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.
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.
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.
3 роки тому+23
please make a full project on this from code to deploying
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
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.
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?
Hi sir i have a doubt. You explained that the grid is considered to have an object only if the center of the bounding box is in that grid.But how do we find the boundung box and center, then?
The issue is that in multiple image classification, you assumed the center of the two objects (human and dog) as given. However, if I am correct, based on what you mentioned prior this point in the video, their center can be calculated after they are detected! Isn't this create a loop?
I think center information is only provided during the training when we have the ground truth. During inference, model just predicts the bounding boxes
sir i saw this video many times but i cant understand one point i.e iou and max probablity both are same what is the diffrence betwwen these 2 both give the same result
I honestly don't get the IOU, but the max propability, just selects he bounding box with highest confidence threshold. Propabbly not elevant anwser after 3 years but maybe someone finds it helpful xd
Hey man, good stuff. I am not a coder so pardon my question but do you know if YOLO7 or 8 can be used for body measurement and not just object detection?
Thank you for the practical tutorials.🙏🙏🙏 I have the following questions: Can we use the saved weights from YOLOv7 instance segmentation for a classification problem? We have a binary classification problem with 500 images, one class having only 30 images and the rest belonging to the other class. Can we extract features using instance segmentation on the images with fewer samples and then use all the features for classification?
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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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
The best explanation for YOLO! It's really helpful. Thank you.
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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
Among all the yolov explaining videos this one makes the most sense! Thanks
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.
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.
I used YOLO before I understood what it was, thank you for helping me understand how YOLO works
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.
please make a full project on this from code to deploying
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.
Perfect and Clear Introduction to YOLO
Glad it was helpful!
thanks mate, went through a couple of videos and your's the one that explain it the best
I like this video very much. You explained the working of YOLO very simple , crystal and clear way. Thank you very much. Expect more.
Every software engineers should subscribe this best channel omg you are just fire 🔥 wow
Such a perfect introduction to YOLO. Thanks!
Excellent introduction to YOLO. Looking forward for code deployment video
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..
Gone thru many udemy courses, no one explains like you! Thanks for the efforts!
it my first time around and i have already got a good level on YOLO...thanks for explanation///
I am new to ML but still i understand what you have said bout YOLO great work
Thank you very much sir !!! Egarly waiting for next part
Sir your explanation is amazing in the field of data science
You have explained things so well Ma Sha Allah, stay blessed and keep up the good work.
man, this was such a good explanation to YOLO!
This was amazing! love it
At 7:28, that looks more like 2 x the width of the grid cell. Why is it 3?
The amount of good information and dogs in this video make me happy :)
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 !
excelente tutorial
well worth watching. thanks for this. i had to pause where you said to as well. then I got it.
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
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 for the presentation, it is easier for me to understand compared to the paper
Excellent explanation, you teach these topics in such a way that even a layman can understand
Nice work. You deserve more than one upvote. Sadly I can only give one.
Thankyou Sir that was a very good and simple explanation of a complex algorithm :) Thankyousomuch sir
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.
Tks a lot sir, perfect explanation....
Very nice, excellent description. Thank you!
Yeah! Very clear explanation.
Glad it was helpful!
Thanks for sharing your knowledge
Really good explanation. I just have one doubt. How are bounding box measures calculated in yolo algo?
yes, it is the million dollar question :)
Helpful. Nice work. Thank you so much.
Glad it was helpful!
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
Sir
The explanation was very clear
And can I get the ppt that you used in the explanation
Thanks in advance
Glad I watched ur video ❤❤❤
very nice explanation , btw either it will help to detect either brand logo is fake or not?
Thanks for the explanation. It's help me alot to understand yolo 👍
Great Explanation. Thank you
Waiting for more videos on yolo👏👏
yup next one will cover coding part
better than andrew ng's explanation thanks!
The best video!!
Best explanation till date
This is a great video, but the real magic of YOLO is in the loss function. Would you do a video on that?
Thank you alot this explanation is all i ever needed
thank you so much for this, very easy to understand !
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 video.. salute !
Great explanation. The images helped to understand concept very easily, thanks
Great Sir
Thanks, it's an excellent explanation, just what I needed.
Hi, This is a very effective video. please provide a full project video with source code like face recognition project.
Best explanation
I like it bro clear and simple explanations
Great explainaition
Thank you very much. your explanation was great!
احسنت الشرح والتفصيل شكرا لك
best explanation... you are doing a great job.
I really loved this video! Thank you!
Brilliant!!!!!!!!!
Hi sir i have a doubt. You explained that the grid is considered to have an object only if the center of the bounding box is in that grid.But how do we find the boundung box and center, then?
Great video!
Excellent 👍
This video was fantastic. Thank you
The issue is that in multiple image classification, you assumed the center of the two objects (human and dog) as given. However, if I am correct, based on what you mentioned prior this point in the video, their center can be calculated after they are detected! Isn't this create a loop?
I think center information is only provided during the training when we have the ground truth. During inference, model just predicts the bounding boxes
Nicely explained everything Thank you sir
sir i saw this video many times but i cant understand one point i.e iou and max probablity both are same what is the diffrence betwwen these 2 both give the same result
he says at the beginning that prob isn't taken into account... and at last he says it is the criteria... so I'm confused!!!
I honestly don't get the IOU, but the max propability, just selects he bounding box with highest confidence threshold. Propabbly not elevant anwser after 3 years but maybe someone finds it helpful xd
Hey man, good stuff. I am not a coder so pardon my question but do you know if YOLO7 or 8 can be used for body measurement and not just object detection?
you made our life easier
Thank you for the practical tutorials.🙏🙏🙏
I have the following questions:
Can we use the saved weights from YOLOv7 instance segmentation for a classification problem?
We have a binary classification problem with 500 images, one class having only 30 images and the rest belonging to the other class. Can we extract features using instance segmentation on the images with fewer samples and then use all the features for classification?
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!
Please make a video on custom data to train efficient det with implementation, format to require train effecient det model by google brain. Thank you!
This is a brilliant tutorial for YOLO. Thank you so much!
Nicely explain
Cool explanation, thanks!!
Best explanation online! Thanks for it. One question is that it is unclear how anchor boxes work?
Thanks for the brief explanation. Wanted to know how center of object can be decided here?
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.
Congratulations on the video. Does yolo only recognize objects or does it classify emotions as well?
Great video. Did you do any image operation to detect overlap of two detected objects in same image ?
Today's best face detection algorithm?yoko also used in face detection?
Amazing explanation as always..
Great explaination of NMS.
Glad it was helpful!
Excellent explanation