| --- |
| pretty_name: GenHisDoc |
| repos: |
| - repo: https://github.com/Anarchiviste/GenHisDoc_Lab/tree/main |
| language: |
| - en |
| - fr |
| tags: |
| - computer vision |
| - historical documents |
| - detection |
| - python |
| base_model: Ultralytics/YoloV8L |
| datasets : |
| - Anarchiviste/GenHisDoc_dataset |
| license: cc-by-nc-sa-4.0 |
| --- |
| # Models trained on GenHisDoc |
|
|
| [the GenHisDoc dataset is on Huggin Face](https://huggingface.co/datasets/Anarchiviste/GenHisDoc_dataset/tree/main) |
|
|
| GenHisDoc is a generalistic datasets for historical documents layout recognition and detection. GenHisDoc use a combination of several previously published datasets which have been adapted and re-annotated to work together and our own annotated data. All embedded datasets are licensed under Creative Commons or other licenses that permit reuse. |
|
|
| This repository contains the different models and training metrics on different versions of GenHisDoc. Every directory is a model with the weights, the test run and an info.yaml with information about the parameters used. |
|
|
| # |
| ``` |
| . |
| ├── Yolo26L |
| │ ├── Train |
| │ │ └── weights -> best.pt # best performing model |
| │ └── Val |
| ├── test_images # not finished |
| ├── inference.py # run a detection using a yolo model older than YolOV5 |
| ├── filter_illustration_only.py # suppress every classes annotations that we want |
| └── metric.py # create metrics between a ground truth set and a prediction set |
| ``` |
|
|
| # Yolo26L trained on GenHisDoc against test set |
| ``` |
| ================================================== |
| ÉVALUATION MODÈLE : yolo26L-21-06-2026 |
| ================================================== |
| Fichiers traités : 876 |
| Nombre total de GT : 1659 |
| IoU Moyen (Detections): 0.8595 |
|
|
| --- PERFORMANCES GLOBALES (Average Precision) --- |
| AP@50 (mAP@50) : 0.8404 (84.04%) |
|
|
| --- MÉTRIQUES AU SEUIL FIXE (Conf >= 0.25) --- |
| Vrais Positifs (TP) : 1448 |
| Faux Positifs (FP) : 261 |
| Faux Négatifs (FN) : 211 |
| Précision : 0.8473 |
| Rappel (Recall) : 0.8728 |
| Score F1 : 0.8599 |
| ================================================== |
| ``` |
| |
| # Yolo26L against Aikon Illustration (only on illustration) |
| ``` |
| ================================================== |
| ÉVALUATION MODÈLE : GenHisDoc illustration |
| ================================================== |
| Fichiers traités : 876 |
| Nombre total de GT : 558 |
| IoU Moyen (Detections): 0.9033 |
|
|
| --- PERFORMANCES GLOBALES (Average Precision) --- |
| AP@50 (mAP@50) : 0.8787 (87.87%) |
|
|
| --- MÉTRIQUES AU SEUIL FIXE (Conf >= 0.25) --- |
| Vrais Positifs (TP) : 501 |
| Faux Positifs (FP) : 80 |
| Faux Négatifs (FN) : 57 |
| Précision : 0.8623 |
| Rappel (Recall) : 0.8978 |
| Score F1 : 0.8797 |
| ================================================== |
|
|
| ================================================== |
| ÉVALUATION MODÈLE : Aikon Illustration |
| ================================================== |
| Fichiers traités : 876 |
| Nombre total de GT : 558 |
| IoU Moyen (Detections): 0.8762 |
|
|
| --- PERFORMANCES GLOBALES (Average Precision) --- |
| AP@50 (mAP@50) : 0.4815 (48.15%) |
|
|
| --- MÉTRIQUES AU SEUIL FIXE (Conf >= 0.25) --- |
| Vrais Positifs (TP) : 355 |
| Faux Positifs (FP) : 208 |
| Faux Négatifs (FN) : 203 |
| Précision : 0.6306 |
| Rappel (Recall) : 0.6362 |
| Score F1 : 0.6334 |
| ================================================== |
| ``` |
| |
