| 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"]) | |
| ``` | |