YOLO 11 is the new state of the art model introduced by Ultralytics.
YOLO 11 outperforms all the previous object detection models, including YOLO ten.
Yolo v nine.
Yolo v eight.
In terms of speed and accuracy, YOLO 11 can be used for a variety of computer vision tasks, including
object detection, instance segmentation.
Object tracking.
Pose estimation.
Some of the key improvements in YOLO 11 include enhanced feature extraction, greater accuracy with
fewer parameters, and faster processing speed.
So YOLO 11 is basically built on the advancements introduced in Yolo v nine and Yolo v ten, incorporating
improved architectural design, enhanced feature extraction techniques, and optimizing the their training
methods.
Yellow 11 medium model achieves a higher mean average precision on the benchmark data set, while using
22% fewer parameters than yellow eight medium model.
Along with this, yellow 11 brings a faster processing speed, with inference time around 2% quicker
than YOLO v ten.
So this makes this like this.
Feature enhancement techniques and make a faster processing speed makes YOLO 11 ideal for real time
applications.
So this is a quick introduction about YOLO 11.
Let's dive into the code and see how we can do object detection, instance segmentation, image classification,
object tracking and pose estimation with YOLO 11.
So this is the Ultralytics repository, which contains all the code for your 11.
And you can see YOLO 11 is a cutting edge, state of the art model that builds upon the success of previous
versions.
Okay.
And you can see over here this blue line is width of YOLO 11.
And you can see that YOLO 11 outperforms all the previous object detection models, including YOLO,
YOLO v9, YOLO, V8, Yolov7 in terms of mean average precision.
And you can see the latency.
Latency is basically the time that is, uh, is basically the time, uh, to do object detection on
an input image, like the time taken to do object detection on an input image is basically the latency,
like how much times time it takes to do object detection on an input image is the latency.
So you can see that, uh, YOLO 11 takes less time and you know.
11 also outperforms in terms of mean average precision then all the other YOLO models.
So YOLO 11 outperforms all the other object detection models in terms of mean average precision.
And it takes less time as compared to other YOLO models.
Okay.
And you can see that YOLO can be used directly in the command line interface with the YOLO command.
And you can also use YOLO 11 directly in the Python environment as well.
So all these steps are provided.
So your YOLO uh 11 comes with 25 open source models.
Okay.
So there are five models for the classification, five models for the detection, five models for the
segmentation and five models for the pose estimation.
And you can use tracking feature with any of these models.
So these are the models that the YOLO 11 comes with YOLO 11 nano YOLO 11.
Small YOLO 11, YOLO 11.
Medium, YOLO 11 large and YOLO 11 extra large model.
Okay.
And you can see that, um, YOLO 11 nano model is has a mean average precision validation score of 39.5.
Uh, but you can see that YOLO 11 Nano gives us is very much faster than other YOLO model.
But YOLO 11 X like extra large model is uh, in in is better in terms of mean average precision score
like with 54.7, but it takes, uh, more time and then other YOLO 11 models.
Okay.
So there is a trade off between, uh, speed and accuracy, which we can see over here.
Okay.
And similarly, you can see these are all the models for the segmentation.
And these are all the models for the pose estimation.
And these are all the models for the oriented bounding boxes okay.
And here these are all the models for the classification okay.
And these are all the details.
You can find all these details over here.
And you can simply use install the Ultralytics package.
And then you can use your 11 simply okay.
So this is all the repository.
You can check out this repository as well.
And you can see different examples over here as well okay.
So here is our Google Colab notebook.
And let's go over here I have written all the code uh to do object detection uh instance segmentation,
image classification, pose estimation using Ultralytics YOLO 11 okay.
So first of all, uh, you can um, set the runtime as default GPU because Google Colab offers free
GPU and you can use this.
Okay.
So first of all we will install the Ultralytics package over here.
So this will take two seconds.
So now you can see the analytics package is installed.
Now we will download some sample image and video from drive.
So I have added some an image and a video on my drive.
So I'm directly downloading that image and video from drive into this Google Colab notebook.
And I will be doing uh, object detection, image classification, instance segmentation and pose estimation
on this image and on this video.
So let's see how we can do object detection in images using YOLO 11.
