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How about yolov4-p6-light in 640 size? #5

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Zigars opened this issue May 18, 2021 · 6 comments
Open

How about yolov4-p6-light in 640 size? #5

Zigars opened this issue May 18, 2021 · 6 comments

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@Zigars
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Zigars commented May 18, 2021

I have test trained yolor-p6.yaml In VisDrone dataset (a famous UAV dataset) use 640 size and your pretrained .pt. And I get a excellent results, It's a great work!
Then, I trained yolov4-p6-light.yaml(remove the reOrg and IDetect module and I fix it in yolov5's rep) in VisDrone dataset use 640 size without pretrained, but the results under the original yolov4-csp‘s result. maybe it have some bug in my code.
So I'm training the same yolov4-p6-light.yaml(remove the reOrg and IDetect module and I fix it in your yolor's rep) in VisDrone use 640 size without pretrained, and I need find the bug in my own code if your yolor rep can get a good results. If not, that says yolov4-p6-light need a coco pretrained? because the model have four output, the loss can hard to convergence?
The all experiment trained in 300epoch, 32batchsize, 640*640 size, use single V100 to train the model. maybe you can solve my question. thks!

@Zigars
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Zigars commented May 19, 2021

my test is over, and these are my test results, I don't know which part have different, my VisDrone-yolov4 Rep is forked by yolov5's latest version, maybe the calculate of mAP have different?
Train
Test

@WongKinYiu
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Calculation of Precision and Recall are different, yolov5 calculate average score of 0.5:0.95 and yolor calculate score of 0.5.
And I think AP(0.5) and AP(0.5:0.95) are using same calculation.

@Zigars
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Zigars commented May 19, 2021

If that your explain is true, I think yolov4-p6-light.yaml (without reOrg and IDetect module) can not catch the yolov4-csp.yaml results in VisDrone dataset, although yolov4-p6-light.yaml have four output, the same infer time as yolov4-csp.yaml. I'm confuse about it. thank you for your explain!
these are my yolov4-p6-light.yaml and yolov4-csp.yaml cfg files.yolov4-csp is a great work, I'm trying to make it more applicable for the VisDrone dataset.
VisDrone-yolov4-cfg.zip

@WongKinYiu
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In my experiments.
input resolution: performance
1280: p6 > p7 > p5
1536: p7 > p6 > p5

maybe for 640 case, p5 models will get better performance than p6 models, but i have not tested it yet.

@Zigars
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Zigars commented May 19, 2021

thank you so much for your reply, I will test it in my code soon.

@Wanghe1997
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Zigars,我可以加你的QQ或者微信吗?我是一个学生,我想用自己的数据集跑作者的YOLOR,但是没跑通,可以请教下您吗?

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