Thank you very much for the best explaination of yolo papers on youtube. I have a question of loss calculation on multiscale training. This affects the number of output grid (WxHXS) used in loss calculation when input image(WxH) size changes. How does the loss calculation maintain consistency for this training scheme?
I will restart making in few days. I stopped making because of personal commitments. Thanks for staying back with the channel, I appreciate your patience🙂
Wonderful videos sir. Great explanation. I've just started learning yolov7 and referring your videos for the base knowledge. I just want to know do i need any specific gpu for experimenting yolov7 or collab gpu will work?
Is there a link for code to develop YOLO from scratch for a custom dataset I know I can use the one in Github but I want to learn about the concept in depth So can U help me with the code part
Watch the full playlist of YOLO Object Detection Family
czcams.com/play/PL1u-h-YIOL0sZJsku-vq7cUGbqDEeDK0a.html
This video ppt is missing in github. But great work.
Yeah, it's huge in MBs, need to upload in lfs or share drive link. Will do
@@MLForNerds yes gdrive link will be good. 👍
The best explanations of YOLO on CZcams. Period. Thank you!🙏
I think even 6th grade student will understand your explanation!!! Awesome awesome!!!
Thank you for the heartfelt response.🙏
Thanks for this tutorial. Your level of understanding and teaching is beyond par - A gift indeed.
After so many searches......The best playlist on CZcams for YOLO Object Detection Family. 👍
Bro ❤ that's so better than 200 usdt lectures
sick lectures so underrated!
best explanation of yolo on the internet
Pls Make video on Yolo v5,v6,v7,v8 and v9. It will be great if you cover YOLO NAS, Yolo World . Thank You for your amazing Videos!!:)
genius bro
your videos are extremely informative. Thank you very much.
Thank you very much for the best explaination of yolo papers on youtube. I have a question of loss calculation on multiscale training. This affects the number of output grid (WxHXS) used in loss calculation when input image(WxH) size changes. How does the loss calculation maintain consistency for this training scheme?
this is awesome !
amazing explaination sir please complete the playlist and make yolo v5,v6,v7 also
good explanation
Thank you
what a excellent explanation. From where you got the in-depth information.
Glad you liked it. I look into source code from github and match it with paper.
@@MLForNerds are you going to make videos? Or you left CZcams?
I will restart making in few days. I stopped making because of personal commitments. Thanks for staying back with the channel, I appreciate your patience🙂
Amazing explanation please explain yolo v7
Thanks Pavan, I will.
Wonderful videos sir. Great explanation.
I've just started learning yolov7 and referring your videos for the base knowledge.
I just want to know do i need any specific gpu for experimenting yolov7 or collab gpu will work?
Collab Gpu should work fine. Usually they’re T4 Gpus of 16GB.
thank you i could have nerver learn and understand any better then your video
If we have only convolution layer then how it helps?? like how grid cell and bounding box number can be increased
Thanks for your explain, but i have a question on the Total loss Function , what the 1MaxIOU
Please make a video on v5 v6 v7 v8 v9 as well
Wonderful tutorials. I couldn't find the slides for Yolo V2 in the repository. Can you please check/upload them?
That ppt is taking more space and I couldn't upload directly via browser. Will upload through git commands.
can you please share github repository link?
Thank you for the greate explanation. Could you please share the slides?
Is there a link for code to develop YOLO from scratch for a custom dataset I know I can use the one in Github but I want to learn about the concept in depth So can U help me with the code part
You meant yolov1?