Such a great communication happening in this video. The awareness of your audience at 8:15 is amazing. While it's true that "communication is what the listener does", to be a communicator, you must have empathy. Be proud of yourself for this.
What an awesome video! You really know how a student thinks. You answered all my questions - even the ones that I didn't realize I had! This was some excellent video format and pacing. I have liked and subscribed.
Awesome work Sir, You explain such complicated things in a way, it feels like cakewalk to understand. Thanks alot . Please make full python yolo implementation for video inputs.
thank you sir .. you have explained the content in very good manner. . with coding from scratch and i like it ... have a very nice moring..and many many best wishes from me to you !
Hi man. Finally, someone that understands how to make a great video. I just see 15'' and got what I was looking for. I also want to watch the rest because it is well explained. thanks
Doubt : after the model predictions non maximum suppression happens with respect to each grid (which is 4*4 here) IOU with predicted boxes... maximum prediction maintained..right
Thank you so much for creating this video! You really explained everything clearly. I was looking for an explanation about YOLO on other platforms but no one could explain this as clearly as you have. May I ask if I can translate your video into Chinese and share it on a Chinese video platform for all the people who are interested in learning YOLO but failed to find an excellent video like this one? Really appreciate your effort in making this video.
At 6:48 - Bh seems correct (1.3), but why is Bw=2? If Bw is the proportion of the grid cell's width, it looks like it should be ~1.5. At 7:28 - Here the dimensions seem like they should be Bw=2, and Bh=1.7, but they are shown in the vector as Bw=3 and By=2. Am I missing something, or are these meant to just be rough estimates for the demo?
I just love this video. It is the best explanation of the real 'concept' of YOLO algorithm. Thank you very much for your great effort and sharing the insight!
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Among all the yolov explaining videos this one makes the most sense! Thanks
My Deep Learning teacher couldn't explain this in 3 weeks the same way you did in 16 minutes, thank you very much.
so true
I think you didn't concentrate to your teacher lecture like you did in this video
@@Abraham33286yes..
Inki mind class me present rhti nhi hai.
Yaha akele dekh rha hai n.
To islie samjh gya 😂😂
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The best explanation for YOLO! It's really helpful. Thank you.
Such a great communication happening in this video. The awareness of your audience at 8:15 is amazing. While it's true that "communication is what the listener does", to be a communicator, you must have empathy. Be proud of yourself for this.
This is the best explanation that I have not seen any where
Only once I watched and got knowledge on yolo
Thank you so much for this knowledge sharing
What an awesome video! You really know how a student thinks. You answered all my questions - even the ones that I didn't realize I had! This was some excellent video format and pacing. I have liked and subscribed.
I really like your style of explanation. It's very clear and informative.
Glad it was helpful!
My God which kind of perfect explanation is this wow I don’t what to say bro just God bless you
Yes.. there is no details about network!, its only about box encoding
I watched a hour long video earlier and understood nothing, and now in just 16 min, I understood everything. Thanks a lot!
Glad you enjoyed it.
Such a perfect introduction to YOLO. Thanks!
Awesome work Sir, You explain such complicated things in a way, it feels like cakewalk to understand. Thanks alot . Please make full python yolo implementation for video inputs.
thanks mate, went through a couple of videos and your's the one that explain it the best
Perfect and Clear Introduction to YOLO
Glad it was helpful!
I like this video very much. You explained the working of YOLO very simple , crystal and clear way. Thank you very much. Expect more.
I used YOLO before I understood what it was, thank you for helping me understand how YOLO works
The amount of good information and dogs in this video make me happy :)
Excellent introduction to YOLO. Looking forward for code deployment video
I am new to ML but still i understand what you have said bout YOLO great work
man, this was such a good explanation to YOLO!
Very clearly explained. Thank you so much
excelente tutorial
Thank you very much sir !!! Egarly waiting for next part
please make a full project on this from code to deploying
Great explanation of YOLO. And I need to say thank you for all your tutorials. I learnt a lot from you. Keep it up!
You clear the concept in 16 min thanks bro..
thank you sir .. you have explained the content in very good manner. . with coding from scratch and i like it ... have a very nice moring..and many many best wishes from me to you !
it my first time around and i have already got a good level on YOLO...thanks for explanation///
well worth watching. thanks for this. i had to pause where you said to as well. then I got it.
