YOLO

Calling all adventure seekers and texhnocrax, regardless of your tech prowess! Brace yourselves for another extraordinary escapade into the captivating world of CNN. Yue and Yedhant, our fearless explorers, have stumbled upon a mind-boggling algorithm known as YOLO. But wait, there's a twist! YOLO isn't just a trendy slang, my friends. In the world of artificial intelligence, it stands for something equally thrilling - "You Only Look Once."

Now, hold onto your hats because things are about to get seriously exciting! We're about to unravel the magic behind this cutting-edge technology and dive headfirst into the fascinating realm of AI. Trust us, you don't need to be a tech wizard to join in on this incredible journey. Whether you're a self-proclaimed tech newbie or a seasoned pro, prepare to have your mind blown as we unravel the secrets of intro to AI in the most captivating and enjoyable way possible.

So, let's set aside any preconceived notions and embark on this adventure together and see what yue and yedhant are upto. 

As our intrepid adventurers, Yue and Yedhant, made their way back from the lab, their spirits were soaring high after their enlightening journey into the world of CNN. The excitement was palpable, and their joy spilled over as they found themselves humming a merry tune while strolling down the road. But lo and behold, what caught their eyes? A hoverboard, beckoning them with its futuristic charm. Without a second thought, they hopped on, ready to embark on another exhilarating escapade.

As Yue took the reins and zoomed ahead on the hoverboard, her exhilaration knew no bounds. The wind rushing through her hair, the adrenaline pumping through her veins, she couldn't help but go faster and faster. Sensing the need for caution, Yedhant called out, "Take it slow, Yue! You don't want to risk falling." However, Yue, full of playfulness, responded with a mischievous grin, "YOLO...you only live once!" The words hung in the air, evoking laughter from Yedhant, who remarked, "People these days, using YOLO as an excuse for their crazy antics."

Yue chuckled and simply replied, "It is what it is." Little did they know, a fascinating revelation awaited them. Yedhant, with a twinkle in his eye, shared a surprising fact, "You know, AI has its own YOLO too, and trust me, it's equally as fun!" Intrigued, Yue's curiosity piqued, prompting her to inquire further. Yedhant, with a grin, explained, "It's a neat concept used for object detection. YOLO stands for 'You Only Look Once'."

The realization dawned upon Yue, and she couldn't help but exclaim, "That sounds intriguing!" The world of AI never failed to amaze her. Yedhant nodded in agreement, adding, "Absolutely! It's a fascinating technique that revolutionizes how we detect and identify objects. The possibilities are endless!" 

Yue's eyes lit up, and she proposed, "How about embarking on a new adventure?" 

Yedhant, matching her fervor, chimed in, "YOLO, so why fear? Let's dive right in!" Yue playfully prodded, "Are you referring to AI's YOLO or my YOLO?" Both laughed and procedded to enter the realm of YOLO.

As Yue and Yedhant delved deeper into the world of YOLO, they found themselves on a quest to understand the inner workings of this remarkable AI concept. In the gallery of the labryinth they found brief about yolo. 

"Yolo is an innnovative object detection algorithm known for its speed and accuracy. It was first introduced by Joseph Readmon in the year 2015. Since then yolo had its own journey and evolution, most recent being YOLO V8. Yolo can detect and identify objects in real-time, all in one pass, with astonishing accuracy and speed. It is currently adapted in various technologies rom autonomous vehicles navigating busy streets to security systems monitoring crowded spaces. "

Yue marveled at the potential and exclaimed " It wont be wrong saying that YOLO would be the power behind the eyes of future robots!"

Yedhant: Yes thats true its a marvelous algorithm lets try to go in detail, as we already read that yolo is state of the art object detection algorithm which was introduced by Joseph readmon in the year 2015. The uniqueness of this approach is that the authors frame the object detection problem as a regression problem instead of a classification task by spatially separating bounding boxes and associating probabilities to each of the detected images using a single convolutional neural network (CNN). 

Yue: What are bounding boxes and what is regression?

Yedhant: Dont worry we will go over each topic, before moving forward lets see what is Object detection

...

