I think the output of SPPELAN has the shape of (B, 256, W, H). [-1, 1, SPPELAN, [512, 256]], the first channel is output channel, the second one is the hidden channel of its intermediate layers
I have one question regarding the detect blocks in the head. Could you please explain why the detect blocks focus on detecting only one size? Of all my object annotations are roughly the same size, can I choose to have just one detect block?
That is the way how YOLO detects object in different scales. If you have object with similar size, prune architecture part that are irrelevant to your case. How to prune? Well, we have a course on how to prune yolo architecture. However, it is specifically for YOLOv8. But the principle is similar btw
I am not very sure for YOLOv9. But cmiiw, some YOLOv9 codes are similar with YOLOv8 codes and someone has tried to implement the attention model. This thread might interest you: github.com/ultralytics/ultralytics/issues/2958
@@businessjournal339 I have check the source code and the paper, i'm not sure there is an attention module in YOLOv9. In fact, there is someone who wants to add attention module to YOLOv9 github.com/WongKinYiu/yolov9/issues/454
Learn YOLOv9 YOLOv8 YOLOv7 and even the latest one (YOLOv10) in one course: 👉 bit.ly/YOLO_MEGA_Course
I think the output of SPPELAN has the shape of (B, 256, W, H). [-1, 1, SPPELAN, [512, 256]], the first channel is output channel, the second one is the hidden channel of its intermediate layers
Can you provide architecture diagram of the YOLOv9 ?
You can downlooad it from here: stunningvisionai.com/article/yolov9-architecture
thank you for your explained ❤❤❤❤
where can i modify the attention layer for tiny object detection?
Hi sir, thank you for the video. Can you please explain the loss functions used in the yolo-seg model and how they are combined?
Halo Pak Doktor. Mohon masukanya. Untuk mendeteksi multi class objek apakah Yolov8 atau Yolov9 ini bisa lebih baik dibandngkan Yolov4 atau Yolov5?
Berdasarkan papernya, seperti itu pak.
Hasil eksperimen kami juga seperti itu
Thank sir, keep going
Thank you too
very good and detailed explanation. Thank you very much. Can I use the architecture of YOLOv9 in my write-up?
Yes you can. Please cite this site stunningvisionai.com/article/yolov9-architecture
Could you please show the YOLOv9-seg architecture also? Thank you so much ~~~
That would be making another video :D
But thank you for asking
I really appreciate your explanation. However, What about the Multi-Level Auxiliary Information branch as mentioned in the paper?
That would be another level of explanation. We need another video for it
I have one question regarding the detect blocks in the head.
Could you please explain why the detect blocks focus on detecting only one size?
Of all my object annotations are roughly the same size, can I choose to have just one detect block?
That is the way how YOLO detects object in different scales. If you have object with similar size, prune architecture part that are irrelevant to your case.
How to prune?
Well, we have a course on how to prune yolo architecture. However, it is specifically for YOLOv8. But the principle is similar btw
@@Dr.Priyanto.Hidayatullah Thank you.
@@VibesOfEastCoast no problem
can we apply attention layer to their arch. can you help me to explain where should it incorp.
I am not very sure for YOLOv9. But cmiiw, some YOLOv9 codes are similar with YOLOv8 codes and someone has tried to implement the attention model.
This thread might interest you: github.com/ultralytics/ultralytics/issues/2958
Dear Sir,
Which activation function is used in YOLOv9?
Which attention module is used in YOLOv9?
SiLU activation function
SiLU activation function
@@Dr.Priyanto.Hidayatullah Thank you sir. What about attention module?
@@businessjournal339 i have to check the code.
@@businessjournal339 I have check the source code and the paper, i'm not sure there is an attention module in YOLOv9. In fact, there is someone who wants to add attention module to YOLOv9 github.com/WongKinYiu/yolov9/issues/454
sir, can you make a video explain about all the loss funtion in yolov9, we are highly expecting that, thanks you.
Yes, you can make that video 😀
@@Dr.Priyanto.Hidayatullah oh im sorry i wrong, i mean would u make this for us
@@ucdung6092 hihihi. Thank you for the request, btw
cbfuse?
you can find out when explaining auxiliary
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Segera saja, hanya berlaku 4 harian lagi
@@Dr.Priyanto.Hidayatullah makasih bapak, otw check out
@@Dr.Priyanto.Hidayatullahmakasih bapak, otw checkout