YOLOv8_trained_Bombe

YOLOv8 Object Detection Model for Die Bombe

Model Description

This repository contains a fine-tuned YOLOv8m object detection model trained on digitized pages of the historical satirical periodical Die Bombe.

The model is based on the Ultralytics yolov8m.pt checkpoint.

Classes

The annotation schema contains seven categories:

• Advertisement
• Comic
• Editorial Cartoon
• Headline
• Illustration
• Map
• Photograph

The training data contain the following numbers of annotated objects:

Photograph and Map are therefore part of the annotation schema but are not meaningfully represented in the trained model.

Training Data

The model was trained on manually annotated pages of Die Bombe.

The dataset was divided into:

• 508 training pages
• 106 validation pages

The training and validation sets contain separate periodical issues.

The annotated training dataset is published separately on Zenodo:

Dataset: [Training-Dataset_Bombe_1871-1925]
DOI: [10.5281/zenodo.18757852]

Training Configuration

Training was performed with Ultralytics YOLOv8 using the following configuration:

Early stopping was enabled with a patience value of 20. No further improvement in [email protected]–0.95 was observed after epoch 17, and training stopped after epoch 37.

The best-performing checkpoint is provided as best.pt.

Evaluation

The final model was evaluated on the held-out validation set of 106 pages containing 1,004 annotated objects.

Overall Results

Class-Specific Results

Photograph and Map do not have meaningful evaluation results because they are not sufficiently represented in the training data.

Reproducibility

The code and notebooks used for model training, evaluation, and subsequent processing are available in the Building Character GitHub repository:

Code: https://github.com/lisagollner/Building-Character_Code

The Corpus created with the workflow using this model is distributed over Zenodo:

Corpus-Title: Building Character Corpus

DOI: 10.5281/zenodo.21918823

The model was trained using:

• Ultralytics: YOLOv8.2.0
• Python: 3.12.13
• PyTorch: 2.4.1+cu121
• GPU: NVIDIA Tesla T4

Additional training configuration is included with the model files.

License

This model is released under the GNU Affero General Public License v3.0 (AGPL-3.0).

The model was trained using the Ultralytics YOLOv8 framework. Users should consult the applicable Ultralytics and AGPL-3.0 licensing terms when reusing or redistributing the model.