The other way, then, is the latest state of the art object detection model that has been developed
by the researchers at Tsinghua University in China.
So YOLO.
It then introduces a novel approach for real time object detection.
So it basically addresses the deficiencies in both post-processing and model architecture that are found
in the earlier YOLO versions, which include Yolo b nine, B eight, Yolo v7.
So what are the problems in previous versions?
So the previous YOLO models, including YOLO, YOLO, V, eight and V7 were using non expression technique
in post-processing.
During inference, I will explain you what is known by expression later on.
But remember the previous models were using non expression techniques in post-processing during inference,
which leads to inefficiencies and increased inference latency time.
So you love it then eliminates the need for the normal expression.
In yolo v ten we are not using the normal expression technique.
Okay, so the non expression technique leads to inefficiencies and increases the inference latency.
So yolo v ten eliminates the need for the normal expression.
And along with this architecture enhancement are made as well in YOLO beta which include optimizing
various model components.
So in short, you can achieve state of the art performance with significantly reduced computational
overhead.
And when wallaby ten is ten state on the benchmark Ms-coco data set, wallaby ten showed shows a superior
accuracy and latency trade off than the other YOLO models, which include YOLO, Be nine, YOLO, V8,
and Yolov7.
So in this tutorial.
We will see what is geology and how geology ten works.
And, uh, we will see what architecture enhancements are made in geology ten.
And we will do a performance comparison of we ten with other Yolov2 models as well.
And that's all what we will cover in this tutorial.
So let's get started.
So YOLO ten is the real time state of the art object detection model introduced in the paper YOLO v
ten Real-Time End to End Object Detection.
So this paper is available online.
I have just added a snapshot of this paper so you can review this complete paper as well.
In this tutorial I will try to present the crux of this paper.
What is inside this paper.
So I will just try to present the crux of this paper.
So YOLO ten is released in May 2024 is a new advancement in the field of real time object detection.
Basically, YOLO ten tries to address the issues that are faced by the, uh, previous models.
Like what enhancements are made in YOLO ten plus.
Our model architecture enhancements are made in YOLO plus YOLO and addresses the post-processing issues
like the.
It eliminates the need for the random expression.
But.
So what is YOLO we then?
YOLO v ten is a cutting edge.
Computer vision architecture designed for real time object detection, and it is built upon the advancements
of its predecessor.
Okay, Yolo v ten model achieves a higher mean average compare.
Mean average precision compared to earlier modular models such as V9, Yolo V8, Yolo v7 when benchmarked
against the Ms-coco data set.
So you can see over here, um, I will explain it in the Dail as well, but you can see over here this
is the accuracy average precision, and this is the latency.
So we can see an accuracy and uh latency and accuracy trade off over here.
And you can see this red line.
So this is uh you can see over here YOLO V ten outperforms all the previous models in terms of accuracy
as well as in terms of latency as well.
So what is latency basically?
Uh, latency is basically the time taken to do object detection on an input image.
So this is latency.
Latency is basically the time which is taken to do object detection on an input image.
So we can say that YOLO with ten uh takes less time as compared to other YOLO models like you can see
over here.
Uh, from here we can see that YOLO ten definitely takes very much less time as compared to other YOLO
models like, uh, it, uh, quickly does object detection on an input image or an input frame than
other YOLO models.
Plus it you can see over here, uh, YOLO ten gives good accuracy like you can see over here in comparison
to other YOLO retirement.
Uh, YOLO models.
So, uh, that is all, uh.
So YOLO with an object or various strategies to tackle the limitations of previous ruler models.
Earlier neural models rely on non expression for post-processing during inference.
I told you that uh, earlier models rely on the non expression for technique uh, during post-processing,
uh, while doing inference which leads to inefficiencies and increase the inference latency.
So the basically if I use Non-max suppression technique it post-processing uh during inference, uh,
basically this will lead to uh, more latency time.
Like it will take more time to process, uh, to do object detection on input image.
Uh, and plus this will also compromise on the accuracy as well, which basically comes with inefficiencies.
Uh, due to uh non expression comes with some inefficiencies to address these limitations.
YOLO we then comes with a consistent view assignment strategy.
So Yolo v ten basically adopts a consistent dual assignment strategy which eliminates the need for non
expression during inference.
And it significantly reduces the inference latency.
So now you can see in yolo v ten we have eliminated or removed uh non expression technique.
So by removing normal expression technique you can see that our latency time has reduced or reduced
a bit.
Uh our latency time has reduced significantly.
Plus you can see that our accuracy has improved like YOLO outperforms all the other object detection
models.
So long.
Expression technique is evaluated during inference in your log10, and it significantly reduces latency,
the increased latency, while retaining competitive performance.
So Jilawatan incorporates efficiency accuracy driven strategy, which involves optimizing various components
of the model to minimize computational overhead enhance performance.
So Yolov3 ten basically operates efficiency accuracy driven design strategy.