Object Detection

Yedhant leaned in, a glint of enthusiasm in his eyes, as he began to elucidate the intricacies of object detection to Yue. "Yue, you know, object detection is like the Sherlock Holmes of computer vision. It's the technique that helps us identify and pinpoint objects within images or videos."

Yue nodded, intrigued. "So, how does it work? What's the deal with image localization?"

Yedhant grinned, ready to dive into the details. "Image localization is the magic behind it. It's the process of precisely locating objects using what we call 'bounding boxes.' These boxes are like invisible frames that we draw around the objects, showing where they are in the image."

Yue furrowed her brows, pondering. "So, is this like classifying objects in images?"

Yedhant shook his head gently. "Not quite, Yue. That's where it gets interesting. Object detection isn't just about saying 'this is an object.' It goes a step further. Image classification is about putting an image or object into a category, like saying 'this is a cat.' But object detection tells you not only what's in the image but also where it is, precisely. It's like knowing there's a cat in the room and being able to point to exactly where it's sitting."

Yue's face lit up with understanding. "I see! It's about not just recognizing objects but also locating them within the image. Like a treasure map with 'X' marking the spot!"

Yedhant chuckled. "Exactly! And this illustration," he continued, pointing to a visual aid, "shows how it works in practice. See the image? We've detected an 'object,' and in this case, it's clearly a 'Person.' But it's not just knowing there's a person; it's drawing that bounding box around them, telling us precisely where they are."

Yue smiled, feeling the pieces come together. "That's fascinating, Yedhant! Object detection is like giving computers the power of both sight and location."

Yedhant nodded with a satisfied grin. "You've got it, Yue. It's like teaching computers to see and point to what they see, all in one go. It's the backbone of many exciting applications, from self-driving cars to security systems. Previously various algorithms such as Sliding Window Object detection, R CNN, Fast R CNN, Faster RCNN were used. But in 2015 YOLO was invented which outperformend  all the previous object detection algorithm and from then various version of YOLO has been launched."

Yue: That's cool. But why is YOLO so popular?

Yedhant: YOLO is a superstar for several reasons. First, it's incredibly fast, processing images at 45 Frames Per Second (FPS). Second, it's highly accurate with minimal background errors. Third, it adapts well to new domains, and it's open-source, so the community keeps improving it.

Yue: Speed and accuracy, that's impressive. How did the author came with this approach?

Yedhant: Thats interesting question, for yolo the authors frame the object detection problem as a regression problem instead of a classification task. They did this by spatially separating bounding boxes and associating probabilities to each of the detected images using a single convolutional neural network (CNN). This helped in  combining the tasks of localization and classification into a single model, resulting in efficient and precise object detection, which has contributed to its popularity in the field of computer vision.

Yue: Changing perspective can make such a big difference!

Yedhant: Certainly its similar to life! And interestingly YOLO's architecture is a bit like a puzzle. It starts by resizing the input image and passing it through a series of convolutional layers which we learned last time. These layers work together to identify objects and their positions. They even use tricks like batch normalization and dropout to improve performance and prevent overfitting.

How YOLO works in laymen terms

Yue: I'm curious, how does YOLO actually find stuff in pictures?

Yedhant: Well, think of it like a game of treasure hunt. YOLO is like a super-duper treasure hunter for pictures. It uses four cool tricks: "special moves," "aiming," "checking the treasure," and "cleaning up."

Yue: Oh, that sounds exciting! What's the first trick, "special moves"?

Yedhant: "Imagine you're exploring a jungle. Sometimes, you can take shortcuts or do special moves to find the treasure faster. YOLO has these special moves called "residual blocks." They help it look at pictures and find stuff really quickly.

Yue: So YOLO moves fast through pictures with those blocks! What's next, "aiming"?

Yedhant: Yes, "aiming" is like when you're trying to hit a target in a game. YOLO is great at aiming. It predicts where the treasure (or objects) is by drawing boxes around them. It's like saying, "Hey, the treasure is right here!"

Yue: That's awesome! "Checking the treasure" sounds interesting. What's that?

