Ultralytics.
YOLO 11 is a cutting edge, state of the art computer vision model that is built upon the success of
previous YOLO versions.
YOLO 11 outperforms all the previous models in terms of speed, accuracy, and efficiency, which makes
it an excellent choice for a wide range of object detection, instance segmentation, pose estimation,
and image classification tasks.
Your 11 comes with a faster processing speed, and it is around 2% quicker than YOLO v ten, which makes
it an ideal choice for real time applications.
YOLO 11 is built upon the advancements that are introduced in YOLO v9 and YOLO ten.
YOLO 11 incorporates improved architectural designs, enhanced feature extraction techniques, and optimized
training methods.
YOLO 11 supports a variety of computer vision tasks which include object detection.
In object detection, we identify and locate objects with an image and video frames, and we draw bounding
boxes around each of the detected object.
And in case of object detection, application lies in surveillance, autonomous driving, and retail
analysis.
YOLO 11 also supports instance segmentation.
In instance segmentation, we draw masks around each of the detected object, and the application of
instance segmentation lies in medical imaging and defect detection in manufacturing.
YOLO 11 also supports pose estimation.
In pose estimation, we detect specific key points with an image or video frame to track movements or
poses.
The application of pose estimation lies in sports analytics, healthcare applications, and in fitness
tracking.
YOLO 11 also supports image classification.
In image classification, we categorize entire image into predefined classes, and the application of
image classification lies in e-commerce and wildlife monitoring.
YOLO 11 also provides support for object tracking.
In object tracking, we basically assign a unique ID to each of the detected object, and then we track
that detected object throughout the entire video frames.
In case of if we want to compare the performance of YOLO 11 with other YOLO orders, here you can see
a chart in your screens.
So we can see clearly see that YOLO 11 comes with five different models, and all these five models
outperforms all the other YOLO models.
And you can see here a blue line over here.
And you can clearly see YOLO 11 outperforms all the other YOLO models in terms of accuracy, as well
as in terms of speed.
In the on the x axis x axis, you can see a latency.
Latency is basically a time that is taken to do object detection on an input image.
So from the graph we can clearly see that Yolo YOLO 11 takes less inference time and it is more accurate
as compared to other YOLO models.
So you might be thinking how we can use YOLO 11 to perform object detection, instance segmentation
and pose estimation to use yellow 11 to perform object detection, instance segmentation, and pose
estimation.
You just need to install the Ultralytics package and nothing more.
You just need to install Ultralytics package by writing pip install Ultralytics, and you can also use
your 11 directly into your command line interface using the YOLO command.
And so you can simply write YOLO and predict mode, and you can just model in the model.
You can just pass the name of the YOLO 11 model you want to use.
User 11 comes with five different models, and 11 nano is the smallest and it is the most fast, but
it is less accurate.
While user 11 Extra Large is the most accurate.
But it takes more inference time as compared to other YOLO models.
So you can use any of the YOLO 11 model as per your requirements and in the source you can pass your
input image, video or live feed path.
So in this way you can uh, you can do object detection, instance segmentation, and pose estimation
using YOLO 11 by writing these two lines of code.
So that's all from this tutorial.
And I have given you a quick demo about YOLO 11.
So let's get started with this.