|
--- |
|
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. |
