WongKinYiu / yolor

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
GNU General Public License v3.0
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How about yolov4-p6-light in 640 size? #5

Open Zigars opened 3 years ago

Zigars commented 3 years ago

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 commented 3 years ago

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 commented 3 years ago

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 commented 3 years ago

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 commented 3 years ago

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 commented 3 years ago

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

Wanghe1997 commented 3 years ago

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?