readme: ---
  tags:
  - Tensorflow
  license: apache-2.0
  datasets:
  - Pubtabnet
  ---
  
  
  # Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. 
  
  The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . 
  
  Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683). 
  
  The model has been trained on detecting cells from tables. Note, that the datasets contains tables only. Therefore, it is required to perform a table detection task before 
  detecting cells.
  
  The code has been adapted so that it can be used in a **deep**doctection pipeline. 
  
  ## How this model can be used
  
  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.
  
  ## This is an inference model only
  
  To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check this [model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c) .
  
  ## 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.filter_categories(categories="CELL")
      
  path_config_yaml=os.path.join(get_configs_dir_path(),"tp/cell/conf_frcnn_cell.yaml")
  path_weights = ""
      
  dataset_train = pubtabnet
  config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1",
                    "TRAIN.CHECKPOINT_PERIOD=50","BACKBONE.FREEZE_AT=0", "PREPROC.TRAIN_SHORT_EDGE_SIZE=[200,600]"]
  build_train_config=["max_datapoints=500000"]
  dataset_val = pubtabnet
  build_val_config = ["max_datapoints=4000"]
      
  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"
                    )
  ```
  
  ## How to fine-tune this model
  
  To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial.