YOLO-World: Real-Time, Zero-Shot Object Detection Explained

Summary

This video introduces YOLO World, a zero-shot object detection model that's 20 times faster than its predecessors. It requires no training and can detect a variety of objects in real-time, even on budget GPUs. The video discusses its architecture, speed advantages, and demonstrates how to run it on Google Colab. It also covers its limitations and potential applications, like detecting objects in controlled environments or combining it with segmentation models for faster processing.

Takeaways

• 🚀 YOLO World is a zero-shot object detection model that can detect objects without any training.
• 💻 It is designed to be 20 times faster than its predecessors, making real-time detection feasible.
• 🔍 The model can be run on Google Colab, allowing for easy access and use without extensive hardware requirements.
• 📈 YOLO World's architecture consists of a YOLO detector, a text encoder, and a custom network for cross-modality fusion.
• 📊 It uses a lighter and faster CNN network as its backbone, contributing to its speed.
• 🔑 The 'Prompt then Detect' paradigm allows for efficient processing by caching text embeddings, reducing the need for real-time text encoding.
• 👥 The model can detect objects from a user-specified list of classes without needing to be trained on those specific classes.
• 📉 Lowering the confidence threshold can help detect more objects, but may also result in duplicated detections.
• 🎥 YOLO World excels in processing videos, achieving high FPS on powerful GPUs and decent FPS on more budget-friendly options like the Nvidia T4.
• 🛠️ Non-max suppression is used to reduce duplicated detections by discarding overlapping bounding boxes with high Intersection over Union (IoU) values.
• 🌟 While YOLO World is a significant advancement, it may not replace models trained on custom data sets in all scenarios, especially where high accuracy and reliability are critical.

Q & A

What is the main advantage of YOLO World over traditional object detection models?

YOLO World is a zero-shot object detector that is significantly faster than its predecessors, allowing for real-time processing without the need for training on a predefined set of categories.

How does YOLO World achieve its speed?

YOLO World achieves its speed through a lighter and faster CNN Network as its backbone and a 'prompt then detect' paradigm that caches text embeddings, bypassing the need for real-time text encoding during inference.

What are the three key parts of YOLO World's architecture?

The three key parts of YOLO World's architecture are the YOLO detector for multiscale feature extraction, the text encoder that encodes text into embeddings, and a custom network for multi-level cross-modality fusion between image features and text embeddings.

How does YOLO World handle detecting objects outside of the COCO dataset?

YOLO World uses a zero-shot detection approach where it can detect objects by specifying the list of classes it is looking for without needing to be trained on those specific classes.

What is the 'prompt then detect' paradigm mentioned in the script?

The 'prompt then detect' paradigm refers to the process where the model is given a prompt (list of classes) once, and then that information is used for subsequent detections without needing to re-encode the prompt for each inference.

How can YOLO World be used in Google Colab?

YOLO World can be run in Google Colab by installing necessary libraries, ensuring GPU acceleration, loading the model, setting the classes of interest, and then inferring on images or videos.

What is the significance of the Nvidia T4 GPU in the context of YOLO World?

The Nvidia T4 GPU is significant because it allows for decent FPS (frames per second) with YOLO World, making it a budget-friendly option for real-time object detection.

How does non-max suppression help in refining YOLO World's detections?

Non-max suppression is an algorithm that eliminates overlapping bounding boxes by keeping the one with the highest confidence score and discarding others, thus refining the detections and preventing duplicates.

What is the role of the 'relative area' filter in processing videos with YOLO World?

The 'relative area' filter is used to discard detections that occupy a large percentage of the frame, which helps in filtering out large, high-level bounding boxes that are not the desired objects.

What are some limitations of YOLO World compared to models trained on custom datasets?

YOLO World may be slower and less accurate than models trained on custom datasets. It also struggles with detecting objects outside of the COCO dataset with high confidence and may misclassify objects in complex scenes.

How does the author suggest combining YOLO World with other models for improved performance?

The author suggests combining YOLO World with fast segmentation models like Fast Mask R-CNN or Efficient Mask R-CNN to build a zero-shot segmentation pipeline that is significantly faster than using GroundingDINO alone.

