Wallaby nine is the new computer vision object detection model released by Chenyang Wang and his team
on 21st February 2024.
In this video tutorial, I will give you a detailed overview of the architectural improvements that
are introduced in YOLO V9 model on 21st February 2024.
Chenyang Wang and his team released a paper titled Yolo v9 Learning What You Want to Learn using programmable
Gradient Information.
In this paper, a new computer vision model architecture is introduced, which is YOLO v9.
The source code was made available on GitHub, allowing anyone to train their own YOLO v9 model so you
can fine tune or train the YOLO v9 model on any of their custom data set, and you can fine tune the
YOLO v9 model as per your requirements.
You can fine tune the YOLO v9 model on license plate data set, and you can do license plate detection
and recognition.
You can fine tune the YOLO v9 model on potholes data set, and you can do potholes detection.
You can also do pen book detection by fine tuning the YOLO v9 model on pen book data set.
And in this way, you can perform multiple, uh, tasks.
You can also do personal protective equipment detection by training or fine tuning the YOLO v9 model
on PPE data set.
So from the results provided in the paper.
The wallaby nine achieves higher mean average precision than existing models, which include Yolo,
V8, Yolov7, YOLO, Yolo v5 when benchmarked against the Mscoco validation data set.
So Yolo v nine paper introduces two new architectures YOLO v nine and Glenn.
So both of these uh YOLO v nine weights and Glenn architecture weights are available on the GitHub repository
of Yolo v nine.
You can try YOLO v nine weights and Glenn weights.
And you can do object detection on images, videos or on the live webcam feed as well.
So YOLO v nine introduces two new architectures, Yolo v nine and Glenn, and both of these models,
weights YOLO nine and Glenn weights are available on the YOLO v nine GitHub repository, and you can
use those weights and do object detection on images, videos, and on the live webcam feed going ahead.
Jinyao Wang and his team have also developed YOLO v4, Yolo R, and Yolov7, so YOLO R, Yolov7 and
YOLO v4 have also been developed by the Chen Yang Wang and his team, while Yolov5 and Yolo v eight
are developed by the Ultralytics.
You know, V9 is an advancement from Yolov7, as the previous version of YOLO model, Yolov7 was also
developed by the Shenyang Wang and his team.
So YOLO v9 is basically an advancement from Yolov7.
What, uh, what uh, basically improvements that is made from Yolov7 in YOLO v9 let's discuss those
in Yolov7.
A significant progress is made in terms of optimizing the training process.
That is why it is called trainable bag of freebies.
So if you read the title of the Yolov7 paper, so it is Yolov7 Trainable Bag of freebies and why we
called Yolov7 trainable bag of Freebies, because in yolov7, a significant improvement is made in terms
of optimizing the training process, which, uh, results in harnessing the training efficiency to boost
the object detection model accuracy without adding to the inference cost.
So in, uh, in yolov7 we have optimized the training process.
But the in yolov7 we does not the authors do not specifically address the problem of information loss.
So in Yolov7 the problem of information loss is not addressed, although they have optimized the training
process.
So Yolov7 does not specifically address the problem of information loss during the input data feed forward
process, which is a challenge known as information bottleneck.
So the information loss problem is basically a challenge which they have called as information bottleneck.
So the issue arises from downscaling operations in the network.
So we are the information of the problem of information loss occurs when we, uh perform downscaling
operations in the network.
And as a result of this, an important input data which we have passed to the model can be diluted or
removed.
So to address the issue of information loss, which we have called as information bottleneck, there
exists some solution.
So there are already some solutions which can solve the issue of information loss or information bottleneck,
which include reversible architectures, mask modeling and deep supervision.
So these solutions also help us to uh, uh, reduce the issue of information loss or information bottleneck.
But all of these solutions have some drawbacks by which we have to, uh, which can also result in,
uh, minimizing the accuracy of our object detection model.
So to overcome the issue of information loss information bottleneck, uh, new uh enhancement is being
introduced in the Yolo VI nine paper, which is.
Two different innovative approaches have been used in the following nine papers to address the issue
of information loss, which include programmable gradient information and the Generalized Efficient
Layer Aggregation network.
So two different approaches, which include programmable gradient information and the Generalized Efficient
Layer aggregation network is being used to tackle the information bottleneck or information loss problem
directly.
And if we solve this information bottleneck or information loss problem, this help us to improve the
accuracy and efficiency of object detection model further as well.
Doing the Yolov7 paper.
The authors have optimized the training process, and in the YOLO v9 they have solved the, uh, information
loss issue or information bottleneck issue by adding or introducing two different approaches, which
include programmable gradient information and the generalized efficient layer aggregation network.
So these two approaches will be used to tackle the information bottleneck or information loss problem.
And it will result in further increase of accuracy and efficiency of the object detection model.
