Update README.md

README.md

@@ -1,29 +1,32 @@
----
-tags:
-- Pytorch
-license: apache-2.0
-datasets:
-- Publaynet
----
-
-
-# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
-
-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.   
-
-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 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_publaynet). 
-
+---
+tags:
+- Pytorch
+license: apache-2.0
+datasets:
+- Publaynet
+---
+
+
+# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
+
+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 20K iterations.
+Performance of this model is now superior to the Tensorpack model.   
+
+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 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_publaynet). 
+