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I am trying to train on my own dataset as with the case with issue #215. I opted to load the weights from the crowdAI dataset trained model and then continue training on my own images from there.
Using issue #160 as reference, I loaded the weights from best.torch.
(btw, is it correct to use self.load('.../experiments/mapping_challenge_baseline/checkpoints/unet/best.torch')?)
I also set self._initializar _model_weights = None'.
However it threw out an error: ‘module’ object has no attribute ‘_rebuild_tensor_v2’
Which I was able to fix via this thread.
Another error occurred:
And I fixed it via this thread.
Now, running python main.py train --pipeline_name unet_weighted does not throw any more errors, but training seems to not start at all (no prints of epoch 0).
Here is the full printout of the console:
I'm also trying to train my images on either of the contest weights (unet or scoring_model) but I'm not sure how to load those weights such that the training continues on them.
@zeciro running that command with either unet or unet_weighted gives basically the same console output as the OP shows (no errors, but no output seems to be produced).
Good day,
I am trying to train on my own dataset as with the case with issue #215. I opted to load the weights from the crowdAI dataset trained model and then continue training on my own images from there.
Using issue #160 as reference, I loaded the weights from best.torch.
(btw, is it correct to use
self.load('.../experiments/mapping_challenge_baseline/checkpoints/unet/best.torch')
?)I also set
self._initializar _model_weights = None'
.However it threw out an error:
‘module’ object has no attribute ‘_rebuild_tensor_v2’
Which I was able to fix via this thread.
Another error occurred:
And I fixed it via this thread.
Now, running
python main.py train --pipeline_name unet_weighted
does not throw any more errors, but training seems to not start at all (no prints of epoch 0).Here is the full printout of the console:
No errors are reported but the training does not seem to start. Do you have any ideas for why this is the case? Thank you.
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