Comments (12)
Hello @xy-guo . Thanks for your amazing work. I have re-trained the network using the same cofiguration, but the training loss don't converge.
I don't think the training loss is very plausible. Do you have this problem??
Regards.
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hi, did you continue training? What does the final loss curve look like? Also does the depth in the image tab seem improving? My training loss also fluctuates a lot.
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Hello @xy-guo . Thanks for your amazing work. I have re-trained the network using the same cofiguration, but the training loss don't converge.
I don't think the training loss is very plausible. Do you have this problem??
Regards.
Have you ever try to use larger batchsize?
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I use 8 gpus to train the model. It seems your training loss is converging, try using larger smoothing weight in tensorboard.
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Do you have the final metrics of abs_depth_error
, thres2mm_error
, thres4mm_error
, thres8mm_error
? I want to compare with my results, thank you.
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Sorry I have lost the final metrics information. @kwea123
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@kwea123 Have you try bigger batchsize? What's the difference with the training loss of batchsize=1?
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The model consumes too much memory so I cannot try bigger batchsize (batchsize=1 requires ~8GB). You will need multiple gpus.
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@QTODD Hi, have you solve the problem that the training loss is not plausible?
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@xy-guo Do you use 8 GPUS to test?
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I tried, even 8GPUs with batchsize 4 results still in fluctuating losses. I think it's insolvable then; although it doesn't worsen the model performance..
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