BCE to CE lconf + batch size 16
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@ -101,7 +101,6 @@ class YOLOLayer(nn.Module):
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self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
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self.weights = class_weights()
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def forward(self, p, targets=None, requestPrecision=False):
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FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
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@ -173,8 +172,8 @@ class YOLOLayer(nn.Module):
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# lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
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lconf = k * BCEWithLogitsLoss(pred_conf, mask.float())
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# lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
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lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
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lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
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# lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
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else:
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lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
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@ -26,7 +26,6 @@ cd yolov3/checkpoints
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wget https://storage.googleapis.com/ultralytics/yolov3.pt
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cp yolov3.pt latest.pt
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cd ..
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python3 train.py -img_size 416 -batch_size 12 -epochs 1 -resume 1
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python3 train.py -img_size 416 -batch_size 16 -epochs 1 -resume 1
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python3 test.py -img_size 416 -weights_path checkpoints/latest.pt -conf_thres 0.5
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