Yedhant: Imagine you found something shiny in the jungle. You want to make sure it's really the treasure, not just something shiny. YOLO does the same thing with "Intersection Over Unions (IOU)." It checks how well its prediction matches the real treasure. It's like saying, "Yep, that's definitely the treasure!"

Yue: So it's like being sure it's the real deal! What's the last trick, "cleaning up"?

Yedhant: "Cleaning up" is like keeping your room neat and tidy. After YOLO finds lots of predictions, it doesn't want to count the same treasure multiple times. So, it uses "Non-Maximum Suppression (NMS)" to remove the extra predictions. It's like saying, "Okay, we found the treasure once; we don't need to count it again."

Yue: Ah, so YOLO makes sure everything's in order! YOLO does sound like a treasure hunter for pictures!

Yedhant: You got it! YOLO is super cool at finding things in pictures fast and accurately, just like a treasure hunt!

Yue: thanks for that easy understanding it makes yolo so simple .

Yedhant: I am glad I could help now as we know whats yolo lets dive deeper into it with an example

2nd level of YOLO labyrinth

Yue: That sounds fun, it will make things more clearer and give us deeper understanding about the system

Yedhant: Thats the plan, now taking the next step as we saw earlier that in object detection we just dont classify the object also localize it. So lets take an example where we want to determine if the image have cars or person, we could represent those numerically as this. 

Where ,

Pc= Probability wherether there is a person or vehicle

Bx= bounding box center x coordinate

By= Bounding box center y coordinate

Bw= Bounding box width

Bh= bounding box height

C1=class 1 i.e person

C2= clas 2 i.e vehicle

So similarly we could represent any image and object and similarly the neural network would give this matrix for object detection

Yue: SO to train the neural network we need to label these images

Yedhant: Yes since it is a supervised learning problem we have to give the bounding boxes and since computer just understand numbers we have to convert into these kind of vectors as mentioned above.  Using 1000s of such images and these vectors we would train the neural network as shown below, in a way that  when we provide image to the neural network it will tell us the vector.

Yue; thats pretty neet , so after training this cnn if we provide it with image it should give us the vector for object in it, right?

Yedhant: Absolutely correct , If you give trained network a image as below it will output the vector as explained earlier 

Grid cell

Yue: What if there are multiple objects in an image?

Yedhant: That's a great question, and I was actually hoping you'd bring that up. When we're dealing with multiple objects in an image, it becomes challenging to determine the optimal dimensions of the neural network because the number of objects, denoted as 'n', can vary. Unlike scenarios with a fixed number of objects, where we could adjust the neural network accordingly, handling variable 'n' is more complex. One approach is to set an upper limit on the number of objects.

Yue: But what if we encounter more objects than that upper limit?

Yedhant: Precisely! In cases where we exceed the upper limit, this approach falls short. To address situations with varying numbers of objects, the YOLO (You Only Look Once) algorithm employs the concept of grid cells. To illustrate this, consider a simple example where we divide the image into a 4x4 grid for clarity. In this image, we have both a person and a car. Each of these grid cells will contain the vector we discussed earlier.

Yue: So, to clarify, we calculate similar vectors for all the remaining grid cells. If an object doesn't have its center in a particular grid cell, we assume there are no objects in that cell.

Yedhant: Exactly. So, after obtaining vectors for each grid cell, we end up with a 4x4x7 volume, where 4x4 represents the grid dimensions, and 7 represents the vectors we've discussed. Now, just like our previous training process, we train the neural network. However, for each image, we have 16 of these vectors.

Yue: So, the output we receive will also consist of 16 vectors?

Yedhant: Yes, that's correct. This methodology is known as the YOLO algorithm because we're not processing the image 16 times individually for each cell. Instead, we send the entire image at once and get the output in a single forward pass, as depicted in the diagram below. That's why it's called "You Only Look Once."

Yue: So, to clarify, we calculate similar vectors for all the remaining grid cells. If an object doesn't have its center within a grid cell, we assume there are no objects in that cell.

