You're the World is a zero shot object detection model, which means we can do object detection without
training the object detection model.
So in YOLO world, all we have to do is prompt the model by specifying the list of classes that we are
looking for and in an image or video.
And that's it.
No training is required in case of YOLO world.
So YOLO world is this is designed to solve a limitation of existing zero shot object detection model
which is speed.
Or you can say latency.
So what is latency?
Latency is basically the time taken by the object detection model to do object detection when an input
image.
So in case of YOLO world we solve this limitation of existing zero shot object detection models.
So the other state of the art object zero shot object detection models like grounding dino uh, use
transformer based architecture, which is typically slow architecture.
But in case of YOLO world, YOLO world is being designed using faster CNN based YOLO architecture.
So YOLO world is being designed using faster CNN based YOLO architecture.
So the downside of Granadino is its speed.
So the downside of grounding 0 or 0 shot object detection model is its speed.
So the time like grounding model takes around one second to process a single image, which is pretty
slow.
If you are thinking about processing live video streams using Grounding Dino.
The following chart is being taken from the Yellow World paper.
So from the chart we can see that yellow world maintains almost the same accuracy, and it is 20 times
faster and five times smaller than the leading zero shot object detection models.
According to the YOLO world paper, the small version of the YOLO YOLO world model achieves up to 74.1
fps on Nvidia V 100 GPU, which is quite impressive.
Traditional object detection models such as faster R-cnn, SSD, and YOLO models are designed to detect
objects within a predefined set of categories.
For example, models which are trained on Coco dataset are limited to limited to 80 categories.
So if you want a model to detect new objects that does not exist in the Coco data set.
For example, if I want to detect a pistol or if I want to detect personal protective equipment, I
if I want to do personal protective equipment detection.
So then what we need to do, we need to, uh, create a data of personal protective equipment detection
where we have the images of, like, different personal protective equipment.
And if you want to do, uh, pistol detection, I need to create a data set where we have different
images of pistols.
So then we need to annotate those images.
And then I need to train the object detection model on this data set.
So this is of course very much time consuming okay.
So uh due to this limitation, like we have to, uh, collect the data set and data data set and train
the object detection model.
So this is what we need to do in case of traditional object detection model.
So due to this limitations the researchers began to develop open vocabulary models.
So the traditional object detectors are object detector detectors are basically fixed vocabulary detection
models.
So the researchers that have done tried to develop some open vocabulary detection models like YOLO world
is an open vocabulary detection model.
Ground in Reno is an open vocabulary detection model.
So due to this limitations like so much time consuming process.
So the due to this limitations, researchers begin to develop open vocabulary detection models.
A few months back, uh, like a few months back, Ronaldinho was introduced, which is an open vocabulary
detection model.
Or you can say zero shot detection model.
Why we say it a zero shot object detection model.
Because, uh, we do not require to train the model to do object detection.
So in zero short object detection model or in open vocabulary detection object detection models, all
we have to do is prompt the model by specifying the list of classes that you are looking for.
So in case of hello world, we will see uh, in next lecture.
So in case of YOLO world, what we need to do is we just need to specify the list of classes that we
want to detect in an image or video.
And we just want to need to pass that list of classes into the input prompt of the YOLO world model.
Okay.
And we don't need to train the model on that specific classes that you want to detect in image or video.
So this is what it, uh, it's we're doing, uh, open vocabulary detection model, which include the
underworld and growling dino.
We just need to pass the list of classes that we want to detect in image or video, uh, as a prompt
to the underworld or bounding dino model.
But YOLO world outperforms the grounding dino model.
There is a some limitations or you can say downsides of grounding Dino model.
So the downside of downside of grounding dino model is its speed, so that the grounding dino model
takes around one second to process a single image.
Uh, which is good enough if you don't care about the amount of latency, but pretty slow if you are
thinking about processing a live video streams using grounding Dino model.
This reason is that the grounding model and other zeros.
Uh uh, zero shot object detector models, except for the world, use heavy transformer based architecture
and require simultaneous processing of text and images during the inference.