Outlines

00:00

🚀 Introduction to YOLO World

The script introduces YOLO World, a zero-shot object detection model that can detect a wide range of objects without any training. It is highlighted as being 20 times faster than its predecessors and capable of real-time processing with a powerful GPU, or even decent FPS on a budget-friendly Nvidia T4. The presenter discusses the architecture of YOLO World, explaining its three key parts: the YOLO detector for feature extraction, the text encoder for embedding, and a custom network for cross-modality fusion. The model's speed is attributed to its lighter CNN backbone and the 'prompt then detect' paradigm, which caches text embeddings to avoid real-time encoding. The script also mentions a community session where questions about YOLO World can be addressed.

05:01

🛠️ Setting Up and Using YOLO World

The script outlines the process of setting up YOLO World in Google Colab, including ensuring GPU acceleration and installing necessary libraries. It details the steps to load the model, set classes, and perform inference. The presenter demonstrates how to use the model to detect objects like a person and a dog, and discusses the challenges of detecting classes outside the standard COCO dataset. To improve detection, the script introduces techniques like lowering the confidence threshold and using non-max suppression to eliminate duplicate detections. The presenter also shares their experience of achieving 15 FPS on Nvidia T4 and suggests that YOLO World is particularly effective for video processing.

10:02

🔍 Advanced Detection with YOLO World

The script explores advanced detection scenarios with YOLO World, such as detecting objects with specific characteristics like 'holes filled with yellow substance'. It discusses the challenges of defining prompts for such objects and how using color references can improve detection accuracy. The presenter also addresses issues like large high-level bounding boxes that do not precisely match the target objects, especially with low-resolution images. To refine results, the script introduces a method to filter detections based on the relative area of bounding boxes to the frame. The presenter demonstrates how to process an entire video, save results, and discusses the limitations of YOLO World compared to custom-trained models.

15:03

🌟 Conclusion and Future of YOLO World

The script concludes by emphasizing YOLO World's significance in making open vocabulary detection faster and more accessible. It mentions the potential of combining YOLO World with segmentation models to create powerful video processing pipelines. The presenter encourages viewers to experiment with YOLO World and provides links to resources like a cookbook and a hacking face space for further exploration. The script ends with a call to action for viewers to like, subscribe, and join community sessions for more computer vision content.

Keywords

💡YOLO World

YOLO World is a zero-shot object detection model that can identify objects without any training, making it a powerful tool for real-time object detection. It is highlighted in the video as being 20 times faster than its predecessors, which is a significant advancement in the field of computer vision. The script mentions using YOLO World to process images and videos, emphasizing its speed and efficiency.

💡Zero-shot object detection

Zero-shot object detection refers to a model's ability to detect and recognize objects without being explicitly trained on examples of those objects. This is a key feature of YOLO World, as it can detect objects from a predefined set of categories without additional training. The video discusses how this capability allows for more flexible and immediate deployment in various scenarios.

💡Real-time processing

Real-time processing is the ability of a system to process information quickly enough to handle live input, such as video streams. The video script emphasizes YOLO World's capability to run in real-time, which is crucial for applications like live video analysis or surveillance systems.

💡GPU

GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. The script mentions the need for a powerful GPU to run YOLO World effectively, indicating the computational intensity of real-time object detection tasks.

💡Architecture

In the context of the video, architecture refers to the structural design of the YOLO World model, which consists of a YOLO detector, a text encoder, and a custom network for cross-modality fusion. This architecture is what enables the model to be both fast and accurate, as explained in the video.

💡Inference

Inference in machine learning is the process of deriving conclusions from premises. In the script, inference is used to describe the process by which YOLO World analyzes input data to detect objects. The video discusses how YOLO World can perform inference without needing to encode prompts each time, which contributes to its speed.

💡Prompt then detect paradigm

The 'prompt then detect' paradigm is a method where the model is given a prompt (in this case, a list of classes to look for) and then performs detection based on that prompt. The video explains how YOLO World uses this paradigm to avoid real-time text encoding, thus improving its speed.

💡Non-max suppression

Non-max suppression is an algorithm used to reduce overlapping bounding boxes to a single box for each object, which improves detection accuracy. The video mentions how this algorithm is used by YOLO World to refine object detection results.

