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--- |
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license: cc0-1.0 |
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datasets: |
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- biglam/european_art |
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base_model: |
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- blesot/Mask-RCNN |
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pipeline_tag: object-detection |
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tags: |
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- cultural |
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--- |
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# 🖼️ Saint George on a Bike – Mask R-CNN for Iconographic Object Detection |
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## Model Summary |
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This model uses the [Matterport Mask R-CNN](https://github.com/matterport/Mask_RCNN) implementation fine-tuned for detecting iconographic and symbolic elements in religious artworks. It is developed as part of the **Saint George on a Bike** project to enable semantic enrichment and understanding of historical imagery. |
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--- |
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## 🧠 Model Details |
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- **Architecture**: Mask R-CNN with ResNet backbone |
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- **Framework**: TensorFlow 1.14.0 + Keras 2.2.5 |
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- **Source**: https://github.com/matterport/Mask_RCNN |
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- **Configuration**: |
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- `NUM_CLASSES`: 69+1 (background) |
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- `DETECTION_MIN_CONFIDENCE`: 0.76 |
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--- |
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## 🎯 Use Cases |
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- Iconography detection in religious paintings |
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- Digital humanities and art historical research |
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- Training multimodal models for cultural heritage |
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- Enriching metadata in museum and archive collections |
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--- |
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## 🏷️ Labels (Selected) |
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The model detects over 40 iconographic concepts including: |
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- `crucifixion` |
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- `angel` |
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- `crown of thorns` |
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- `monk` |
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- `sword` |
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- `chalice` |
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- `dove` |
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- `lion`, `shepherd`, `scroll`, `key of heaven`, `mitre`, and more |
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> Full class list is available in the source notebook. |
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--- |
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## 📊 Training Data |
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- The model was trained on a DEArt dataset curated for the **Saint George on a Bike** project. |
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- Dataset contains annotated religious artworks with rich symbolic content. |
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- Format and exact size unspecified; annotations PascalXML structure. |
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--- |
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## 🧪 Example Usage |
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```python |
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from mrcnn.config import Config |
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from mrcnn.model import MaskRCNN |
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from mrcnn.model import mold_image |
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from keras.preprocessing.image import load_img, img_to_array |
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from numpy import expand_dims |
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import matplotlib.pyplot as plt |
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from matplotlib.patches import Rectangle |
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# Define class labels (shortened list) |
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classids=["BG","crucifixion","angel","person","crown of thorns", "horse", "dragon","bird","dog","boat","cat","book", |
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"sheep","shepherd","elephant","zebra","crown","tiara","camauro","zucchetto","mitre","saturno","skull", |
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"orange","apple","banana","nude","monk","lance","key of heaven", "banner","chalice","palm","sword","rooster", |
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"knight","scroll","lily","horn","prayer","tree","arrow","crozier","deer","devil","dove","eagle","hands", |
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"head","lion","serpent","stole","trumpet","judith","halo","helmet","shield","jug","holy shroud","god the father", |
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"swan", "butterfly", "bear", "centaur","pegasus","donkey","mouse","monkey","cow","unicorn"] |
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# Define the inference config |
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class PredictionConfig(Config): |
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NAME = "PREDICTION_cfg" |
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NUM_CLASSES = len(classids) |
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GPU_COUNT = 1 |
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IMAGES_PER_GPU = 1 |
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DETECTION_MIN_CONFIDENCE = 0.76 |
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# Initialize model |
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cfg = PredictionConfig() |
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model = MaskRCNN(mode='inference', model_dir='./', config=cfg) |
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model.load_weights('<weights of model>', by_name=True) |
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# Load and process image |
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img = load_img("example.jpg") |
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image = img_to_array(img) |
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scaled_image = mold_image(image, cfg) |
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sample = expand_dims(scaled_image, 0) |
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# Run detection |
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yhat = model.detect(sample, verbose=0)[0] |
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# Visualize detections |
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fig = plt.figure(figsize=(12, 12)) |
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ax = fig.add_subplot(111) |
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ax.imshow(img) |
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for i in range(len(yhat['rois'])): |
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y1, x1, y2, x2 = yhat['rois'][i] |
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width, height = x2 - x1, y2 - y1 |
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rect = Rectangle((x1, y1), width, height, fill=False, color='red') |
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ax.add_patch(rect) |
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ax.text(x1 + 5, y1 + 10, classids[yhat['class_ids'][i]], fontsize=12, color='white') |
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plt.show() |
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``` |
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--- |
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## 📌 Limitations |
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- Accuracy on modern images or non-religious art is not guaranteed |
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- Requires legacy versions of TensorFlow and Keras |
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--- |
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## 📚 Citation |
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If you use this model, please cite: |
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- The Matterport Mask R-CNN repository: https://github.com/matterport/Mask_RCNN |
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- DEArt Dataset |
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``` |
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@misc{reshetnikov2022deartdataseteuropeanart, |
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title={DEArt: Dataset of European Art}, |
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author={Artem Reshetnikov and Maria-Cristina Marinescu and Joaquim More Lopez}, |
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year={2022}, |
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eprint={2211.01226}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2211.01226}, |
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} |
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``` |
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--- |
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## 🙏 Acknowledgements |
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This research has been supported by the Saint George on a Bike project 2018-EU-IA-0104, co-financed by the Connecting Europe Facility of the European Union. |