So I have just given you a quick interview or a detailed interview about the Yolov5 model.
So what advancement and inaccuracy or architecture are made in Yolov5 nine, and how, uh, how Yolov5
nine outperforms other models?
Let us see that as well.
So Eulabee nine introduces architecture enhancement, which sets your V9 apart from the other object
detection models.
So.
Let's look at what architectural improvements that are made in YOLO v9.
So YOLO v9 incorporates advancements like programmable gradient formation and the Generalized Efficient
Layer aggregation network.
So these two architecture improvements are made in YOLO.
V9 one is programmable gradient information and the other is generalized efficient layer aggregation
network.
So what does programmable gradient information do.
Programmable gradient information prevents data loss or information loss during gradient updates.
And what does generalized efficient layer aggregation network do?
Generalized efficient layer aggregation network optimizes lightweight models through gradient path planning.
So these are the two uh architecture improvements made in YOLO v9.
And what does they perform?
I have already explained.
So the inclusion of a programmable gradient information and the adaptable generalized efficient layer
aggregation network into the architecture of YOLO nine not only boosts the model learning capabilities,
but it also guarantees the preservation of wider information.
So if we incorporate programmable gradient information and generalized efficient layer aggregation network
into the YOLO v nine, this will not only boost the model learning capabilities, but it will also make
sure that no information is lost throughout the detection process, so which results in increase in
accuracy and performance as well.
So we can say that in YOLO, v nine is basically centered around tackling the issue that arises from
information loss in deep neural networks.
So in YOLO nine, the authors Zhenya Wang and his team have addressed the issue of information loss
by using PGI and Glen architecture.
So you using the YOLO model you can do object detection train object detection model on custom data
set.
But we cannot perform segmentation, classification and pose estimation task with YOLO v9 currently.
So as as I'm recording this tutorial on 19th of March, currently you cannot perform segmentation,
classification or pose estimation task which you can perform using Yolo V8.
Uh, but you cannot perform this task with YOLO v9.
With yolo v9.
Currently, you can only perform object detection.
You can train your object detection model, or you can train the object detection Yolo v9 object detection
model on any custom data set as well.
So here we have uh, yolo yolo v nine models.
So YOLO v nine comes with four different models which are ordered by parameter code.
You can skip the Yolo v 90 model.
We will start from YOLO v9's, YOLO v nine medium, YOLO v nine compact and Yolo v nine extended.
So YOLO v nine comes in four models orders by the parameter count.
So now you can see that v nine small model has 7.1 million.
So here I am.
Represent millions 7.1 million parameters V nine medium has 20.0 million parameters.
Yolo v nine compact has 25.3 million parameters and Yolo v nine extended has 57.3 million parameters.
Yolo v nine comes in four models.
Orders by the parameter count by nine small Yolo v nine medium, Yolo by nine compact, and Yolo v nine
extended.
Each model differs in terms of parameter, count and performance.
So now you can see here.
Here we have all the parameter in millions.
And here we have the performance mean average precision on the validation set of the Ms-coco data set.
So you can see that yolo V9S has the least mean average precision as compared to other YOLO v nine model,
which is 46.8%, and Yolo v nine extended has the highest mean average precision on the validation set
of the Ms-coco data set, which is 55.6%, and it has also has the higher number of parameters, which
is 57.3 million as compared to the other Yolo v nine models.
When it has more number of flops as well.
So YOLO benign coco benchmarks.
So the diagram below like you can see the diagram over here.
So.
You can see this diagram.
The diagram below illustrates how the Yolo v nine models achieve high accuracy on the Coco dataset,
while utilizing few parameters showcasing their efficiency in balancing model complexity with performance.
So now over here, you can see that, uh, here we have on the x axis we have the number of parameters.
And on the y axis we have the mean average precision on the validation set of the mscoco data set.
And the model.
We have evaluated the Yolo v nine model with other YOLO models on the Mscoco data set, uh, on the
Mscoco data set, which is the benchmark data set to evaluate the object detection models.
So over here you can see that this is this green line, this um, uh, you can say maroon color show
the performance of YOLO V nine and the Glen model.
So YOLO v nine paper publishes two different release, two different models.
One is Glen model and other is YOLO v nine model.
And here this.
Maroon color show the performance of YOLO V9 and the Glen model.
So this is the YOLO v9 model.
And this is the, uh, Glen model.
So you can see that YOLO v9 models uses less parameters and it outperforms in terms of accuracy than
the previous Yolo YOLO model.
So you can see that it outperforms Yolo V8 model.
Yolo V6 model Yolov7 model Yolov5 model YOLO miss model gold YOLO model.
So we can easily say that YOLO week nine uses less parameters and it like it is more accurate as compared
to the other YOLO models YOLO models.
