| | --- |
| | library_name: transformers |
| | --- |
| | |
| | Microsoft Table Transformer Table Structure Recognition trained on Pubtables and Fintabnet |
| |
|
| | If you do not have the deepdoctection Profile of the model, please add: |
| |
|
| |
|
| | ```python |
| | import deepdoctection as dd |
| | |
| | dd.ModelCatalog.register("deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", dd.ModelProfile( |
| | name="deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", |
| | description="Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper " |
| | "Aligning benchmark datasets for table structure recognition by Smock et " |
| | "al. This model is devoted to table structure recognition and assumes to receive a slightly cropped" |
| | "table as input. It will predict rows, column and spanning cells. Use a padding of around 5 pixels", |
| | size=[115511753], |
| | tp_model=False, |
| | config="deepdoctection/tatr_tab_struct_v2/config.json", |
| | preprocessor_config="deepdoctection/tatr_tab_struct_v2/preprocessor_config.json", |
| | hf_repo_id="deepdoctection/tatr_tab_struct_v2", |
| | hf_model_name="pytorch_model.bin", |
| | hf_config_file=["config.json", "preprocessor_config.json"], |
| | categories={ |
| | "1": dd.LayoutType.table, |
| | "2": dd.LayoutType.column, |
| | "3": dd.LayoutType.row, |
| | "4": dd.CellType.column_header, |
| | "5": dd.CellType.projected_row_header, |
| | "6": dd.CellType.spanning, |
| | }, |
| | dl_library="PT", |
| | model_wrapper="HFDetrDerivedDetector", |
| | )) |
| | ``` |
| |
|
| | When running the model within the deepdoctection analyzer, adjust the segmentation parameters in order to get better predictions. |
| |
|
| | ```python |
| | import deepdoctection as dd |
| | |
| | analyzer = dd.get_dd_analyzer(reset_config_file=True, config_overwrite=["PT.ITEM.WEIGHTS=deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", |
| | "PT.ITEM.FILTER=['table']", |
| | "PT.ITEM.PAD.TOP=5", |
| | "PT.ITEM.PAD.RIGHT=5", |
| | "PT.ITEM.PAD.BOTTOM=5", |
| | "PT.ITEM.PAD.LEFT=5", |
| | "SEGMENTATION.THRESHOLD_ROWS=0.9", |
| | "SEGMENTATION.THRESHOLD_COLS=0.9", |
| | "SEGMENTATION.REMOVE_IOU_THRESHOLD_ROWS=0.3", |
| | "SEGMENTATION.REMOVE_IOU_THRESHOLD_COLS=0.3", |
| | "WORD_MATCHING.MAX_PARENT_ONLY=True"]) |
| | ``` |
| |
|
| |
|