And that slows down the processing and it increases the inference time or it increases the latency.
Okay.
And here comes the YOLO world, uh, which is a zero shot object detector model.
And it is equally accurate, unlike Grounding Dino.
And it is 20 times faster than grounded Dino and other, uh, zero shot models.
And it is five times smaller than its predecessors, like Grounding Dino.
So the YOLO world model outperforms other zero shot object detection models like it is 20 times faster,
five times smaller, and it is equally accurate.
So going ahead.
So what is YOLO world?
So YOLO world is introduced in the research paper, uh, title YOLO World Real Time Open Vocabulary
Object Detection, which shows a significant advancement in the field of open vocabulary object detection
by demonstrating that lightweight detector, uh, such as these from the YOLO series.
So like the YOLO world model, use faster CNN based YOLO architecture like in YOLO E8, and it achieves
a strong vocabulary performance.
Like it?
Uh, it is equally accurate than the other zero shot object detection world object detector models.
Okay, so YOLO world basically introduces the prompt then detect paradigm which is a novel approach.
So YOLO world a novel approach by the name prompt that then detect paradigm is being introduced that
avoids the need for real time text encoding or like a property of other zero shot object detector,
uh, object detector models like grounding.
So in ground, uh, in YOLO world, we don't perform real time text encoding.
That basically reduces the speed of the model.
And this real time text encoding is being performed in grounding Reno and other zero shot object detector
models as well.
So the yellow world provides comes with three different models.
You have the world small, which has 313 million parameters, and when Reparameterized goes to 77 million,
and the yellow world medium model has 29 million parameters, and when it is reparameterized, it goes
to 92 million.
And yellow world large model has 48 million parameters.
And when it is reparameterized, it is close to 110 million parameters.
So the yellow world team benchmarked the model on Elvis data set and measured the performance on a V
and V 100 GPU without any performance acceleration mechanism like quantization or tensor RT.
So according to the paper, Yellow World uh reached uh between 35.4 and mean average precision with
52.0 on the large model, uh, and 26.2 mean average precision with 74.1 for the large version.
So this performance is quite, uh, low if we compare with other object detector models.
But this performance is quite compatible if we compare it with other zero shot object detector models.
But if you compare with like state of the art object detector models like YOLO, yolo, v9, this performance
is low, but it is, uh, equally accurate when we compare it with grounding dino Glip like these models.
So here comes the conclusion.
So, is your world a golden solution or is that golden ocean the model that ends training on the custom
dataset?
So do I consider a YOLO world a golden solution?
Like it ends training on the custom dataset, you just need to pass the name of the classes that you
want to detect in image or video in the uh, as a prompt, uh, to the YOLO world model.
So.
But I don't think so that your world is a golden solution, because there are still cases where I would
choose the model trained on a custom data set.
Uh, like the Yolo v eight or YOLO v nine model trained on the custom data set over zero shot object
detector like YOLO world.
Because there is one issue which is latency.
Like the Hello World takes around, uh, hello world takes more time than other state of the art object
detector, uh, to process an image or the latency is a much higher than the other state of the art
object detection models.
So although YOLO world is faster than Reno, but it is slow as compared to other object detection models
like V8 or YOLO Nye.
Okay, so if we required faster processing and have limited limited computational resources, for example,
if I'm working on an Nvidia T4 GPU, like I have limited, uh, the resources like limited GPU requirements
and uh, I require fast processing in real time, then I will be using the traditional object detectors
like YOLO v9, Yolo V8 plus Yolo V8 is less accurate and less reliable as compared to other object detectors
like YOLO seven, Yolo V8 or YOLO v nine when trained or custom data set.
So in short, YOLO world is is an important step in making open vocabulary object detection models faster,
cheaper and widely available, making nearly the same accuracy like other its predecessors like Ground
and Reno.
But and YOLO we will avoid is 20 times faster and five, uh, times smaller than the leading zero shot
detectors models.
So that's all from hysteria.
Thank you.