 How this model was trained.   To recreate the model run on the **deep**doctection framework, run:  ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn  pubtabnet = DatasetRegistry.get_dataset("pubtabnet") pubtabnet.dataflow.categories.set_cat_to_sub_cat({"ITEM":"row_col"}) pubtabnet.dataflow.categories.filter_categories(categories=["ROW","COLUMN"])  path_config_yaml=os.path.join(get_configs_dir_path(),"tp/rows/conf_frcnn_rows.yaml") path_weights = ""  dataset_train = pubtabnet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1", "TRAIN.CHECKPOINT_PERIOD=50"] build_train_config=["max_datapoints=500000","rows_and_cols=True"] dataset_val = pubtabnet build_val_config = ["max_datapoints=2000","rows_and_cols=True"]  coco_metric = MetricRegistry.get_metric("coco") coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]])  train_faster_rcnn(path_config_yaml=path_config_yaml,                   dataset_train=dataset_train,                   path_weights=path_weights,                   config_overwrite=config_overwrite,                   log_dir="/path/to/dir",                   build_train_config=build_train_config,                   dataset_val=dataset_val,                   build_val_config=build_val_config,                   metric=coco_metric,                   pipeline_component_name="ImageLayoutService"                   ) ``` 