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README.md

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+## MGH Critical Edition YOLO Model
+
+This repository hosts a YOLO model specifically trained to detect and annotate various elements from medieval manuscripts. The model is built and trained using the Ultralytics YOLOv8n architecture.
+
+### Dataset
+
+The model is trained on the dataset available at: [medieval-data/mgh-critical-edition-layout](https://huggingface.co/datasets/medieval-data/mgh-critical-edition-layout). This dataset comprises images from medieval manuscripts and their associated annotations.
+
+### Training Details
+
+- Architecture: YOLOv8n
+- Pretrained Model: `yolov8n.pt`
+- Image Size: 640
+- Batch Size: 25
+- Augmentation: Enabled
+- Epochs: 300
+
+### Evaluation Metrics
+
+Forthcoming...
+
+### Usage
+
+To utilize this model in your projects, you can use the `ultralytics` YOLO library. Here's a simple code snippet to get you started:
+
+```bash
+git clone https://huggingface.co/medieval-data/yolov8-mgh
+```
+
+```bash
+cd clone yolov8-mgh
+```
+
+```python
+from ultralytics import YOLO
+import cv2
+from matplotlib import pyplot as plt
+
+model = YOLO("yolo8v-mgh.pt")
+
+# Prediction on an image
+image_path = "page_103.jpg"
+results = model(image_path)
+
+# Visualize the results
+results[0].boxes.data.tolist()
+
+# Load the image
+image = cv2.imread(image_path)
+threshold = 0.5
+# Draw bounding boxes on the image
+for result in results[0].boxes.data.tolist():
+    x1, y1, x2, y2, score, class_id = result
+    if score > threshold:
+        cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
+        cv2.putText(image, results[0].names[int(class_id)].upper(), (int(x1), int(y1 - 10)),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
+
+# Convert BGR image to RGB for plotting
+image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# Display the image in the notebook
+plt.figure(figsize=(10, 10))
+plt.imshow(image_rgb)
+plt.axis('off')
+plt.show()
+```
+
+### Expected Output
+
+![outout](output.JPG)

YOLOv8-mgh.pt → yolo8v-mgh.pt

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