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.
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.