Introduction into YOLO v3

Content of the brief introduction lecture into YOLO version 3:
Architecture of YOLO v3;
Detections at 3 Scales;
Detection Kernels;
Grid Cells;
Anchor Boxes;
Predicted Bounding Boxes;
Objectness Score.

How to train custom object detector with own data and YOLO v3 algorithm.

[bakervhaigaming]

I regret why I haven't found this gem earlier! I had to go through 5-6 papers and hours of reading to understand these topics but your video made it very clear and specific. Please make more quality content like this. Thanks a lot.

[abdshomad]

~ Timeline for watching again later ~
00:01 Intro
01:17 What is YOLO?
03:13 Architecture of YOLO v3
05:28 Input
07:27 Detections at 3 Scales
09:28 Detection Kernels
12:02 Grid Cells
14:23 Anchor Boxes
18:25 Predicted Bounding Boxes
21:41 Objectness Score
Conclusion

[azmyin]

This is one of the simplest and most articulated explanation of YOLOv3. Thank you very much for this video and please keep up the good work.

[mastuart]

I was code in YoloV3 from Indian Youtuber, and now here I am learning the true nature of Yolo. It helps alot for this OCR Project where I can ignore the image that did not intended to be uploaded to Server.

[sumitbali]

I have seen lot of videos on CNN, mostly crap. But your video is a gem. Appreciate the effort you have put into making this video. Diagrams are a great help in understanding the architecture. Thanks again

[adithyanarayan]

Spent multiple hours trying to read through various papers in order to understand some of the topics. Should've stumbled upon your channel and the video much earlier. Love the fact that everything is explained to the point. You've earned yourself a subscriber in me. Can't stress this enough, but please put out more videos like these, along the lines of Computer Vision. Well done mate and once again, THANK YOU SO MUCH!

[dp.]

Great video!
We need more detailed explanation-videos like this, any other video i've watched are same few lines of explanation of YOLO where can be found all over the internet.

[iProFIFA]

Legitmely the clearest video I could find on this topic, amazing! Thanks a lot and keep up the great work Valentyn! :-)

[hima-]

This video really contains the details of yolov3! It helps me a lot! Thx!

[krishhhhh]

This is one of the best I have seen . Thank you

[mtmotoki]

It is explained with a lot of diagrams, so even though I am not very good at English, I was able to understand it. Thank you

[naufalramadhani]

thank you for thorough explanation sir, much appreciated it, keep it this way it is great.. cheers sir

[hoangvancuong]

thank for detail and easy to understand video. I love it.

[Can-uede]

Amazing Explanation of Yolo v3. Thank you very much.

[shannondoyle]

Really great detailed explanation. I don't get exactly what the ground truth values are determined for grid cells close to the centre grid cell of an object. Would you be able to explain this ?

[Alpha-hjss]

Great Video! Can you please come with more videos

[syafiqbasri]

thank you so much sir.Its very useful and great explanation!

[kristopherhuber]

I enjoyed your video. Thank you for putting in the effort. Could you comment on the receptive field of YoloV3? For example if I put in a shape=(416, 416, 3) image; then as you said, YoloV3 decimates by 32, to produce an output feature map at layer 82 of shape=(13, 13, 255). This shown quite clearly in your video (15:50 mark). My question is what is the receptive field for that first cell in the output feature map? (ie. the top left cell - of shape=(1, 1, 255) )? To ask another way, what portion of the original 416, 416, 3 image is mapped to the 1, 1, 255 feature cell?

[pascalschluchter]

Hey, can someone explain to me, why the detection is happening in Layer 82, 94 and 106. Is there any mathmatical background or is it like a fix parameter of YOLOv3?

[glowwell]

Thanks a lot. Explained neatly.
Please make videos on V4 and V5 too.