readme: ---
  library_name: Doc-UFCN
  license: mit
  tags:
  - Doc-UFCN
  - PyTorch
  - Object detection
  metrics:
  - IoU
  - F1
  - AP@.5
  - AP@.75
  - AP@[.5,.95]
  ---
  
  
  # Generic page detection
  
  The generic page detection model predicts single pages from document images.
  
  ## Model description
  
  The model has been trained using the Doc-UFCN library on [Horae](https://github.com/oriflamms/HORAE/) and [READ-BAD](https://github.com/ctensmeyer/pagenet) datasets.
  It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.
  
  ## Evaluation results
  
  The model achieves the following results:
  
  |       | set      | IoU   | F1    | AP@[.5] | AP@[.75] | AP@[.5,.95] |
  | ----- | -------- | ----- | ----- | ------- | -------- | ----------- |
  | HOME  | test     | 93.92 | 95.84 | 98.98   | 98.98    | 97.61       |
  | Horae | test     | 96.68 | 98.31 | 99.76   | 98.49    | 98.08       |
  | Horae | test-300 | 95.66 | 97.27 | 98.87   | 98.45    | 97.38       |
  
  ## How to use
  
  Please refer to the Doc-UFCN library page (https://pypi.org/project/doc-ufcn/) to use this model.
  
  # Cite us!
  
  ```bibtex
  @inproceedings{boillet2020,
      author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
      title = {{Multiple Document Datasets Pre-training Improves Text Line Detection With
                Deep Neural Networks}},
      booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
      year = {2021},
      month = Jan,
      pages = {2134-2141},
      doi = {10.1109/ICPR48806.2021.9412447}
  }
  ```