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--- |
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license: cc-by-nc-2.0 |
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datasets: |
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- medieval-data/mgh-critical-edition-layout |
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pipeline_tag: object-detection |
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--- |
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## MGH Critical Edition YOLO Model |
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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. |
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### Dataset |
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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 critical editions and their associated annotations. |
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### Training Details |
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- Architecture: YOLOv8n |
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- Pretrained Model: `yolov8n.pt` |
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- Image Size: 640 |
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- Batch Size: 25 |
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- Augmentation: Enabled |
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- Epochs: 300 |
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### Evaluation Metrics |
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Forthcoming... |
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### Usage |
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To utilize this model in your projects, you can use the `ultralytics` YOLO library. Here's a simple code snippet to get you started: |
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```bash |
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git clone https://huggingface.co/medieval-data/yolov8-mgh |
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``` |
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```bash |
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cd clone yolov8-mgh |
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``` |
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```python |
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from ultralytics import YOLO |
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import cv2 |
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from matplotlib import pyplot as plt |
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model = YOLO("yolo8v-mgh.pt") |
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# Prediction on an image |
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image_path = "page_103.jpg" |
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results = model(image_path) |
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# Visualize the results |
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results[0].boxes.data.tolist() |
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# Load the image |
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image = cv2.imread(image_path) |
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threshold = 0.5 |
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# Draw bounding boxes on the image |
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for result in results[0].boxes.data.tolist(): |
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x1, y1, x2, y2, score, class_id = result |
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if score > threshold: |
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cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4) |
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cv2.putText(image, results[0].names[int(class_id)].upper(), (int(x1), int(y1 - 10)), |
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cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA) |
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# Convert BGR image to RGB for plotting |
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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# Display the image in the notebook |
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plt.figure(figsize=(10, 10)) |
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plt.imshow(image_rgb) |
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plt.axis('off') |
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plt.show() |
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``` |
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### Expected Output |
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