So when we adopt efficiency accuracy driven design strategy.
So we are optimizing various components in the YOLO model.
We will discuss what components we are optimizing, what architecture enhancements are made in YOLO
and later as we go ahead.
So now you can see that as we adopt efficiency accuracy driven design strategy, uh, and we enhance
various component of the model so that we can reduce computational overhead and enhance performance.
And we can see in the graph as well, like you can see over here, you know, we didn't use uh, models
has less number of parameters as compared to other models like these are in billions.
Parameters are in millions.
So now you can see over here this is the red color is for YOLO ten.
And you can see that YOLO ten has uses less number of parameters.
Uh.
As compared to other YOLO models, and YOLO Eden has better accuracy as compared to all the other YOLO
models.
So now you can see that, uh, we have minimized by adopting efficiency, accuracy driven design strategy.
We have minimized the computation overhead.
And unlike less number of parameters are used and we have enhanced the model performance as well.
So like we are discussing about Non-expression, we have evaluated non-max suppression during, in,
uh, in post-processing, during inference.
So what we are saying that, uh, YOLO eliminates the need for non-expert expression during inference.
So what is Non-gm expression?
So non expression is basically a post-processing technique used in object detection to remove the redundant
or overlapping bounding boxes.
Uh, the main aim of normal expression is to retain only the bounding boxes.
Higher confidence score and the bounding boxes with lower confidence score are suppressed or removed.
So now you can see that we have detected, uh, truck or a car in this image, like you can see over
here.
And you can see that after doing object detection with YOLO, we then, uh, what output we get is that,
uh, we have that object detected, like, you can see over here, um, the truck, but you can see
that we have multiple bounding boxes as well.
So we will be we use non expression technique in earlier YOLO models like YOLO, YOLO, yolo v7 used
non extrapolated non max suppression technique during inference.
Uh so that they can remove the redundant or overlapping bounding boxes.
So now you can see there is only one truck.
So there should be only one bounding box like you can see over here.
So.
A little YOLO models by using non-max suppression techniques so that they can remove redundant or overlapping
bounding boxes for the bounding box, which have the highest confidence score among all these bounding
boxes will be retained.
Like this morning box will have the highest confidence score than all the other bounding boxes, so
it is retained.
So.
So previous models were using norm expression technique in during inference so that they can emulate
the, uh, overlapping bounding boxes.
But this is uh, leading to inefficiencies.
And it was also increasing latency as well.
So in rollup we then we have eliminated the need for the norm expression.
So here you can see we will discuss how you have written words.
You also written introduces a novel training strategy and architecture enhancement.
Let's discuss the main component of all your written work.
So yellow written produces an ms3 training strategy with new label assignments.
So this is a snapshot from the paper I have.
This app added our tracks over here.
What is inside the paper.
So YOLO written basically docs on novel training strategy and it provides some architecture enhancements
as well or introduces some architecture enhancement as well.
So like I told you that, uh, YOLO imitates the need for non-max expression.
So traditional YOLO models like YOLO, benign yolo V8 employ 1 to 1 one to many assignments strategy
during training which make it necessary to use norm expression.
So the earlier models like YOLO benign V8 were using uh, norm expression technique.
Or you can see that the earlier models were adopting one to many assignment strategy, which make it
necessary to use norm expression during inference, which like we use norm expression.
I told you that to filter out redundant or overlapping bounding boxes, which leads to inefficiencies
and increase inference latency.
Okay, so earlier YOLO models adopt one to many assignment strategy.
So YOLO v ten adopts a new label assignment strategy that incorporates one to many like YOLO, and uses
the one to many assignment strategy and 1 to 1 matching approaches.
Like now you will be thinking that YOLO written observes one to many and 1 to 1 matching approaches
like.
But by thinking that in one to many assignment strategy they are using in non expression during inference
to filter out predicted bounding boxes.
But as we adopt one to many and 1 to 1 matching approaches, we will not be using non expression during
inference.
So what is 1 to 1 matching.
So in 1 to 1 matching the.
Our model assigns a single prediction to each ground truth instance eliminates the need for non expression.
So in 1 to 1 uh matching we don't use non expression okay.
But uh this results in weaker supervision or could be causing suboptimal accuracy and slower convergence.
So but uh if we 1 to 1 matching we are not using norm expression.
But uh this results in some compromise on accuracy.
But in 1 to 1, one to many assignments, uh, although it provides richer supervisory signals or signals
but uh required non expression per inference.
So a one to many assignments we will be using non expression for inference.
But this leads to uh.
At latency like increased latency.
So in 1 to 1 matching we don't use non-expression, but we are compromising accuracy.
But in one to many, many segments we are not compromising on accuracy.
We are using non-expression in inference, but this leads to increased latency.
So how YOLO we can address this issue like not using non-expression plus not compromising on accuracy
and also reducing the inference latency.
