Update README.md

README.md

@@ -7,4 +7,143 @@ base_model:
 pipeline_tag: object-detection
 tags:
 - cultural
----
+---
+# 🖼️ Saint George on a Bike – Mask R-CNN for Iconographic Object Detection
+
+## Model Summary
+
+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.
+
+---
+
+## 🧠 Model Details
+
+- **Architecture**: Mask R-CNN with ResNet backbone
+- **Framework**: TensorFlow 1.14.0 + Keras 2.2.5
+- **Source**: https://github.com/matterport/Mask_RCNN
+- **Configuration**:
+  - `NUM_CLASSES`: 69+1 (background)
+  - `DETECTION_MIN_CONFIDENCE`: 0.76
+
+---
+
+## 🎯 Use Cases
+
+- Iconography detection in religious paintings
+- Digital humanities and art historical research
+- Training multimodal models for cultural heritage
+- Enriching metadata in museum and archive collections
+
+---
+
+## 🏷️ Labels (Selected)
+
+The model detects over 40 iconographic concepts including:
+
+- `crucifixion`
+- `angel`
+- `crown of thorns`
+- `monk`
+- `sword`
+- `chalice`
+- `dove`
+- `lion`, `shepherd`, `scroll`, `key of heaven`, `mitre`, and more
+
+> Full class list is available in the source notebook.
+
+---
+
+## 📊 Training Data
+
+- The model was trained on a DEArt dataset curated for the **Saint George on a Bike** project.
+- Dataset contains annotated religious artworks with rich symbolic content.
+- Format and exact size unspecified; annotations PascalXML structure.
+
+---
+
+## 🧪 Example Usage
+
+```python
+from mrcnn.config import Config
+from mrcnn.model import MaskRCNN
+from mrcnn.model import mold_image
+from keras.preprocessing.image import load_img, img_to_array
+from numpy import expand_dims
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+
+# Define class labels (shortened list)
+classids=["BG","crucifixion","angel","person","crown of thorns", "horse", "dragon","bird","dog","boat","cat","book",
+          "sheep","shepherd","elephant","zebra","crown","tiara","camauro","zucchetto","mitre","saturno","skull",  
+          "orange","apple","banana","nude","monk","lance","key of heaven", "banner","chalice","palm","sword","rooster",
+          "knight","scroll","lily","horn","prayer","tree","arrow","crozier","deer","devil","dove","eagle","hands",
+          "head","lion","serpent","stole","trumpet","judith","halo","helmet","shield","jug","holy shroud","god the father",
+          "swan", "butterfly", "bear", "centaur","pegasus","donkey","mouse","monkey","cow","unicorn"]
+
+# Define the inference config
+class PredictionConfig(Config):
+    NAME = "PREDICTION_cfg"
+    NUM_CLASSES = len(classids)
+    GPU_COUNT = 1
+    IMAGES_PER_GPU = 1
+    DETECTION_MIN_CONFIDENCE = 0.76
+
+# Initialize model
+cfg = PredictionConfig()
+model = MaskRCNN(mode='inference', model_dir='./', config=cfg)
+model.load_weights('<weights of model>', by_name=True)
+
+# Load and process image
+img = load_img("example.jpg")
+image = img_to_array(img)
+scaled_image = mold_image(image, cfg)
+sample = expand_dims(scaled_image, 0)
+
+# Run detection
+yhat = model.detect(sample, verbose=0)[0]
+
+# Visualize detections
+fig = plt.figure(figsize=(12, 12))
+ax = fig.add_subplot(111)
+ax.imshow(img)
+for i in range(len(yhat['rois'])):
+    y1, x1, y2, x2 = yhat['rois'][i]
+    width, height = x2 - x1, y2 - y1
+    rect = Rectangle((x1, y1), width, height, fill=False, color='red')
+    ax.add_patch(rect)
+    ax.text(x1 + 5, y1 + 10, classids[yhat['class_ids'][i]], fontsize=12, color='white')
+plt.show()
+```
+
+---
+
+## 📌 Limitations
+
+- Accuracy on modern images or non-religious art is not guaranteed
+- Requires legacy versions of TensorFlow and Keras
+
+---
+
+## 📚 Citation
+
+If you use this model, please cite:
+
+- The Matterport Mask R-CNN repository: https://github.com/matterport/Mask_RCNN
+- DEArt Dataset
+```
+@misc{reshetnikov2022deartdataseteuropeanart,
+      title={DEArt: Dataset of European Art}, 
+      author={Artem Reshetnikov and Maria-Cristina Marinescu and Joaquim More Lopez},
+      year={2022},
+      eprint={2211.01226},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2211.01226}, 
+}
+```
+
+---
+
+## 🙏 Acknowledgements
+
+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.