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