Okay, so you love it and cleverly combine these strategies by introducing an additional 1 to 1 head.
So basically, you know, we tend to use introducing an additional one one head.
And.
So mirroring the original one to many branch structure and optimization objectives.
During start training, both heads are jointly optimized labeling that requires supervision from one
to many assignments.
So now this is embodied during the YOLO model.
Initializes only 1 to 1 head, thus bypassing the need for norm expression.
So while doing inference, YOLO with ten only adopts 1 to 1 approach, and it bypasses the need for
norm expression and achieve high efficiency without adding additional inference cost.
So this is how user written works.
So another thing like consistent matching that is like we can see all the details in the paper.
But the crux of this is that a key component of the dual assignment strategy.
So as I told you, adopts a dual assignment strategy that uh, combines uh 1 to 1 matching and one to
many assignments like you can see over here.
So now you can see that dual label assignment strategy of one to many and 1 to 1 approach as well.
But now you can see that after one to many and 1 to 1 they will be spent matching metric.
So a key component of the dual label assignment strategy that we are adopting in YOLO and and so YOLO
we then uh, adopted a novel approach which is called dual label assignment strategy.
And here we have the complete architecture like how it works.
So you can see a key component in new label assignment strategy is persistent matching metric.
A few key component of that new label assignment strategy is the persistent matching metrics, which
is used to evaluate the concordance between the prediction and ground truth instances like calculate
the matching between the prediction and the ground truth strategy so you can see how close they are
or how far away they are, like how close our predictions are with from, uh, ground truth and or how
far our predictions are from the ground.
So now you can see what architecture enhancements are made in your weekend.
So the component of YOLO models are traditionally the components of YOLO model consist of the stem downsampling
layer stages with basic building blocks, and the head.
Uh Yolo focusing on optimizing the other three parts to enhance, uh, efficiency.
So uh, we will be ten introduces basically lightweight classification had in it.
And our light class lightweight classification head is designed to reduce the computational dependency
in ensuring that a model operates more efficiently.
Sparkle channel decoupled downsampling.
So, uh, in YOLO, we dance party channel decoupled downsampling is applied to the Wise feature extraction,
making uh the process more efficient and rank guided block design.
The rank guided block design further streamlines the architecture, enhancing overall efficiency and
large kernel convolution.
The large kernel convolution is utilized to improve the model's capacity to capture detailed features,
and the effective partial self-attention module most aggressive with minimal computational cost.
So these are the architecture enhancement that are made in YOLO.
So now we are doing a YOLO v ten performance comparison with other baseline models like YOLO, YOLO
v ten comparison to other baseline models like v eight.
So you can skip this.
We are not, uh, using YOLO we ten D model in comparison because currently we are doing comparison
with yolo v eight models.
And uh, we are skipping this and we will be using yolo v ten and yolo with n s, YOLO with ten M,
YOLO with n, l and x v x model for the comparison.
In comparison to other baseline models like, there will be ten more straight improvements of 1.2%,
1.4%, 0.5, 0.3 0.5% in average precision like you can see over here.
Here we have the validation average precision.
You can compare the yellow with ten, uh model average precision.
And this yellow eight.
And here you can see your S model average precision with yolo V8 S model over here.
And so after doing this comparison you will see that your W ten demonstrate improvements by the numbers
provided over here in average precision with 28%, 36%, 41%, 44%, 57 fewer parameters.
Like you can see the parameters found in million by end users 2.3 in uh, yellow ten and model uses
nano models.
You can use this 2.3 million parameters, and Yolo V8 nano models uses 3.2 million parameters for definitely
yellow B ten model uses less number of parameters, and YOLO with ten small model is 7.2 million parameters
that YOLO small model uses 11.2 million parameters, so definitely using one model with this less number
of parameters.
And you can see that, uh, uh, with 28%, 36%, 41%, 44%, and 57% fewer minorities and eight model
land uses, and 23%, 40, 24%, 25%, 27% 30% fewer calculation and 70% 65%, 50%, 41%, 37% lower
latencies.
Like you can see over here, latency in milliseconds with ten is and models.
Nano models have 1.84, and the other V8 and nano model has 6.16 with ten, small model has 2.49 and
Yolov5.
A small model has 7.07.
So you can see that Yolo v ten has a low latency as compared to the other.
We're.
So in conclusion, your dividend represents a significant investment in real time object detection and
state of the art performance in terms of speed and accuracy.
So Yolov3 ten introduces NMS speed training and it adopts an efficiency accuracy driven model design
strategy.
In efficiency accuracy driven model design strategy, we optimized various model components so that
we can reduce the number of parameters and get more accurate results.
So you do return includes improves accuracy while reducing computational redundancy and latency.
Competitive analysis against baseline models like if we compare YOLO with ten model with eight Yolov7,
our European R demonstrates superior performance in average precision, parameter efficiency and inference
speed.
Thank you for watching this tutorial.