Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.
Hi Everyone,
I was finetuning a TSR dataset of 487 Tables, The tables are different from the PubTabMed Dataset.
At first I annotated the Dataset using a normal annotation where bounding boxes cover the whole table and the whole columns and rows.
Which was different from the original PubTabMed annotation where the borders touch the text.
In this case the model score was:
AP50: 0.794, AP75: 0.458, AP: 0.472, AR: 0.627
I found that this score was very low so what I did was that I changed the annotation to match the PubTabMed dataset and ended up with a score of:
AP50: 0.705, AP75: 0.348, AP: 0.371, AR: 0.531
Which is much worse in terms of everything
Why is that happening, how can I fix it, and what do I need to look for to make sure that things are running well?
Hi Everyone, I was finetuning a TSR dataset of 487 Tables, The tables are different from the PubTabMed Dataset.
At first I annotated the Dataset using a normal annotation where bounding boxes cover the whole table and the whole columns and rows.
Which was different from the original PubTabMed annotation where the borders touch the text.
In this case the model score was: AP50: 0.794, AP75: 0.458, AP: 0.472, AR: 0.627
I found that this score was very low so what I did was that I changed the annotation to match the PubTabMed dataset and ended up with a score of: AP50: 0.705, AP75: 0.348, AP: 0.371, AR: 0.531
Which is much worse in terms of everything
Why is that happening, how can I fix it, and what do I need to look for to make sure that things are running well?