Yedhant: Precisely. After obtaining these vectors for each grid cell, we end up with a 4x4x7 volume, where 4x4 corresponds to the number of grids, and 7 represents the vectors we've discussed. Moving forward, we train the neural network in a manner similar to our previous approach. However, for each image, we now work with 16 of these vectors.

Yue: So, the output we receive will also consist of 16 vectors?

Yedhant: Absolutely, yes. This approach is known as the YOLO algorithm because it doesn't involve redundant repetitions. We don't process each of the 16 cells individually; instead, we send the entire image as a single unit, resulting in output from one forward pass, as depicted in the diagram below. This is why it's aptly named "You Only Look Once."

Intersection Over Union (IOU)

Yue: Ah, I see, that's why we call it YOLO.

Yedhant: Yes, but there's a bit more to it. The algorithm can sometimes detect multiple bounding boxes for the same object.

Yue: That could be problematic since there's only one object. How do we handle that?

Yedhant: Let's discuss how we solve that. You see, the neural network's output includes a probability value, denoted as PC, which indicates the likelihood of an object being present. With each bounding box, it provides this probability.

Yue: So, we could simply choose the bounding box with the highest probability, right?

Yedhant: Exactly, Yue. While selecting the bounding box with the highest probability might work in some scenarios, like in this example where there are only two different objects, it can get tricky when we have multiple objects of the same class. For instance, if two people are standing side by side in an image, the computer won't know which vector corresponds to which object. So, we may inadvertently neglect the object with the lower probability.

Yue: I see, that could lead to confusion. How do we overcome this challenge?

Yedhant: That's where YOLO's secret sauce, Intersection over Union (IoU), comes into play. Let's break down IoU in a simpler way. Imagine YOLO as a detective trying to find objects in a picture. Sometimes, it gets so excited that it spots multiple boxes for a single object, just like two detectives identifying the same clue.

Instead of blindly selecting the box with the highest "detective confidence" (probability), we introduce the "Overlap-O-Meter." This nifty tool measures how much these boxes overlap or intersect, much like asking the detectives, "Hey, are you both looking at the same thing?"

If the overlap is substantial, like two detectives pointing at the exact same spot, we can confidently conclude that they are on the same case. However, if the overlap is minimal, it's like they're on different trails.

So, here's the rule: If the overlap exceeds a certain threshold, similar to when the detectives are very sure they're observing the same thing, we keep just one box. This ensures we don't count multiple boxes for a single object, avoiding any confusion.

In essence, IoU assists YOLO's detectives in working together, ensuring accurate object detection while adding some excitement to their object-finding adventure! 🕵️‍♂️🕵️‍♀️

Yue: That was a fun explanation! So, in the case of our example, suppose we have multiple vectors or bounding boxes for the vehicle. Let's assume there are two bounding boxes with probabilities of 0.8 and 0.9. Yue snaps her fingers The blue represents the intersection, and the black is the union; we calculate their division to determine if both bounding boxes belong to the same object.

Yedhant: I am happy to see that now you are able to click and have image appear.

Yue: chuckles!

Yedhant: So, now that we can identify which bounding boxes are for the same object, we can discard the bounding boxes with an IoU above a certain threshold and keep the one with the highest probability, as you mentioned earlier. In this case, we'd drop the bounding box with a probability of 0.8 and retain the one with 0.9. We repeat this process for all the bounding boxes. This technique is also known as non-maximum suppression.

Anchor boxes

Yue: That's a neat trick, but I still have a question. We use grids to handle multiple objects, but what if one grid contains the center of multiple objects?

Yedhant: That's a great question, and it ties back to what we discussed earlier. To address this, we can increase the complexity of our vector. Instead of having just 7 elements, we can expand it to 14 elements, and each set of 7 elements corresponds to what we call an anchor box.

Yue: But doesn't this seem to go against the reason we use grid cells in the first place? And how do we decide how many anchor boxes we should have?

Yedhant: You're absolutely right, Yue. Grid cells are meant to help us divide the image into manageable sections for object detection. However, when we encounter multiple objects within a single grid cell, it can indeed complicate matters.

