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YOLO Basic Introduction. | You only LIVE once. | Object Detection.
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YOLO algorithm detects and classifies objects in an image. It just needs one forward pass of the network at the test time.
This is a very basic introduction video for those who want a first hand simple explanation of the algorithm.
FAQs about the video:
1. Can IOU be more than 1.
2. If an object is very big, which grid's bounding box will be responsible to detect it.
3. What if there are more than one object in one grid?
FAQs about the channel:
1. Why do you make series on a particular topic (eg. YOLO) when this is a topic which could have been covered in one video?
2. When will you make videos on common machine learning topics instead of research papers?
3. Subscribe. WHY?
4. Comment. WHY?
Answers on FAQs about the video.
1. No, union is always greater than the intersection.
2. The grid which contains the center of the object is responsible for detecting the object.
3. There is something called Anchor boxes which are responsible for detecting multiple bounding boxes in one grid.
Answers on FAQs about the channel.
1. Explaining one concept in great detail, will help people understand one very important paper to the fullest. This will make the understanding of other papers easier. For ex. the loss function of YOLO, will help you get a very good intuition of how loss functions in general work. So to explain all this in detail, I make a full series on just one paper. I have started a series on Viola Jones algorithm, where I plan to take up each topic in detail so that any paper similar to this or based on this will be easy to digest.
2. Soon, as I think balancing between common topic of ML which are essential for removing doubt and help stuff easier in general is as important as doing some semi advanced stuff for better growth in the field of ML.
3.It is very common in machine learning students to leave some doubts for later whenever they encounter an advanced or difficult topic. Even though there is nothing bad in that because machine learning concepts are difficult at times, but since those concepts are important, they come to haunt you back. So, if you have notifications for my channel, on, I am pretty sure that some of my videos that I will post will be on the topics that you have missed or are still not clear. The best part of this exercise will be that on some random day you will have an important concept clear without even explicitly going to YouTube to clear those concepts out.
4.Comments help both of us connect inside YouTube. If you post a doubt, I will try to answer that doubt as a reply. If the doubt is good, I will make a video. Yes, I definitely plan to make videos explicitly on complex doubts that are difficult to understand without pictorial demonstrations and a step by step approach. I think this will help the community in the best possible way.
Your comments will also help me know my mistakes and how can we grow as machine learning enthusiasts.
Social media:
Thank You.
This is a very basic introduction video for those who want a first hand simple explanation of the algorithm.
FAQs about the video:
1. Can IOU be more than 1.
2. If an object is very big, which grid's bounding box will be responsible to detect it.
3. What if there are more than one object in one grid?
FAQs about the channel:
1. Why do you make series on a particular topic (eg. YOLO) when this is a topic which could have been covered in one video?
2. When will you make videos on common machine learning topics instead of research papers?
3. Subscribe. WHY?
4. Comment. WHY?
Answers on FAQs about the video.
1. No, union is always greater than the intersection.
2. The grid which contains the center of the object is responsible for detecting the object.
3. There is something called Anchor boxes which are responsible for detecting multiple bounding boxes in one grid.
Answers on FAQs about the channel.
1. Explaining one concept in great detail, will help people understand one very important paper to the fullest. This will make the understanding of other papers easier. For ex. the loss function of YOLO, will help you get a very good intuition of how loss functions in general work. So to explain all this in detail, I make a full series on just one paper. I have started a series on Viola Jones algorithm, where I plan to take up each topic in detail so that any paper similar to this or based on this will be easy to digest.
2. Soon, as I think balancing between common topic of ML which are essential for removing doubt and help stuff easier in general is as important as doing some semi advanced stuff for better growth in the field of ML.
3.It is very common in machine learning students to leave some doubts for later whenever they encounter an advanced or difficult topic. Even though there is nothing bad in that because machine learning concepts are difficult at times, but since those concepts are important, they come to haunt you back. So, if you have notifications for my channel, on, I am pretty sure that some of my videos that I will post will be on the topics that you have missed or are still not clear. The best part of this exercise will be that on some random day you will have an important concept clear without even explicitly going to YouTube to clear those concepts out.
4.Comments help both of us connect inside YouTube. If you post a doubt, I will try to answer that doubt as a reply. If the doubt is good, I will make a video. Yes, I definitely plan to make videos explicitly on complex doubts that are difficult to understand without pictorial demonstrations and a step by step approach. I think this will help the community in the best possible way.
Your comments will also help me know my mistakes and how can we grow as machine learning enthusiasts.
Social media:
Thank You.
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