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.
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
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 !
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.
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!
hey, your video is so helpful... It's badly in need of a video of HYPER-PARAMETERS TUNING in tensorflow pls make a video about this topic thank you so much
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?
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
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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 😂😂
Among all the yolov explaining videos this one makes the most sense! Thanks
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.
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.
I really like your style of explanation. It's very clear and informative.
Glad it was helpful!
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
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
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.
Such a perfect introduction to YOLO. Thanks!
I used YOLO before I understood what it was, thank you for helping me understand how YOLO works
I like this video very much. You explained the working of YOLO very simple , crystal and clear way. Thank you very much. Expect more.
thanks mate, went through a couple of videos and your's the one that explain it the best
Great explanation of YOLO. And I need to say thank you for all your tutorials. I learnt a lot from you. Keep it up!
Excellent introduction to YOLO. Looking forward for code deployment video
Perfect and Clear Introduction to YOLO
Glad it was helpful!
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
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I am new to ML but still i understand what you have said bout YOLO great work
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 !
You have explained things so well Ma Sha Allah, stay blessed and keep up the good work.
The amount of good information and dogs in this video make me happy :)
please make a full project on this from code to deploying
Gone thru many udemy courses, no one explains like you! Thanks for the efforts!
Very clearly explained. Thank you so much
it my first time around and i have already got a good level on YOLO...thanks for explanation///
man, this was such a good explanation to YOLO!
You clear the concept in 16 min thanks bro..
Sir your explanation is amazing in the field of data science
Thank you very much sir !!! Egarly waiting for next part
Every software engineers should subscribe this best channel omg you are just fire 🔥 wow
Best explanation till date
the best explanation honestly you are a master
well worth watching. thanks for this. i had to pause where you said to as well. then I got it.
Glad it was helpful!
excelente tutorial
Waiting for more videos on yolo👏👏
yup next one will cover coding part
Yeah! Very clear explanation.
Glad it was helpful!
The best Explanation of Yolo thank you very much
Amazing as always! Thank you for providing this information and helping unravel important topics
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.
Nice work. You deserve more than one upvote. Sadly I can only give one.
Brilliant explanation, thank you so much!
Excellent explanation, you teach these topics in such a way that even a layman can understand
thank you for the presentation, it is easier for me to understand compared to the paper
You are really awesome, explained it clearly
This was amazing! love it
Very nice, excellent description. Thank you!
תודה!
Helpful. Nice work. Thank you so much.
Glad it was helpful!
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!
best explanation... you are doing a great job.
Thankyou Sir that was a very good and simple explanation of a complex algorithm :) Thankyousomuch sir
I like it bro clear and simple explanations
This video was fantastic. Thank you
Thanks for sharing your knowledge
Tks a lot sir, perfect explanation....
Great explaination of NMS.
Glad it was helpful!
Thanks for the explanation. It's help me alot to understand yolo 👍
Glad I watched ur video ❤❤❤
thank you so much for this, very easy to understand !
Thank you alot this explanation is all i ever needed
Excellent 👍
This is a great video, but the real magic of YOLO is in the loss function. Would you do a video on that?
I really loved this video! Thank you!
Cool explanation, thanks!!
Amazing explanation as always..
Great Explanation. Thank you
Nicely explained everything Thank you sir
hey, your video is so helpful...
It's badly in need of a video of HYPER-PARAMETERS TUNING in tensorflow
pls make a video about this topic
thank you so much
great video.. salute !
Great explanation. The images helped to understand concept very easily, thanks
you made our life easier
Really good explanation. I just have one doubt. How are bounding box measures calculated in yolo algo?
yes, it is the million dollar question :)
At 7:28, that looks more like 2 x the width of the grid cell. Why is it 3?
very nice explanation , btw either it will help to detect either brand logo is fake or not?
Great video!
Thanks, it's an excellent explanation, just what I needed.
Excellent explanation
The best video!!
Nice explanation!
Glad it was helpful!
Hi, This is a very effective video. please provide a full project video with source code like face recognition project.
amazing content and good explanation
Thank you 😇
Thank you very much. your explanation was great!
You can always trust the Indian guys on TH-cam when it comes to computer science
Congratulations on the video. Does yolo only recognize objects or does it classify emotions as well?
This is a brilliant tutorial for YOLO. Thank you so much!
Great 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
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
Best explanation online! Thanks for it. One question is that it is unclear how anchor boxes work?
Exceptional.
Nice, I enjoyed the way that you explain it.
Glad you liked it!
Brilliant!!!!!!!!!
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.
Best explanation
Thank you! Now it’s clear for me. Which app do you use for creating slides and graphic objects (tensors, tables, etc)?
Power point
Good job sir