readme: ---
  tags:
  - Tensorflow
  license: apache-2.0
  datasets:
  - Publaynet
  ---
  
  
  # Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
  
  The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . 
  
  Please check: [Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis](https://arxiv.org/abs/1908.07836). 
  
  This model is different from the model used the paper. 
  
  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_publaynet).
  
  
  ## 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
  
  publaynet = DatasetRegistry.get_dataset("publaynet")
  path_config_yaml=os.path.join(get_configs_dir_path(),"tp/layout/conf_frcnn_layout.yaml")
  path_weights = ""
  dataset_train = publaynet
  config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.EVAL_PERIOD=200","TRAIN.STARTING_EPOCH=1",
                    "PREPROC.TRAIN_SHORT_EDGE_SIZE=[800,1200]","TRAIN.CHECKPOINT_PERIOD=50",
                    "BACKBONE.FREEZE_AT=0"]
  build_train_config=["max_datapoints=335703"]
  dataset_val = publaynet
  build_val_config = ["max_datapoints=2000"]
      
  coco_metric = MetricRegistry.get_metric("coco")
  
  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"
                    )
  ```