- 👋 Hi, I’m @BADBADBADBOY
badbadbadboy / pgnet.pytorch Goto Github PK
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thanks for your works ,could you provide a pre_trained model?
could you provide a pre_trained model? pgnet_model.pth
Originally posted by @Devin521314 in #1 (comment)
用百度开源的印章数据集(8千张训练集),训练到50多个epoch,loss还是没降下去, 训练日志如下:
53/600/300/333 | loss:103.55 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.01 | ctc_loss:20.70 | | Lr:0.00100000
53/600/305/333 | loss:96.91 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:19.37 | | Lr:0.00100000
53/600/310/333 | loss:95.08 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:19.01 | | Lr:0.00100000
53/600/315/333 | loss:165.42 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:33.08 | | Lr:0.00100000
53/600/320/333 | loss:162.01 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.01 | ctc_loss:32.39 | | Lr:0.00100000
53/600/325/333 | loss:162.18 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.43 | | Lr:0.00100000
53/600/330/333 | loss:161.29 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.25 | | Lr:0.00100000
epoch_loss:148.26 | epoch_score_loss:0.03 | epoch_border_loss:0.01 | epoch_dire_loss:0.00 | epoch_ctc_loss:29.64
54/600/0/333 | loss:94.88 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:18.97 | | Lr:0.00100000
54/600/5/333 | loss:96.44 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.01 | ctc_loss:19.28 | | Lr:0.00100000
54/600/10/333 | loss:93.79 | score_loss:0.02 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:18.75 | | Lr:0.00100000
54/600/15/333 | loss:161.79 | score_loss:0.02 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.35 | | Lr:0.00100000
54/600/20/333 | loss:162.57 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.51 | | Lr:0.00100000
54/600/25/333 | loss:165.98 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:33.19 | | Lr:0.00100000
54/600/30/333 | loss:164.87 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.96 | | Lr:0.00100000
54/600/35/333 | loss:161.13 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.22 | | Lr:0.00100000
54/600/40/333 | loss:159.28 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:31.85 | | Lr:0.00100000
54/600/45/333 | loss:164.96 | score_loss:0.03 | border_loss:0.01 | direction_loss:0.00 | ctc_loss:32.98 | | Lr:0.00100000
ctc loss tensor(inf, device='cuda:0')
0/600/0/2000 | loss:inf | score_loss:0.82 | border_loss:0.56 | direction_loss:0.42 | ctc_loss:inf | | Lr:0.00100000
/home/wzp/project/torch/lib/python3.6/site-packages/torch/nn/functional.py:1569: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
ctc loss tensor(nan, device='cuda:0')
ctc loss tensor(nan, device='cuda:0')
ctc loss tensor(nan, device='cuda:0')
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