Darknet: Any plan to support pp-yolo?

[toplinuxsir]

Any plan to support pp-yolo? Thanks!
https://arxiv.org/abs/2007.12099

[AlexeyAB]

• SPP +0.3% AP (marked as ”star”)

• Coord Conv +0.5% AP (marked as "diamonds" for each 1x1-conv) https://arxiv.org/abs/1807.03247

• IoU Aware +0.6% AP (predicted_IoU === bbox_confidence, a bit like Gaussian_yolo) https://arxiv.org/abs/1912.05992

• Matrix NMS +0.7% AP https://arxiv.org/abs/2003.10152

• DropBlock (marked as "triangles" in FPN) + EMA (Wema = λWema + (1 − λ)W, λ = 0.9998) + LB (mini-batch=196) = +2.3% AP

  • EMA: https://arxiv.org/abs/1911.09070

• DCN : https://arxiv.org/abs/1703.06211

  • we replace the 3×3 convolutional layer in the last stage of ResNet with deformable convolution layer

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• DropBlock – should be used only FPN (1 DropBlock per 1 FPN-branch)
• DCN – should be used only for the conv-3x3 in the last stage of Backbone
• Coord Conv – should be used only for conv-1x1 in FPN / head
• We can add another one probability = IoU_prob. So prob = objectness * IoU_prob * Class_prob
• Matrix NMS

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• https://arxiv.org/abs/2007.12099

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[FilipLangr]

@AlexeyAB I believe there's a minor mistake in:

• Coord Conv – should be used only for conv-1x1 in backbone

PP-YOLO paper says (at page 4):
"In order to reduce the loss of efficiency as much as possible, we do not change convolutional layers in backbone, and only replace the 1x1 convolution layer in FPN and the first convolution layer in detection head with CoordConv."

[AlexeyAB]

Fixed it.

[toplinuxsir]

@AlexeyAB Great work, Where to find the pp yolo config file to train custom detection? Thanks !

[scamianbas]

@AlexeyAB Great work, Where to find the pp yolo config file to train custom detection? Thanks !

Hi @AlexeyAB and congrats for the great work. Is this still in the todo list ? Thanks !

[Goru1890]

Great work! @scamianbas Yes, it's seems to be still in progress https://github.com/AlexeyAB/darknet/projects/1#card-42538662.