So from the graph, we can say that YOLO v9 uses the less number of parameters, and it outperforms
in terms of accuracy as compared to the other YOLO models.
So YOLO v9 outperforms in terms of accuracy as compared to other YOLO models.
So.
Is the smallest model you v9's.
So you can see over here the smaller.
Smallest model YOLO V9's achieves 46.8 average precision on the validation set of the Ms. Coco dataset,
while the YOLO largest model, YOLO v9 extended achieves 50.6 55.6% average precision on the validation
set of the Ms. Coco data set.
From here you can see.
So now we'll do the comparison of YOLO v nine with YOLO v eight and yolov7 in terms of parameters count
and flops, as well as in terms of mean average precision as well.
So a quick overview of Yolo V8 Yolo V8 became popular because it offers a balance between speed and
accuracy.
So uh, basically YOLO B nine provides faster inference, faster inference with good accuracy, and
it offers good real time performance, which makes it suitable for application, requires low latency.
So inference means, uh, how much quick the output detection are.
So you love it provides quick output detections with good accuracy.
So which make is good for real time applications.
Uh which requires low latency.
So what is latency?
In computer vision, latency refers to the delay or the time lag between the input.
When we pass an image to the object detection model and the output where we get the image with bounding
boxes coordinate, uh, around each of the detected objects.
So the latency, uh, is the basically a time lag or that, uh, delay between the input when we pass
the input to the object detection model and the output.
So the time it takes, uh, for the process, uh, during the processing when we pass an input and we
get the output.
So that time is basically refers as latency.
So in Europe, hate has very low latency.
And in my experiments, which I performed, Yellow Gate has low inference or lower inference speed or
latency as compared to yellow line.
So this these are from my experiments in yellow eight.
Low latency means that yellow eight can process images quickly, quickly, and provide results in real
time or near real time, which makes Yolov2 suitable for application where quick responses are required,
such as autonomous driving, real time anomaly detection or surveillance systems.
So this is the comparison of Yolov7 Yolo v eight and Yolo v nine model.
So in terms of parameter count the following.
So these are the comparison of yolov7 yolo v eight and YOLO nine best models.
So the Yolo v eight best model is Yolo V8X model.
Yolo v nine best model is YOLO v nine extended model, and Yolov7 best model is yolov7 x model.
So these are the parameters count number of model parameters in millions.
So these are the millions in millions parameter count.
So Yolov7 best model has 71.3 million parameters, while Yolo v eight best model has 68.2 million parameters,
while Yolo v nine extended, which is the best YOLO v nine model has 58.1 million parameters.
So we can say that in terms of parameter count count, there is a 15% decrease as compared to YOLO eight
in terms of in the parameter count of Yolo v nine, while if we do the comparison of Yolo v nine with
yolov7, there is a 19% decrease in terms of parameter count as compared to yolov7.
And if we see the number of floating point operations in gigaflops.
So yolov7 x 180 9.9, while in Yolo V8X, the number of floating point operations in gigaflops increased
to 250 7.8.
And in Yolo V9E it decreases to 190 2.5.
So if we do the giga floating point uh operations comparison between Yolo v nine and Yolov7.
So we can say that Yolo v nine has a bit, uh, more than uh, floating point operations as compared
to Yolov7, but it has less floating point operations as compared to Yolo v eight.
In terms of average precision, YOLO v9 outperforms other object detection models.
YOLO v9 achieves an average precision of 56 55.6% on a validation set of the Ms-coco dataset.
So we can, uh.
It also outperforms YOLO v eight as well as Yolov7.
With YOLO v eight, we get an average precision of 53.9% on the validation set of Ms-coco data set,
and with Yolov7 we get only 52.9%.
So we can say that Yolo v nine outperforms YOLO v eight and Yolov7 in terms of average precision on
validation set of the Ms-coco data set, which is a benchmark data set to evaluate object detection
model.
So in conclusion, we can say that Yolo v nine achieves high accuracy and speed in object detection
while reducing model complexity as you can.
We have seen that yolo v nine uh gives good accuracy with less number of parameters and computational
demands.
This is evaluated by its performance on the Coco data set, where it demonstrates improvement with fewer
parameters and less computational overhead compared to the other versions of the YOLO models.
Thus you do not nurse with its unique architecture, using less parameters and less calculation flops
and giving significant improvements in performance.
So YOLO v9 has its unique architecture, in which we have integrated PGI and Glan so that we can, uh,
prevent from the information loss.
Plus we can also we have also seen that YOLO benign uh, outperforms in terms of accuracy.
And it also uses less number of parameters as compared to the other state of the art YOLO models.
So YOLO v9 outperforms other YOLO models by a very margin, uh, due to its unique architecture, and
it uses less number of parameters and it gives better accuracy as compared to the other YOLO models.
So that's all from this tutorial.
Thank you for watching.