Your lecture is one of the best i have seen so far....very informative,clear,to the point and interesting as well. Really awesome keep doing the good work...
Just like sir said the higher level feature map or smalle feature map are mainly for bigger object and lower level feature map or bigger feature map are for smaller object,due to this various scale are neded for differenet feature map , can anyone tell me in simple words what is 'scale' here
hii i have a doubt is there a way i can contact you i have few doubts regarding neural networks that i hope you can solve as i saw your videos and they were pretty awesome
Thankyou for your explanation. I’m currently using yolo v4 for my thesis reserach and do you have some recommendation how many batch and subdivision should i use for training? becuase i keep getting low mAP after training using yolo v4 which is 79%, and i have 450 data for training, is there any chance that i can go higher than that? and how? thank you
Your lecture is one of the best i have seen so far....very informative,clear,to the point and interesting as well. Really awesome keep doing the good work...
Sir, im loving the series, please continue the great work
Thanks, will do!
Really Enjoyed learning these Yolo Version -- Still waiting for remaining versions
Will continue in sometime.
Sir kindly continue the series till YOLOV8. Your series is amazing and we are awaiting for you to complete the series
Amazing Session!
Glad you enjoyed it
Thank you for providing this video. kindly provide such type of video on YOLOv5 , YOLOv6 and YOLOv7 also.
Will keep posting on those one by one
@@MLForNerds looking forwards to that, when will YOLOV5 be released
Sir you deserve more subscribers
One of the best I have come across so far, Could you please continue the series.
what a great material - so well structured. thx for your efforts - beautiful!!!
really great job sir! waiting for more lately versions
Thanks for the great explanation
thanks for giving us perfect video, love you bro
Really informative and precised content.
Please continue the series ASAP sir
sir please make the content for remaining all versions and object detection models, videos are very captivating sir
Just like sir said the higher level feature map or smalle feature map are mainly for bigger object and lower level feature map or bigger feature map are for smaller object,due to this various scale are neded for differenet feature map , can anyone tell me in simple words what is 'scale' here
Does sam-mish activation function have a formula ? or it's same with mish
Hi, could you please post a video about polygon annotation for objects, and which YOLO version supports that, and how to implement that?
Thanks a ton for the nice videos, will be any following videos in the series?
Yes, I will be releasing videos on all YOLO versions.
@@MLForNerds 🙏❤
Great video. Thanks ! Can you provide a link to part 3 ?
I'm working on it. Will be posting next week mostly.
Eagerly waiting :)
@@MLForNerds
Hello sir, can you make videos on latest YOLO version such as V8 and V7, thank you!!
Sure, will try to upload.
could you please share with us the pdf or ppt version of this presentation
hii i have a doubt is there a way i can contact you i have few doubts regarding neural networks that i hope you can solve as i saw your videos and they were pretty awesome
Yes, sure. I have started discord server recently. I’m sharing the link and you can join there. discord.gg/nfZCszw7Rw
@@MLForNerds okie
Thankyou for your explanation. I’m currently using yolo v4 for my thesis reserach and do you have some recommendation how many batch and subdivision should i use for training? becuase i keep getting low mAP after training using yolo v4 which is 79%, and i have 450 data for training, is there any chance that i can go higher than that? and how? thank you
you actually need more data bc it says you need at least 1500-2000 objects per class
hi great work! can you share slides
Please upload yolov7
bro YOLOv5 please
yes,bro
please upload yolov5
please upload yolov5
I will be posting in the coming month.