His directorial provides a brief introduction about YOLO V8.
We will first look at the list of contents which we will cover in this video tutorial.
So following are the list of contents or the objectives which we will cover in this video tutorial.
We have divided this video complete lecture into four parts starting from the first, which is what
is YOLO.
The second part, which we will covered in this video tutorial is what is YOLO V8.
In the third part we will discuss what are the key features of YOLO V8.
In the fourth part we will discuss what are the reasons or what are the benefits of using Adobe Air.
So starting from the first, which is what is YOLO?
So YOLO stands for you only look once.
YOLO is a family of computer vision model which was introduced by Joseph Redmon and those the Santosh
Devala, Ross Khurshid and Ali Farhadi introduced.
YOLO.
YOLO is gaining popularity among introduce.
Day by day because it is being.
Since its launch from 2015 till now, eight different versions of YOLO has been introduced.
So the YOLO has been gaining popularity because of its accuracy while maintaining a small model size.
YOLO model can be trained on a single GPU, which makes his assets, which makes it accessible to a
wide range of developers.
Since its launch in 2015 by Joseph Redmon, time by time, YOLO has been upgraded by the computer vision
community from versions 1 to 4.
YOLO was maintained in C code in a custom deep learning framework written by Redmond called Chakra.
In the last two years, YOLO.
YOLO.
Yolo v six Yolo V7 has average around the world out of their own PyTorch based implementation.
Each model has brought new SOTA techniques, new SOTA techniques that continue to push more like model
accuracy and efficiency.
So here we have learned the few reasons that why is YOLO gaining popularity?
One of the reason is that that YOLO has been in a continuous upgrade.
There is a lot of research has been done by the computer vision community members, members on YOLO
and YOLO has been upgraded after every few months like it has been a few months back that Yolov7 was
introduced.
And then now we have YOLO V8.
So after few months we might have the YOLO V9 So there is a continuous research plus.
Because of accuracy and maintaining a small model size YOLO model can be trained on a single GPU, so
that's make it accessible to a wide range of developers around the world.
So that is the other reason that YOLO is being gaining the popularity.
So now we will discuss what is YOLO V8.
So geology.
It is the latest version of YOLO which was released on January 10th, 2023.
So YOLO V8 outperforms all the other versions of YOLO in terms of accuracy, speed and at training time.
So generally in YOLO V8, that mean average precision on which basically we judge a YOLO model mean
average precision of 53.7 has been marked by YOLO V8, which is highest ever in the YOLO history.
So YOLO is the new state of the art model that can be used for object detection, image classification
and also, for instance, segmentation tasks as in YOLO.
Here the issue of prolonged training is what is fixed as well.
Like I have done comparison of YOLO V8 with Yolov7 pose using license plate detection dataset.
And what are my chances are that in comparison to V8 in comparison to Yolov7 YOLO V8 takes less training
time, less YOLO V8 is more accurate than Yolov7, so Yolov7 takes more training time and it is less
accurate while YOLO v8 take less training time and it is more accurate than YOLO.
V7 okay, so the trade off between training time and precision is somewhat achieved in YOLO V8 as well.
So here are the key features of using YOLO V8.
So now let's look at some of the key features of using YOLO V8.
The key feature of YOLO V8 is its access extensibility.
YOLO V8 is basically designed as a framework that support all the previous version of YOLO.
So make it easier to switch between different versions and compare their performance.
So I can switch with YOLO v3.
YOLO V5 and compare the performances of YOLO V8 with Yolov5 and YOLO V3.
YOLO V8 includes a new backbone network which includes a series of convolution layer and also you.
It introduces a new anchor for detection head, which makes the YOLO V8 more accurate than previous
version.
YOLO V8 is highly efficient and can run a variety of platforms on CPU and GPU as well.
And while the V7 can also be run on GPU and VR, it can also be run on CPU and Yolov7 can also be run
on CPU and GPU.
And while we can get good performances or result and fastest training time on GPU as you all know as
well.
So basically anchor free detections make YOLO V8 more accurate than previous version version.
So as the Ultralytics doc says, V8 is a cutting edge state of the art model.
YOLO V8 introduces new features and improved improvements to further boost performance as well as the
reliability.
So YOLO.
It uses anchor free detection and new convolution layers to make predictions more accurate.
So anchor free detection makes your loving aid model more accurate and faster than previous version.
So in YOLO, we aired the issue of prolonged training time is one fixed?
Whereas as I told you previously, the trade off between training time and precision is achieved in
YOLO V8.
I have told you in the previous slide as well, like how we did it basically by considering the license
plate detection dataset.
We check it and New Backbone Network, which includes a series of convolution layers and a new anchor
for detection head and a new loss function make YOLO V8 much faster.
So these are the reasons in YOLO.
YOLO is basically anchor free.
Yolo V here does not predict based on bounding box anchors, which is what the previous models used
to do.
So Coco dataset is the benchmark dataset that people use.
We use for object and object detection on.
So it has been validated on the Coco dataset.
Here are what are the reasons or what are the benefits of using YOLO?
V eight.
So here I listed a few reasons that why I should use YOLO V8 or you should use YOLO V8 for your computer
vision project.
YOLO V8 has a high rate of accuracy measured by Coco and Roboflow for YOLO.
V8 outperforms all other YOLO models in terms of speed and accuracy means it achieves a mean average
precision of 53.7.
Yolo V8 can be installed in two ways from the source, like by coding the GitHub repo or by PIP by writing
PIP install Ultralytics, which is the first iteration of YOLO to have an official package like PIP
install Ultralytics.
So New Backbone Network which includes series of convolution layer, new anchor projection head and
a new loss function make YOLO V8 much faster, reliable and more precise and efficient.
YOLO V8 does not predict based on bonding of sucker anchors like I've already told you by the other
models used to do this.
So this is a brief introduction about YOLO V8.
Let's move towards the next tutorial.