Glad it was helpful!
At 7:28, that looks more like 2 x the width of the grid cell. Why is it 3?
Best explanation till date
The best Explanation of Yolo thank you very much
Sir your explanation is amazing in the field of data science
Yeah! Very clear explanation.
Glad it was helpful!
You have explained things so well Ma Sha Allah, stay blessed and keep up the good work.
תודה!
the best explanation honestly you are a master
Brilliant explanation, thank you so much!
Very nice, excellent description. Thank you!
This was amazing! love it
thank you for the presentation, it is easier for me to understand compared to the paper
Amazing as always! Thank you for providing this information and helping unravel important topics
Hi man. Finally, someone that understands how to make a great video. I just see 15'' and got what I was looking for. I also want to watch the rest because it is well explained. thanks
Thankyou Sir that was a very good and simple explanation of a complex algorithm :) Thankyousomuch sir
Nice work. You deserve more than one upvote. Sadly I can only give one.
Every software engineers should subscribe this best channel omg you are just fire 🔥 wow
Gone thru many udemy courses, no one explains like you! Thanks for the efforts!
Waiting for more videos on yolo👏👏
yup next one will cover coding part
You are really awesome, explained it clearly
Helpful. Nice work. Thank you so much.
Glad it was helpful!
Doubt : after the model predictions non maximum suppression happens with respect to each grid (which is 4*4 here) IOU with predicted boxes... maximum prediction maintained..right
Tks a lot sir, perfect explanation....
Thanks for sharing your knowledge
I like it bro clear and simple explanations
Thanks for the explanation. It's help me alot to understand yolo 👍
This video was fantastic. Thank you
best explanation... you are doing a great job.
Thank you alot this explanation is all i ever needed
thank you so much for this, very easy to understand !
Excellent explanation, you teach these topics in such a way that even a layman can understand
Great Explanation. Thank you
Cool explanation, thanks!!
I really loved this video! Thank you!
Glad I watched ur video ❤❤❤
Brilliant!!!!!!!!!
Great explaination of NMS.
Glad it was helpful!
Thanks, it's an excellent explanation, just what I needed.
The best video!!
Excellent 👍
Amazing explanation as always..
Nicely explained everything Thank you sir
You can always trust the Indian guys on UA-cam when it comes to computer science
11:22 IOU was performed over a pair of rectangles. How do you know which one of the two to "discard"? It is not clear at all.
Basically, you use Yolo non-max suppression for that. It discards values not meeting threshold and other criteria.
Excellent explanation
Great video!
great video.. salute !
you made our life easier
Exceptional.
Thank you very much. your explanation was great!
Great explanation. The images helped to understand concept very easily, thanks
Thank you so much for creating this video! You really explained everything clearly. I was looking for an explanation about YOLO on other platforms but no one could explain this as clearly as you have. May I ask if I can translate your video into Chinese and share it on a Chinese video platform for all the people who are interested in learning YOLO but failed to find an excellent video like this one? Really appreciate your effort in making this video.
احسنت الشرح والتفصيل شكرا لك
amazing content and good explanation
Nice explanation!
Glad it was helpful!
Splendid!
Good job sir
At 6:48 - Bh seems correct (1.3), but why is Bw=2? If Bw is the proportion of the grid cell's width, it looks like it should be ~1.5.
At 7:28 - Here the dimensions seem like they should be Bw=2, and Bh=1.7, but they are shown in the vector as Bw=3 and By=2.
Am I missing something, or are these meant to just be rough estimates for the demo?
I agree - that really putted me off, lol
Great Sir
wonderful video very informative
Best explanation
I just love this video. It is the best explanation of the real 'concept' of YOLO algorithm. Thank you very much for your great effort and sharing the insight!
Totally Awesome
excellent introduction!!!
Glad it was helpful!
Great explanation
This is a brilliant tutorial for YOLO. Thank you so much!
Great explainaition
Nicely explain
Thank you 😇
1:40 Why do we need Pc anyway when it is completely depend on C1 and C2? What's the point of probability when it can only be 0 or 1?