readme: ---
  tags:
  - Pytorch
  license: apache-2.0
  datasets:
  - Pubtabnet
  ---
  
  
  # Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. 
  
  The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script. 
  The Tensorflow and Pytorch models differ slightly (padding ...), however validating both models give a difference of less than 0.03 mAP.   
  
  A second model has been added where the Tensorpack model has been used as initial checkpoint and training has been resumed for 50K iterations.
  Performance of this model is now superior to the Tensorpack model.   
  
  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 use Tensorflow, as well as its training script. More information can be found in this [this model card](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c). 
  