Add model description and evaluation to README

README.md

@@ -12,3 +12,42 @@ metrics:
 - 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}
+}
+```