updates
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4
train.py
4
train.py
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@ -39,8 +39,8 @@ hyp = {'giou': 1.582, # giou loss gain
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'wh': 0.1857, # wh loss gain
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'wh': 0.1857, # wh loss gain
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'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20)
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'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20)
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'cls_pw': 1.446, # cls BCELoss positive_weight
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'cls_pw': 1.446, # cls BCELoss positive_weight
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'obj': 21.35, # obj loss gain
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'obj': 21.35, # obj loss gain (*=80 for uBCE with 80 classes)
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'obj_pw': 3.941, # obj BCELoss positive_weight (*=80 for uBCE with 80 classes)
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'obj_pw': 3.941, # obj BCELoss positive_weight
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'iou_t': 0.2635, # iou training threshold
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'iou_t': 0.2635, # iou training threshold
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'lr0': 0.002324, # initial learning rate
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'lr0': 0.002324, # initial learning rate
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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@ -367,7 +367,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
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t = torch.zeros_like(pi[..., 5:]) # targets
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t = torch.zeros_like(pi[..., 5:]) # targets
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if nb:
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if nb:
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t[b, a, gj, gi, tcls[i]] = 1.0
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t[b, a, gj, gi, tcls[i]] = 1.0
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lcls += BCEcls(pi[..., 5:], t)
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lcls += BCEobj(pi[..., 5:], t)
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lbox *= k * h['giou']
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lbox *= k * h['giou']
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lobj *= k * h['obj']
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lobj *= k * h['obj']
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