Expanding the vector to 14 elements, with each 7-element subset corresponding to an anchor box, provides us with more flexibility. It allows us to use different anchor boxes for different object sizes and shapes, which can be incredibly useful.

As for the number of anchor boxes, that's an important consideration. Technically, we could have as many as we want, but it's crucial to strike a balance. Having too many anchor boxes can lead to confusion and increased computational complexity, while having too few might not effectively capture the variety of objects in the image.

So, it's a bit of a puzzle, finding the right number of anchor boxes. We need enough to handle diversity in object sizes and shapes, but not so many that our "mini-detectives" (the grid cells) become overwhelmed. It's about finding that sweet spot to make object detection work effectively within the grid framework!

Balance between number of grid cells and achor boxes

Yue: Do we also decide number of grid cell such that the number of anchor boxes are minimum

Yedhant: Today you are on fire Yue! The number of grid cells and the number of anchor boxes are indeed interconnected. Ideally, we want to strike a balance between these two factors.

Here's the thing: If we have too few grid cells, we might miss fine-grained details in the image, and it could become challenging to precisely localize objects. On the other hand, having too many grid cells might lead to inefficiency and increased computational cost.

So, it's like a delicate dance. We usually choose the number of grid cells based on the scale of the objects we expect to detect. Smaller objects might need more grid cells, while larger objects could be handled with fewer.

Similarly, the number of anchor boxes should align with the variety of object sizes and shapes we expect to encounter. If we anticipate lots of diversity, we'll need more anchor boxes. If objects in our scene are more uniform, we can get away with fewer.

In essence, it's about designing a grid and anchor box setup that's just right for the specific object detection task at hand. Finding that sweet spot is a bit like being a detective yourself! 🔍🕵️‍♀️

Yue: Thats tricky is there a thumb rule in seleting this information

Yedhant: Ah, selecting the right number of grid cells and anchor boxes can indeed be a bit of an art in the world of object detection. While there isn't a one-size-fits-all thumb rule, there are some guiding principles that can help us make these decisions:

Object Size and Scale:

 Consider the typical size and scale of the objects you want to detect. Smaller objects might require more grid cells and smaller anchor boxes, while larger objects may need fewer grid cells and larger anchor boxes.

Diversity of Objects: 

Think about the variety of objects in your dataset. If you have a wide range of object sizes, shapes, and aspect ratios, you'll likely need more anchor boxes to handle this diversity.

Computational Resources: 

Keep in mind your computational resources. More grid cells and anchor boxes mean increased computational demand. So, choose a configuration that your hardware can handle efficiently.

Training Data: 

The amount and quality of your training data also play a role. If you have a vast and diverse dataset, you might need more anchor boxes to capture the nuances.

Empirical Testing: 

Sometimes, the best way to determine the right setup is through experimentation. Try different combinations of grid cell sizes and anchor box numbers, and evaluate their performance on a validation dataset. This empirical approach can often lead to the best results.

Consulting Literature: 

Reviewing research papers and articles related to your specific object detection task can provide insights into what configurations have worked well for similar problems.

So, while there's no universal rule, it's a mix of domain knowledge, practical constraints, and experimentation that guides the selection of grid cells and anchor boxes. It's all part of the detective work in crafting an effective object detection system! 🔍🕵️‍♂️

Yue: That's a neat problem and interesting!

Yedhant: Oh, it sure is! And check this out – YOLO can detect lots of treasures in real-time.

Yue: Wow, that's pretty amazing! It's like how we explore and understand our world! 🌍🕵️‍♂️

Yedhant: Absolutely! That's why it's the rock star in many cutting-edge applications like robotics and autonomous vehicles.

Yue: Wow, this has been a thrilling ride! Thanks for being my guide through this exciting world of AI.

Yedhant: It's been my pleasure! How about we reconvene next week for another exhilarating adventure?

Yue: I'm already counting down the days! Thanks again for all your assistance.

And with smiles on their faces, they parted ways after this incredible journey. We hope you enjoyed this adventure with us, and we can't wait to see you next week for our next thrilling expedition! 🚀