Highlights

Introduction to YOLO World, a zero shot object detector that is 20 times faster than its predecessors.

YOLO World can run in real-time with access to a powerful GPU, offering decent FPS even on cheaper Nvidia T4.

Traditional object detection models are limited to predefined categories, requiring new training for new objects.

Zero shot detectors like Grounding Dyo and YOLO World eliminate the need for training by using a prompt list of classes.

YOLO World's architecture consists of a YOLO detector, text encoder, and a custom network for cross-modality fusion.

The use of a lighter CNN network and prompt-then-detect paradigm contributes to YOLO World's speed.

Instructions on how to run YOLO World in Google Colab to process images and videos.

Demonstration of the model's ability to detect objects from a list of specified classes without training.

The model's performance in detecting classes outside of the COCO dataset is significantly lower in confidence.

Techniques to improve detection by lowering the confidence threshold and using non-max suppression.

YOLO World's superior performance in processing videos compared to individual images.

Practical example of using color and position as references in prompts for better detection.

How to filter detections based on relative area to avoid detecting the entire object when only a part is desired.

The process of saving the results of video processing using the supervision package.

Discussion on the limitations of zero shot detectors like latency and accuracy compared to custom-trained models.

Potential use cases for YOLO World, such as in controlled environments like a factory.

Encouragement to prototype with YOLO World and refine detections using the techniques shown.

The future potential of combining YOLO World with fast segmentation models for a zero shot segmentation pipeline.

Introduction of a hacking face space where users can process images and videos using YOLO World and other models.

Final thoughts on YOLO World as an important step towards making open vocabulary detection faster and more accessible.

