xy and wh losses respectively merged
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@ -131,6 +131,7 @@ class YOLOLayer(nn.Module):
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if p.is_cuda:
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if p.is_cuda:
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self.grid_xy = self.grid_xy.cuda()
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self.grid_xy = self.grid_xy.cuda()
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self.anchor_vec = self.anchor_vec.cuda()
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self.anchor_vec = self.anchor_vec.cuda()
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self.anchor_wh = self.anchor_wh.cuda()
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# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh)
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# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh)
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p = p.view(bs, self.nA, self.nC + 5, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
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p = p.view(bs, self.nA, self.nC + 5, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
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@ -1,7 +1,7 @@
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#!/usr/bin/env bash
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#!/usr/bin/env bash
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# Start
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# Start
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 train.py --freeze
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 train.py
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# Resume
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# Resume
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python3 train.py --resume
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python3 train.py --resume
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