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Added two new classes(rows and columns) to data, but model is able to train only for column, not learning rows. #175
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How did u make it run? Which version of pytorch, cuda are you using? Which version of python are you using? |
are you using windows or linux? |
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I know this is really unorthodox but is it possible that we can do an online meeting? I have been stuck on this problem for the past 2 months now and I am unable to solve it. I would be grateful if you can show me the changes. |
ImportError: cannot import name 'deform_conv_cuda' from 'mmdet.ops.dcn' I get this error |
Hi Folks, greetings of the day to y'all. |
Can you please share the Colab link? |
Once we get it working, I will post the soultion here. We are still stuck. |
The original base model is trained for bordered, borderless and cells. If I add my custom invoice data with labelled data for these three classes it works very well, I am able to extend the model for my data. However if I add two more classes(rows and columns) to data model is able to train only for column, it is not learning rows.
borderless, row, col and bordered ----> If I train for these classes, it is able to learn column(upto 98% confidence) and tables(upto 98% confidence) well, but it fails for rows. The score for row does not go more than 20%. Here is an example for row prediction:
{'l': 0, 't': 3508, 'r': 83, 'b': 3508, 'confidenceScore': '19.895745813846588', 'class': 'row'}.
For tables and columns :
{'l': 1616, 't': 1333, 'r': 1936, 'b': 1571, 'confidenceScore': '99.99583959579468', 'class': 'column'}
{'l': 248, 't': 1337, 'r': 664, 'b': 1544, 'confidenceScore': '99.9913215637207', 'class': 'column'}
{'l': 2047, 't': 1331, 'r': 2414, 'b': 1567, 'confidenceScore': '99.99083280563354', 'class': 'column'}
{'l': 129, 't': 1343, 'r': 260, 'b': 1530, 'confidenceScore': '99.45705533027649', 'class': 'column'}
{'l': 0, 't': 3508, 'r': 83, 'b': 3508, 'confidenceScore': '19.89872008562088', 'class': 'column'}
{'l': 95, 't': 1342, 'r': 2471, 'b': 1537, 'confidenceScore': '99.66540336608887', 'class': 'borderless'}
I am smudging the image and training, using mmdetection v2. Has anyone else faced similar problem. If more info/data is needed to help understand this, please let me know I will add.
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