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---
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tags:
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- Tensorflow
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license: apache-2.0
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datasets:
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- Pubtabnet
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---
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# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
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The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
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Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683).
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The model has been trained on detecting rows and columns for tables. As rows and column bounding boxes are not a priori an element of the annotations they are
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calculated using the bounding boxes of the cells and the intrinsic structure of the enclosed HTML.
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The code has been adapted so that it can be used in a **deep**doctection pipeline.
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## How this model can be used
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This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial.
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## How this model was trained.
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To recreate the model run on the **deep**doctection framework, run:
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```python
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>>> import os
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>>> from deep_doctection.datasets import DatasetRegistry
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>>> from deep_doctection.eval import MetricRegistry
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>>> from deep_doctection.utils import get_configs_dir_path
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>>> from deep_doctection.train import train_faster_rcnn
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pubtabnet = DatasetRegistry.get_dataset("pubtabnet")
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pubtabnet.dataflow.categories.set_cat_to_sub_cat({"ITEM":"row_col"})
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pubtabnet.dataflow.categories.filter_categories(categories=["ROW","COLUMN"])
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path_config_yaml=os.path.join(get_configs_dir_path(),"tp/rows/conf_frcnn_rows.yaml")
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path_weights = ""
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dataset_train = pubtabnet
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config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1", "TRAIN.CHECKPOINT_PERIOD=50"]
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build_train_config=["max_datapoints=500000","rows_and_cols=True"]
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dataset_val = pubtabnet
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build_val_config = ["max_datapoints=2000","rows_and_cols=True"]
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coco_metric = MetricRegistry.get_metric("coco")
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coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]])
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train_faster_rcnn(path_config_yaml=path_config_yaml,
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dataset_train=dataset_train,
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path_weights=path_weights,
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config_overwrite=config_overwrite,
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log_dir="/path/to/dir",
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build_train_config=build_train_config,
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dataset_val=dataset_val,
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build_val_config=build_val_config,
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metric=coco_metric,
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pipeline_component_name="ImageLayoutService"
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)
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```
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## How to fine-tune this model
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To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial. |
