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Hope can also provide yolov9-s and m model,thanks #3
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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. |
need to test the yolov9-s model, when to release them? |
Can you give us a time when s model release? |
need yolov9-s和yolov9-n model? when release them? |
release yolov9s and yolov9n model!!! |
Sorry, but when could we know whether your models could be released? |
Hello Guys, are these released all? Currently i have checked in "https://github.com/WongKinYiu/yolov9/releases/", it is still not released? |
still not release t and s model!! when to release them? |
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。 |
close yolov9, yolov7-plus will be nice. |
Is there any way to contribute in YOLOv9? For model releasing or any other thing required |
go to use the yolov10, v10 is better. |
yolov9-s and yolov9-m are released, you could try them. |
Thank you very much!
Let us try
…On Wed, Jun 5, 2024 at 6:59 PM Kin-Yiu, Wong ***@***.***> wrote:
yolov9-s and yolov9-m are released, you could try them.
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Thanks! |
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. YOLOv9parametersnc: 80 # number of classes anchorsanchors: 3 gelan backbonebackbone: 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 headhead: elan-spp block[-1, 1, SPPELAN, [256, 128]], # 9 up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']], elan-2 block[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 12 up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']], elan-2 block[-1, 1, RepNCSPELAN4, [128, 128, 64, 3]], # 15 avg-conv-down merge[-1, 1, AConv, [96]], elan-2 block[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 18 (P4/16-medium) avg-conv-down merge[-1, 1, AConv, [128]], 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']], elan-2 block[-1, 1, RepNCSPELAN4, [192, 192, 96, 3]], # 25 up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']], 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) |
yolov9-m use multi-level reversible aux branch. |
Do you plan to release a smaller version of YOLOv9 for segmentation tasks? Smaller than yolov9c-seg. |
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