Currently we plan to release yolov9-s and m models after the paper is accepted and published. If our plan changes, we will directly release the models on the repo.

from yolov9.

这是什么操作？发布了成绩不公开模型权值，连模型配置、结构都不公开。非要说接收了论文才公开权值。是害怕被偷师改完抢发yolo10吗？要不要看看开源社区对yolov9的支持是怎么样的？是0，我没有看到任何第三方框架宣布对yolov9的支持。正因为作者迟迟不公开细节，人家都不知道怎么复现。
作者既不急着认自己是正统，又害怕别人抢了作者的正统，就挺怪的。

from yolov9.

need to test the yolov9-s model, when to release them?

from yolov9.

release yolov9s and yolov9n model!!!

from yolov9.

need yolov9-s和yolov9-n model? when release them?

from yolov9.

still not release t and s model!! when to release them?

from yolov9.

Can you give us a time when s model release?

from yolov9.

Hello Guys, are these released all? Currently i have checked in "https://github.com/WongKinYiu/yolov9/releases/", it is still not released?

from yolov9.

close yolov9, yolov7-plus will be nice.
Your Team is going ahead just as Close-AI.

from yolov9.

yolov9-s and yolov9-m are released, you could try them.

from yolov9.

Sorry, but when could we know whether your models could be released?

from yolov9.

Is there any way to contribute in YOLOv9? For model releasing or any other thing required

go to use the yolov10, v10 is better.

from yolov9.

Is there any way to contribute in YOLOv9? For model releasing or any other thing required

from yolov9.

from yolov9.

yolov9-s and yolov9-m are released, you could try them.

Thanks!
Why is there no auxillary branch in YOLOv9-s model? I couldnt find in the config file? Would be really grateful if you could explain.

from yolov9.

yolov9-m and yolov9-s have auxiliary branch.
Provided weights files are reparameterized, which auxiliary branch have removed.

from yolov9.

I understand that the weight files are re-parametrized. However, I do not get why I do not see reversible aux branch in the config file like in the YOLOv9-C config file(which has the comment showing multi-level aux branch part). Can you please explain if i am missing something basic? I would be grateful if you could pinpoint the branches.

YOLOv9

parameters

nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()

anchors

anchors: 3

gelan backbone

backbone:
[

conv down

[-1, 1, Conv, [32, 3, 2]], # 0-P1/2

conv down

[-1, 1, Conv, [64, 3, 2]], # 1-P2/4

elan-1 block

[-1, 1, ELAN1, [64, 64, 32]], # 2

avg-conv down

[-1, 1, AConv, [128]], # 3-P3/8

elan-2 block

[-1, 1, RepNCSPELAN4, [128, 128, 64, 3]], # 4

avg-conv down

[-1, 1, AConv, [192]], # 5-P4/16

elan-2 block

[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 6

avg-conv down

[-1, 1, AConv, [256]], # 7-P5/32

elan-2 block

[-1, 1, RepNCSPELAN4, [256, 256, 128, 3]], # 8
]

elan head

head:
[

elan-spp block

[-1, 1, SPPELAN, [256, 128]], # 9

up-concat merge

[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4

elan-2 block

[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 12

up-concat merge

[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3

elan-2 block

[-1, 1, RepNCSPELAN4, [128, 128, 64, 3]], # 15

avg-conv-down merge

[-1, 1, AConv, [96]],
[[-1, 12], 1, Concat, [1]], # cat head P4

elan-2 block

[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 18 (P4/16-medium)

avg-conv-down merge

[-1, 1, AConv, [128]],
[[-1, 9], 1, Concat, [1]], # cat head P5

elan-2 block

[-1, 1, RepNCSPELAN4, [256, 256, 128, 3]], # 21 (P5/32-large)

elan-spp block

[8, 1, SPPELAN, [256, 128]], # 22

up-concat merge

[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4

elan-2 block

[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 25

up-concat merge

[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3

elan-2 block

[-1, 1, RepNCSPELAN4, [128, 128, 64, 3]], # 28

detect

[[28, 25, 22, 15, 18, 21], 1, DualDDetect, [nc]], # Detect(P3, P4, P5)
]

from yolov9.

yolov9-m use multi-level reversible aux branch.
yolov9-s use multi-level aux branch.

from yolov9.