 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"
                   )
 ```
 
 