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some comparison #32
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@amusi Hello, I saw your article, here I provide some comparison of Pytorch version YOLOv3, YOLOv4, and YOLOv5. (All experiments are run on a same Tesla V100 GPU) Pytorch versionTrain with YOLOv3 setting (416x416)trained on coco 2014 trainvalno5k set and tested on coco 2014 5k set. YOLOv3-SPP: yolov3-spp 43.1% AP @ 608x608
Model Summary: 152 layers, 6.29719e+07 parameters, 6.29719e+07 gradients
Speed: 6.8/1.6/8.3 ms inference/NMS/total per 608x608 image at batch-size 16 Train with YOLOv4 setting (512x512)trained on coco 2014 trainvalno5k set and tested on coco 2014 5k set. YOLOv3-SPP: yolov3-spp 43.6% AP @ 608x608
Model Summary: 152 layers, 6.29719e+07 parameters, 6.29719e+07 gradients
Speed: 6.8/1.6/8.3 ms inference/NMS/total per 608x608 image at batch-size 16 CSPDarknet53s-YOSPP: (~YOLOv4(Leaky) backbone + YOLOv3 head) cd53s-yospp 43.7% AP @ 608x608
Model Summary: 184 layers, 4.89836e+07 parameters, 4.89836e+07 gradients
Speed: 6.3/1.6/7.8 ms inference/NMS/total per 608x608 image at batch-size 16 CSPDarknet53s-YOSPP-Mish: (~YOLOv4 backbone + YOLOv3 head) cd53s-yospp-mish 44.3% AP @ 608x608
Model Summary: 184 layers, 4.89836e+07 parameters, 4.89836e+07 gradients
Speed: 7.9/1.6/9.6 ms inference/NMS/total per 608x608 image at batch-size 16 CSPDarknet53s-PASPP: (~YOLOv4(Leaky)) cd53s-paspp 44.5% AP @ 608x608
Model Summary: 212 layers, 6.43092e+07 parameters, 6.43092e+07 gradients
Speed: 6.9/1.6/8.5 ms inference/NMS/total per 608x608 image at batch-size 16 CSPDarknet53s-PASPP-Mish: (~YOLOv4) cd53s-paspp-mish 45.0% AP @ 608x608
Model Summary: 212 layers, 6.43092e+07 parameters, 6.43092e+07 gradients
Speed: 8.7/1.6/10.3 ms inference/NMS/total per 608x608 image at batch-size 16 CSPDarknet53s-PACSP: cd53s-paspp-cspt 45.1% AP @ 608x608
Model Summary: 222 layers, 5.84596e+07 parameters, 5.84596e+07 gradients
Speed: 6.6/1.5/8.1 ms inference/NMS/total per 608x608 image at batch-size 16 Train with YOLOv5 setting (640x640)trained on coco 2017 train set and tested on coco 2017 5k set. YOLOv3-SPP: yolov3-spp 45.5% AP @ 736x736
Model Summary: 225 layers, 6.29987e+07 parameters, 6.29987e+07 gradients
Speed: 10.4/2.1/12.6 ms inference/NMS/total per 736x736 image at batch-size 16 YOLOv5s: yolov5s 33.1% AP @ 736x736
Model Summary: 99 layers, 6.99302e+06 parameters, 6.99302e+06 gradients
Speed: 2.2/2.1/4.4 ms inference/NMS/total per 736x736 image at batch-size 16 YOLOv5m: yolov5m 41.5% AP @ 736x736
Model Summary: 165 layers, 2.51928e+07 parameters, 2.51928e+07 gradients
Speed: 5.4/1.8/7.2 ms inference/NMS/total per 736x736 image at batch-size 16 YOLOv5l: yolov5l 44.2% AP @ 736x736
Model Summary: 231 layers, 6.17556e+07 parameters, 6.17556e+07 gradients
Speed: 11.3/2.2/13.5 ms inference/NMS/total per 736x736 image at batch-size 16 YOLOv5x: yolov5x 47.1% AP @ 736x736
Model Summary: 297 layers, 1.23102e+08 parameters, 1.23102e+08 gradients
Speed: 20.3/2.2/22.5 ms inference/NMS/total per 736x736 image at batch-size 16 |
@WongKinYiu Hi, It obviously
While our new YOLOv4 model is even much better:
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No, there is no any inference time augmentation.
I just follow Ultralytics testing protocol with batch size 16.
It is not amusi's repo, it is Ultralytics's new repo.
There are some modifications in Ultralytics's new repo. I am training |
Yes:
May be we should use |
OK, will train this setting on |
@AlexeyAB Hello, Yes, the AP is benefit by 640x640 training. YOLOv3-SPP: yolov3-spp: 45.5% AP @736x736
Model Summary: 225 layers, 6.29987e+07 parameters, 6.29987e+07 gradients
Speed: 10.4/2.1/12.6 ms inference/NMS/total per 736x736 image at batch-size 16 CSPDarknet53s-YOSPP: (~YOLOv4(Leaky) backbone + YOLOv3 head) cd53s-yospp: 45.6% AP @736x736
Model Summary: 225 layers, 4.90092e+07 parameters, 4.90092e+07 gradients
Speed: 9.1/2.0/11.1 ms inference/NMS/total per 736x736 image at batch-size 16 YOLOv5l: yolov5l 44.2% AP @ 736x736
Model Summary: 231 layers, 6.17556e+07 parameters, 6.17556e+07 gradients
Speed: 11.3/2.2/13.5 ms inference/NMS/total per 736x736 image at batch-size 16 |
@WongKinYiu Nice.
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I am not sure for Darknet due to I do not train it on ImageNet, but yes for Ultralytics.
To acheive this goal I have to take a look how to construct P6 model using new Ultralytics repository. Then I need construct the YOLOv4 model, it does not support all of blocks of YOLOv4 currently. |
@WongKinYiu Hi, Can you share cfg/weights files for this model?
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Hi WongKinYiu, what does -PACSP mean ? And I can't find config and weight file of it, thanks a lot ! |
Hello, PACSP means apply CSP to PANet, the model is still in training process, will release |
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