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tags: |
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- Pytorch |
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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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# Detectron2 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 has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conversion script. |
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The Tensorflow and Pytorch models differ slightly (padding ...), however validating both models give a difference of less than 0.03 mAP. |
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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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## This is an inference model only |
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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_rc). |