So first of all we are using the YOLO 11 pre-trained model.
And we only want to do the prediction using YOLO 11 pre-trained model.
And we are using YOLO 11 nano model.
You can use uh other models as well.
But YOLO 11 nano is the most fastest model, but it is less accurate as compared to other YOLO 11 models
okay.
And we are doing detection okay.
Object detection.
And we are doing prediction using YOLO 11 and a model.
And we are doing prediction on this sample image okay.
So let's see.
So now you can see YOLO 11 is very much fast.
And like it took few seconds like 10 to 15 seconds over here.
And now you can see over here we have our runs folder.
And inside this we have prediction detection prediction.
And here is our output image.
So you can just copy the path from here.
And you can just add this path over here.
And you can say.
Okay.
So you now you can see the output.
We are able to detect the person bus traffic light.
We are able to detect both the traffic lights over here.
We are able to detect the handbag over here.
So the results look very good.
From now Okay.
So, like, on using, uh, your command over here, uh, we are able to do object detection on an input
image.
Okay, let's see how we can do object detection in videos using 11.
So you just need to, uh, add the path of your video file in the source as all the other things will
remain the same.
Okay.
So let's run this up.
So our complete video is being divided into 458 frames.
Like you can see over here.
And we are doing, uh, object detection on each of the frame one by one.
Okay.
So this will take few seconds.
Like this is very quick.
Like you can see as we are using GPU.
So you can see that, uh, processing is very fast and you can see it's done.
And uh, we'll use this code to display the output video in this Google Colab notebook over here.
well.
And this will take two seconds.
And we have the output video over here.
So you can also download the output video from here.
But the size is very large.
And here we have the complex output video being displayed over here.
So we can download this.
So let's wait for a few seconds before we.
After this we will see how we can do object segmentation or in images or instance segmentation in images
using YOLO 11.
And then you will see how we can do instance segmentation in videos using your 11 as well.
So let's wait for this to finish up.
And you can see that a compressed file is being created.
Now you can see over here the size of this output video was 94.81 MB.
And now the compressed video size is around 10.34 MB.
Or you can see we have this recipe file over here okay.
And you can see here is our output video.
And let me download this video and show you.
Okay.
So now you can see over here we are able to do object detection on on this object.
Like we are able to detect a traffic light person bicycle.
So the results look quite good.
Like you can see over here we are able to tag the back bicycle person traffic lights uh road signs as
well.
So the results are very promising.
Okay, uh, let's move ahead and see how we can do instance segmentation in images using YOLO 11.
So we are using YOLO 11 nano dash segmentation model.
If you see over here uh we have the segmentation models available over here.
Like you can see over here we have the post model.
So you can use any of these models okay.
So let me just.
So now you can see over here we are able to do segment instance segmentation on this input image.
And you can see over here we have our output saved in the predict folder.
So if we just refresh this up so you can see segment and the predict folder we have our output image.
So you can simply copy path from here and add this path over here.
Okay.
And let's run this.
So now you can see over here we are able to do instance segmentation on the bus traffic light handbag
or son backpack.
Okay.
So you can see the results look very promising.
Okay.
Uh let's see how we can do object segmentation in videos using YOLO 11.
Okay.
So let's run this up now.
So you just need to add a video path over here.
And nothing else.
But now you can see over here the processing is very quick.
Okay.
So okay so our output is saved to run segment predictor.
So if you just refresh this up and here you can find the output video.
And you can simply run these two cells from here.
And you can see.
So if I just run this again uh this will take a few seconds before I have my output video being displayed
over here.
So let's wait for few seconds.
After this, we will see how we can do pose estimation in images using YOLO 11.
Okay.
And we will also see how we can do pose estimation in videos using YOLO 11 and then you will see how
we can do image classification over here.
And we will take an image of a sports card over there.
Okay.
So this will take a few seconds before we have our output video over here.
So let's wait for a few seconds.
So you can see here we have our output video.
Let me download this video and show you on a full screen how our output looks like.
Okay so let me just navigate this.
So you can see over here we are able to do instance segmentation.
Like you can see over here.
We are able to detect bicycle person traffic light road signs.
And the results look very promising.
Like you can see over here we are able to detect bicycle person traffic lights.
