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README.md

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+---
+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.
+
+## 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")
+``` 
+
+## 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.