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--- |
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license: cc-by-4.0 |
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tags: |
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- yolov5 |
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- yolo |
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- digital humanities |
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- object detection |
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- computer-vision |
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- document layout analysis |
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- pytorch |
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datasets: |
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- datacatalogue |
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--- |
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# What's YOLOv5 |
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YOLOv5 is an open-source object detection model released by [Ultralytics](https://ultralytics.com/), on [Github](https://github.com/ultralytics/yolov5). |
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# DataCatalogue (or DataCat) |
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[DataCatalogue](https://github.com/DataCatalogue) is a research project jointly led by Inria, the Bibliothèque nationale de France (National Library of France), and the Institut national d'histoire de l'art (National Institute of Art History). |
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It aims at restructuring OCR-ed auction sale catalogs kept in France national collections into TEI-XML, using machine learning solutions. |
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# DataCat Yolov5 |
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We trained a YOLOv5 model on custom data to perform document layout analysis on auction sale catalogs. |
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The training set consists of **581 images**, annotated with **two classes**: |
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* *title* (585 instances) |
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* *entry* (it refers to a catalog entry) (5017 instances) |
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59 images were used for validation. |
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We reached: |
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| precision | recall | mAP_0.5 | mAP_0.5:0.95 | |
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|---|---|---|---| |
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| 0.99 | 0.99 | 0.98 | 0.75 | |
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# Dataset |
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The dataset is not released for the moment. |
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## Demo |
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An interactive demo is available on the following HugginFace Space: https://huggingface.co/spaces/HugoSchtr/DataCat_Yolov5 |
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<img alt='detection example' src="https://huggingface.co/HugoSchtr/yolov5_datacat/resolve/main/eval/detection_example.png" width=30% height=30%> |
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## What's next |
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The model performs well on our data and now needs to be incorporated into a dedicated pipeline for the research project. |
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We also plan to train a new model on a larger training set in the near future. |
