Add model description, how to use, how trained, how to fine tune

README.md

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+---
+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.
+
+## 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.