Transcripts

what if I tell you that there is a model

that you can use to detect all of these

objects and more without any training

and that it can run in real

time well at least if you have access to

a powerful GPU but you can still get a

decent FPS on cheap Nvidia T4 today we

are going to talk about YOLO World a

zero shot object detector that is 20

times faster than its predecessors we'll

talk about architecture discuss the main

reasons why it's so fast and above all I

will show you how to run it in Google

collab to process images and videos

traditional object detection models such

as faster rcnn SSD or YOLO are designed

to detect objects within predefined set

of

categories for instance models trained

on Coco data sets are limited to 80

categories if you want a model to detect

new objects we need to create a new data

set with images depicting the objects we

want to detect annotate them and train

our detector this of course is timec

consuming and expensive in response to

this limitation researchers began to

develop open vocabulary models not even

a year ago I showed you grounding dyo a

zero shot object detector that back then

blew my mind and to be honest I'm still

impressed by its capabilities all have

to do is prom the model specifying the

list of classes that you are looking for

and that's it no training is required

the downside of grounding Dino was its

speed it took around 1 second to process

a single image good enough if you don't

care about the amount of latency but

pretty slow if you are thinking about

processing live video streams that's

because zero shut detectors are usually

using heavy Transformer based

architecture and require simultaneous

processing of text and images during the

inference and that brings us to Yellow

World a zero shot object detector that

at least according to the paper is

equally accurate and 20 times faster

than its predecessors if you would like

to learn more about grounding Dino and

yellow world you can find links to two

of my blog posts covering those models

in the description below now let me give

you a brief overview of the model

architecture

YOLO world has three key Parts YOLO

detector that extracts multiscale

features from input image clip text

encoder that encodes the text into text

embeddings and a custom networks that

perform multi-level cross modality

Fusion between image features and text

embeddings which has the name so

complicated that I won't even try to

pronounce it rep parameterizable Vision

language path aggregation network using

a lighter and faster CNN Network as its

backbone is one of the reasons for

yellow world speed the second one is

prompt then detect Paradigm instead of

encoding your prompt each time you run

inference yellow world use clip to

convert the text into embeddings those

embeddings are then cached and reused

bypassing the need for realtime text and

coding okay enough of the talk let's

take a look at some code but first a

short announcement

this week we will have our first

Community session so if you have any

questions about yellow World model or

about the code that I will show today

leave them in the comments I will try to

answer all of them during the live that

we will host this week you can find more

details about it in the description

below you can also join the live and ask

your questions real time that would be

awesome because I don't want to sit

alone the link to the The cookbook I'll

be using is in the description below and

I strongly encourage you to open it in

separate Tab and follow along we click

the open in collab button located at the

very top of the cookbook and after a few

seconds we should get redirected to

Google collab website now the first

thing we need to do is to ensure that

our collab is GPU accelerated as usual

we can do it by executing the Nvidia SMI

command after brief moment we should see

a table containing information including

installed version of Cuda and the name

of the graphic card that we have at our

disposal now that we confirm that Cuda

session has GPU support it's time to

install the necessary libres the first

is rof flow inference a python package

that we will use to run Yello World

locally but you can use it to run all

sorts of different computer vision

models and the second is supervision the

computer vision Swiss army knife that we

will use among other things for

filtering and annotating our detections

the installation may take few moments so

let's use the magic of Cinema to skip it

to confirm that everything went smoothly

let's try to import the packages we need

both open CV and tqdm are available in

Google collab out of the box that's why

we didn't include them in the

installation section if you're running

the notebook locally on your PC make

sure that those packages are also

installed yellow world is available in

four different sizes SM m l and X but

for now only the first three are

accessible via inference package of

course along with different sizes you

should expect different speeds and

accuracies in this tutorial I will use

the L version but you should use the

version that is suitable for you based

on your speed accuracy and Hardware

requirements

to load the model we simply create an

instance of yellow world class which we

imported a few cells above this class

has two core functions set classes and

infer as mentioned in the introduction

to avoid the need for realtime text

encoding yellow World utilizes The

Prompt then detect Paradigm by using the

set class method our prompt is encoded

into offline vocabulary let's choose a

list of our classes person by backpack

dog eye nose ear and tongue we see that

the clip model is being downloaded in

the background it will be used to

convert our list of classes into

embeddings now we just need to load our

image and pass it as an argument to a

second method I mentioned infer then

using the utilities available in

supervision package we can visualize our

results well unfortunately from the

entire list of the class cles that we

provided only two were detected a person

and a dock in my experiments I noticed

that classes outside of Coco data set

are detected with a significantly lower

confidence level let's try to lower the

threshold to include detections that

model is less certain about our updated

code is very similar essentially only

two things have changed we drastically

lowered the confidence threshold from

the default

0.5 to

0.03 and we updated our code visualizing

detections to display not only the class

name but also the confidence level such

a low confidence level is not something

that I would usually recommend but in

case of yellow world this strategy works

really well in this visualization we see

that this time significantly more of the

wanted classes have been detected but we

have new problem duplicated detections

each object is now associated with two

or even three bounding boxes to solve

this problem we'll use non-max

suppression non-max suppression is an

algorithm that use intersection over

Union IOU to estimate the degree to

which detections overlap with each other

detections with high IOU over the set

threshold and low confidence are then

discarded some time ago I wrote a blog

post about nms so if you want to learn

more the link is in the description once