And the results are very very promising.
Like, these results are very impressive.
Okay.
Let's go back over here and let me just save this up now.
Okay.
So next we will see how we can do over estimation in images using YOLO 11 over here.
Okay.
So we will just like the task is equal to post mod is equal to predict.
And we are using your 11 and dash post model.
And you can see over here uh we have this post models in the repository.
You can just use any of these models.
Okay.
And so you go for object detection we use dot is equal to detect.
For instance segmentation we use toss is equal to segment.
And uh for pose estimation we use the toss is equal to pose over here.
And we are using basically pre-trained model.
We are not fine tuning the model.
So we are using the pre-trained model over here.
And we are just setting the mod mode is equal to prediction.
If you are using doing that training or fine tuning the YOLO 11 model, you can see set mode is equal
to train, but we are currently doing predictions.
So we have set mode is equal to prediction.
And here we have passed our input image okay.
So you can see over here we have the post folder and the predict we have the output image.
And you can just simply copy path and just add this path over here.
And here you can see we are able to do the pose estimation on this on the person over here.
And the results look quite promising like results are good okay.
And let's see how we can do over estimation in videos using YOLO 11.
So you just need to add the video path over here.
And we are using YOLO 11.
And postmodern mode is equal to predict and toss is equal to pose estimation.
Let's run this up now.
So this will take few seconds.
Like you can see we are able to drag the person over here.
So YOLO 11 pre-trained word estimation model can only do pose estimation of person.
Uh, to do pose estimation of an animal or a class, you need to fine tune the YOLO 11 pose estimation
model.
But the pre-trained YOLO 11 pose estimation model or other pose estimation models, YOLO pose estimation
models including uh yolo, YOLO v eight can do pose estimation, or only person to do any other for
any other animal, or to do pose estimation of any object.
You need to fine tune the the pose estimation model of YOLO.
Okay, so it's done now.
And let's display this output video over here.
Okay.
So after this we are only left with one task which is image classification.
Yeah.
And we will do the classification of this, uh, this sports car.
Okay.
And let's see how it is, but let's display this output video first over here.
And then we'll go ahead towards the image classification task as well.
So this will take a few more seconds before we have everything ready over here.
So here you can see we are our output video.
You can just run this output video over here.
But let me download this video and show you a big screen over here.
So it's downloading and let me open over here.
And let me just navigate my screen towards it.
So you can see over here we are able to do pose estimation of person over here.
And the results you can see are very promising.
You can see we are also doing the detection over here as well.
And we are also doing the pose estimation.
So we are doing two things uh object detection and pose estimation.
And the results look very very promising okay.
So let's go ahead and go to our last task which is image classification.
Okay.
So let me just save this up now.
So now for image classification we will download a car image or sports car image from the drive directly
to this, uh, Google Colab notebook over here and we will select the task is equal to classify mode
is equal to predict.
And we are using a YOLO 11 extra large model.
And we are doing classification image classification over here.
And let's run this up now.
And you can see the size of the YOLO 11 extra large model is quite large like it's 56.9 MB.
The YOLO 11 nano model is around 5.6 MB.
Okay, so we have our output image in saved into the folder or run classify predict okay over here.
So let me just display the output image over here okay.
So if I just open this image into the new tab over here you know we can say.
So if I just open this output image into a bigger window.
So now you can see over here we are able to like for the sports card.
Uh, the model is 66% confident that this is a sports car.
And this is for the car will.
The model is 11% confident for the cab.
The model is 10% confidence.
For the wagon, the model is 0.05% confident.
For the dresser, the model is 0.03% confident.
But um, as we can see over here, this is a sports car and the model is 66% confident that this is
a sports car.
So you can see that how accurately we can do, uh, image classification.
Like you can see over here.
Our model is able to classify correctly that this is a sports car.
And you can see over here 0.66.
This is 66%.
Our model is confident that this is a sports car.
So in this tutorial I have presented you an introduction to YOLO 11.
We have seen that how we can do, uh, object detection, instance segmentation, image classification
and pose estimation using YOLO 11 I hope you have learned something in this tutorial are some exciting
tutorials are coming ahead and do subscribe my channel.
Thank you for watching.
Bye bye.