V7 so we can easily say that YOLO eight outperforms YOLO V7 and the
previous versions as well.
So here are the snapshots of results.
The CoLab file is also given below this video tutorial.
You can check it as well.
So from the results we can see that if I can show you over here that if you can see that, if this is
the mean average precision 5350 which we got with YOLO v eight is 0.93 and mean average precision with
varies from 50 to 95 is 0.57, while the training time taken is 0.802 hours, which is 48 minutes.
So this is the comparison of YOLO v eight or these are the results of YOLO v eight.
Now let us see the results of YOLO V7 The CoLab file is also attached below this video tutorial.
You can check it as well.
Hi guys.
This is the snapshot of the results which we got with YOLO.
V7 considering license plate detection problem.
So in the previous slide we see the results of V8 model considering license plate detection problem.
In this slide I am discussing the result of Yolov7 model considering license plate detection problem.
So you can see over here the mean average precision value considering IOU of 0.5 we got is 0.817 with
YOLO V7 model.
While in the last slide we have seen that with YOLO V8, the mean average precision value which we got
is 0.93.
So YOLO being the case of YOLO, V7, we have a less value of mean average precision, while in the
case of YOLO V8, we got a better mean average precision than YOLO.
V7 So now let us see the value of mean average precision.
Considering IOU from 0.5 to 0.95 is 0.429.
You can see over here.
So in the case of YOLO V8, we got a mean average precision value of 0.57.
While in the case of YOLO V7 here our value is 0.429.
So the mean average precision considering IOU from 0.5 to 0.95 in the case of YOLO, V7 is less than
YOLO V8.
So in that case, in this case YOLO.
In the case of YOLO V8, we have a higher mean average precision considering IOU of 0.5 and 0.95, while
in the case of Europe V7, our mean average precision value is less than zero.
V8 while that in the case of YOLO V8, the training time taken by the model to train on unrolled epochs
is 48 minutes, while in this case, in the case of YOLO V7 the training time taken by the model to
train on 100 epochs is 1.051 hours, which is 63 minutes.
So YOLO V7 takes a 63 minutes to train on 100 epochs, while YOLO V8 model takes 48 minutes to train
on 100 epochs.
So training time is less in Yolo V8 than in YOLO.
V7 So in your case of YOLO V7, the model takes more training time than YOLO V8 so it means YOLO V8
outperforms YOLO V7 model in all aspects in the case in Europe.
It takes less training time than YOLO, V7 Yolo V3 It gives better mean average precision than YOLO
V7 as well.
The result analysis which we come up with is.
So it basically performs better than Yolov7 model.
YOLO V8, as we have discussed in previous slide, gives us better mean average precision values while
YOLO.
It takes less training time than YOLO V7.
So YOLO V8 gives us maximum value at the expense of reduced time for training and the improvement.
Wearing YOLO is the sensibility of YOLO.
V8 is an important characteristic.
It is created as a framework that works with all prior YOLO iteration makes it easier to switch between
them as so in YOLO V8, we can switch between other versions of YOLO as well.
We can switch with with YOLO V5.
We can also switch between YOLO V3 as well.
So YOLO V8 is the only version which allows us to switch between other versions of YOLO as well, so
we can do a performance comparison with each version as well.
So YOLO.
Because of this, YOLO V8 is the best option for those who wish to benefit from the most recent YOLO
technology while keeping their YOLO models functional.
And on my observation, which I have drawn after doing the comparison between YOLO and YOLO.
V7 So what does have come with the result is that YOLO it takes less training time than YOLO, V7 and
YOLO V8 gives us better mean average precision value then YOLO V7 So in case of YOLO V8, that trade
off between training time as precision is achieved more in YOLO V8 than all the other models of YOLO
and in case of YOLO V8, a new backbone network, a new anchor free detection head and a new loss function
make things much faster.
It blesses the training time and gives us the faster processing and give us the more better results
than all the previous versions of YOLO.
Thank you for watching this video tutorial.
See you all in the next video tutorial.
Till then, bye bye.