deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c

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 .

Regarding the dataset, please check: Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation.

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 deepdoctection pipeline.

How this model can be used

This model can be used with the deepdoctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this Get_started tutorial.

How this model was trained.

To recreate the model run on the deepdoctection framework, run:

>>> 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 tutorial.