This is a wonderful explainer, thanks much for doing this 🙏 just getting to know more about YOLO and everything about object detection. Have been in a rabbit hole & watching multiple videos but no other video explains as succinctly as this one.
I missed something...for training and testing we have images plus bounding boxes in our inputs. But the final model input is image only. How is this handled?
Sir I have a doubt please help me, you told that: 1) Output layer consists of both classification(pc, c1, c2, ...) and bounding box values(bx, by, bh, bw) i.e, its a regression. 2) At 2:45 you told that for ouput layer softmax activation is applied, but how can a softmax activation be applied on bounding box values which is regression. 3) Ok let me assume that as the width and height values of Image and grid will be between 0 and 1 their may be a chance of using softmax, because softmax activation output will be between 0 and 1, but Iam not sure about this. But at 17:05 you told that in some cases in output layer bounding box width and height can be more than 1, but softmax which is applied to output layer can give values between 0 and 1, then how can bounding box width and height get the value more than 1. 4) Softmax when used in output layer it will consider bounding box values also as classes, so how can softmax be used in output layer. Can you please solve my confusion.
I have doubt could you please clear this...Suppose consider 3 X3 Grid (grid1,2,3,4,5,6) and consider a image ie car is spread over 2 grids (5th and 6th grids ) For Grid 5th, Yolo through CNN operation identifies image and its bounding box and vector cordinates are predicted covering two (5th and 6th) grid cells . Now for 6th grid also same operation will be applied . So now after whole grids operation does.5th and 6th grid predictions combined through NMS and IOU to single prediction where image is exactly PRESENT ? Is my understanding correct?
multiple anchor boxes are predicted for every object, YOLOv2 uses NMS (non maximal suppression through IoU (Intersection over Union)) and the Pc values to reduce down to a single anchor box for every object
Bro today yenaku interview coding test iruku ....object detection model built pana solirukanga help pana mudiyum ma ?I have one two day to complete the code
Bro I like this explanation but I have doubts How bh bw bx by will be calculated Means who is responsible to calculate And how bunch of images get bounding boxes for training
any resources to the newer or better methods to solve the limitations of anchor boxes? what if my image has 100 instances of different objects to be detected, can someone point a link or mention them
usage: yolo.py [-h] -i IMAGE [-c CONFIDENCE] [-t THRESHOLD] yolo.py: error: the following arguments are required: -i/--image i am getting above error ,please help ji
Very helpful, great explanation on YOLO. Thank you very much
Hello ma'am
Thanks, sir. Your content helped a lot. Everybody just codes and moves on, but nobody tells how it happens. Thank You
Very lucid explanation and easy to understand. Learned a lot from this video alone, thanks and keep it coming
This is a wonderful explainer, thanks much for doing this 🙏 just getting to know more about YOLO and everything about object detection. Have been in a rabbit hole & watching multiple videos but no other video explains as succinctly as this one.
The best explanation on YOLO so far. Thank you.
Simple, clear and instructible. Perfect to introduce to YOLO. SO GOOD
Amazing video. Thank you for explaining everything in just one video😃
Amazing explanation with enough time thanks for saving my time
Perfect and Crisp Explanation!
simple and clear easy to comprehend
Sir👏, your teaching is just😚
very nicely explained thank you.
I missed something...for training and testing we have images plus bounding boxes in our inputs. But the final model input is image only. How is this handled?
Excellent brother🎉
Thank you so much. You are a legend!.
really good simplification of yolo part1 ..... Thankyou
Thanks much balaji. This will help me in my project preparations!
Thank you 😊
Thnx balaji. Your content is awesome
Thank you
Sir I have a doubt please help me, you told that:
1) Output layer consists of both classification(pc, c1, c2, ...) and bounding box values(bx, by, bh, bw) i.e, its a regression.
2) At 2:45 you told that for ouput layer softmax activation is applied, but how can a softmax activation be applied on bounding box values which is regression.
3) Ok let me assume that as the width and height values of Image and grid will be between 0 and 1 their may be a chance of using softmax, because softmax activation output will be between 0 and 1, but Iam not sure about this. But at 17:05 you told that in some cases in output layer bounding box width and height can be more than 1, but softmax which is applied to output layer can give values between 0 and 1, then how can bounding box width and height get the value more than 1.
4) Softmax when used in output layer it will consider bounding box values also as classes, so how can softmax be used in output layer.
Can you please solve my confusion.
Great Explanation
Thanks balaji. You taught really well. Pls upload more videos. will be more useful
thanks for this explaintion
nice explaination..........really good........
well explained , thank you much
Great explanation!
Nice introduction, thank you
Very well explained👌
@Balaji Srinivasan, Sir you explained exactly like Andrew ng in a detailed manner. Happy to come to know about your channel
beautiful...
Thanks for sharing ❤️
very well explained
Excelent it really benifical for me Thank you for your guidance
I have doubt could you please clear this...Suppose consider 3 X3 Grid (grid1,2,3,4,5,6) and consider a image ie car is spread over 2 grids (5th and 6th grids ) For Grid 5th, Yolo through CNN operation identifies image and its bounding box and vector cordinates are predicted covering two (5th and 6th) grid cells . Now for 6th grid also same operation will be applied . So now after whole grids operation does.5th and 6th grid predictions combined through NMS and IOU to single prediction where image is exactly PRESENT ? Is my understanding correct?
WELL EXPLAINED...
is it for training or identification
Must thank you bro❤️
great work
please i want to know which tensor or vector of the images saved. all I see is the bounding box and classification and probability
Great explanation thank you 😊
#Subscribed
its is an awesome video and u explained everything quite well. plz make a list of videos about opencv and neural network working.
If y output only detect one object at a time then how come we can have multiple object detected in single frame at a time?
1. how anchor boxes are placed(initially).
2. what is the value of ground truth at the time of inferencing
Anchor boxes are defined by us by giving the y value as ground truth while training. During the inference time you don't have the ground truth right.
HI SIR , Excellent Explanation
Thank you 😊
@@BalajiSrinivasan25 Are You From TAMILNADU ...Sir???
Hi Balaji, could you pls upload RCNN and its types. Masked RCNN also?
Sure, will upload them in a few days. Thanks for the suggestion 😊
How program decides that how many Anchor boxes should be present for that particular image ?
multiple anchor boxes are predicted for every object, YOLOv2 uses NMS (non maximal suppression through IoU (Intersection over Union)) and the Pc values to reduce down to a single anchor box for every object
thanks
Bro today yenaku interview coding test iruku ....object detection model built pana solirukanga help pana mudiyum ma ?I have one two day to complete the code
Is it possible to integrate the YOLO algorithm with arduino or raspberry pi using a webcam?
Bro I like this explanation but I have doubts
How bh bw bx by will be calculated
Means who is responsible to calculate
And how bunch of images get bounding boxes for training
Those training data are manually generated by data labellers.
0:27
Bro can you make aa face mask detection and social distancing using yolo
any resources to the newer or better methods to solve the limitations of anchor boxes?
what if my image has 100 instances of different objects to be detected, can someone point a link or mention them
Bro code not working arguments error came
Nanba I'm new subscriber hope you are tamil
Can someone develop project for my business using YOLO.
glad to do for you!
Code run agilla bro ..
are able to share me slide?
A GOD!!
#YOLO
Love u 3000
usage: yolo.py [-h] -i IMAGE [-c CONFIDENCE] [-t THRESHOLD]
yolo.py: error: the following arguments are required: -i/--image
i am getting above error ,please help ji
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