again we introduced only minor change in

the code this time we use the with nms

function available in supervision and

set an aggressive IOU threshold value of

0.1 the lower the value within the range

between 0 and one the smaller the

overlap between detections must be for

one of them to be discarded comparing

the results at each stage we see that

ultimately we could detect a lot more

objects from Wanted list while

maintaining the high quality of obtained

results I know that confidence levels at

around 1% appear quite unusual but as I

said in case of yellow World it just

works yellow World shines brightest when

processing videos not just individual

images according to the paper it can

achieve up to 50 FPS on Nvidia v00

during my experiments I got 15 FPS on

Nvidia T4 a much budget friendly

alternative transition from processing

single image to processing entire videos

is fairly straightforward we just Loop

over the frames of the video and run

inference for each of them because we

look for the same objects on each frame

we only need to encode our class list

once in our next experiment we will

attempt something more ambitious than

detecting dock class in this video we

see an object with holes that are being

filled with yellow substance let's check

if yellow world will be able to locate

those filled

holes because the objects that we are

looking for are hard to Define choosing

the right prompt was a challenge I tried

several variants But ultimately using

Color reference proved to be the most

effective one thing that I didn't me

mention in the intro but I think is

quite important authors of the paper

show examples of using color and

position as reference so don't hesitate

to use them in your

prompts let's load the first frame of

our video and run set classes method

setting our prompt to Yellow feeling as

before we run inference with low

confidence threshold of

0.02 now our model successfully detects

individual holes but along with them it

accidentally detect the entire object

with holes we observed the same effect

few months ago while testing grounding

dyo both models tend to return large

highlevel bounding boxes that in some

sense meet our criteria but are not the

objects that we are looking for this

especially happens when processing

images or video with low resolution to

solve this problem we'll filter our

detections based on the relative area if

a given bounding box occupy a larger

percentage of the frame than a set

threshold it will be dropped it sounds

quite complicated but implementing it

with supervision is a piece of cake

first using the video info class will

obtain information about the resolution

of the video the width and the height of

the entire frame knowing this values

calculating the total area in pixels is

quite straightforward on the other hand

supervision also provides easy access to

the area of individual bounding boxes

now all we need to do is divide the

individual areas by the area of the

entire frame to obtain the relative area

thanks to naai the entire operation can

be vectorized and performed

simultaneously for all bounding

Boxes by setting a relative area

threshold in my case

0.1 we can construct a l iCal condition

that allows us to filter out bounding

boxes larger than 10% of entire frame

when we visualize the result we see that

only detections representing the filled

holes remain awesome the final step is

to process the entire video and save the

result in separate file to do this we

will use two utilities available in

supervision the frame generator will

help us Loop over frames of the source

video while video sync will take care of

recording according the result all the

code we've wrote so far goes inside the

for Loop and will be triggered for each

frame of the video we see that yellow

world has successfully solved a task

that traditionally would have required

training of a model on the custom data

set so is yellow World a golden solution

the model that ends training on custom

data sets well no there are still cases

where I would choose model trained on

custom data set over zero detector like

yellow world let's start with obvious

issue

latency yellow world is much faster than

its predecessors but still significantly

slower than state-of-the-art real-time

object detectors therefore if we need

fast processing and have limited

computational resources we still need to

rely on more traditional Solutions

yellow world is also less accurate and

reliable than detectors trained on

custom data set there are of course

cases when yellow world may prove useful

especially when we tightly control the

environment a perfect example is one of

the Snippets I showed you in the intro I

think yellow world could be successfully

deployed a cound factory for instance to

keep count of daily

production unexpected objects cannot

suddenly come into the view of the

camera and qu sounds move one by one on

the other hand preparing the suitcase

demo took me quite some time and and I

still must admit I did some cherry

picking yellow World excels at detecting

suitcases however other objects often

appear in the frame as well and the

model May occasionally misclassify those

objects as

suitcases so to sum it up I encourage

you to prototype with yellow World check

if it works in your use case use the

techniques that I showed you today to

refine detections however be prepared

that at the end of the day you may still

train your model on custom data set

especially if you don't control the

environment you have poor camera

placement or you simply look for objects

that for some reason cannot be detected

yellow World opens the way to use cases

that so far have been impossible like

open vocabulary video processing or

using zero shut detectors on the edge

but it's just the beginning by combining

yellow world with fast segmentation

models like Fast sum or efficient sum we

can build a zero shut segmentation

pipeline that run dozens of times faster

than grounding dyo plus some combo I

showed you a few months ago inspired by

this idea I created a hagging face space

where you can use yolow world and

efficient Sam to process your images and

videos you can even take this idea a

step further and build video processing

pipeline that automatically removes

background behind detections use the

fusion base models to dynamically

replace them or completely remove them

from the frame by combin combining

yellow World efficient Sam and prop

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painter

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yellow world is an important step in

making open vocabulary detection faster

cheaper and wildly available maintaining

almost the same accuracy it is 20 times

faster and five times smaller than

leading zero shot object detectors I

highly encourage you to take a look at

our materials the link to cookbook and

hagging face space are in the

description below if you have any

questions leave them in the comment I'll

try to answer all of them during the

upcoming Community session and that's

all for today if you like the video make

sure to like And subscribe and stay

tuned for more computer vision content

coming to this channel soon my name is

Peter and I see you next time

bye

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