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YOLOv12 for Comic Panel Detection

This repository contains a YOLOv12x object detection model fine-tuned to detect individual panels in comic book pages. The model identifies the bounding boxes for each panel, making it a valuable tool for digitizing comics, extracting content, or building datasets for downstream analysis.

This model was trained in PyTorch using the powerful ultralytics library and demonstrates high performance on a custom-annotated dataset of comic pages.

Visit this space to try out the model right now: The_Best_Comic_Panel_Detection.

Model Details

• Architecture: YOLOv12x (the extra-large variant)
• Fine-tuned on: A custom Roboflow dataset named "Custom-Workflow-3-Object-Detection-1".
• Classes: Comic Panel
• Frameworks: PyTorch, Ultralytics

How to Get Started

You can easily use this model with the ultralytics library. The model file best.pt from this repository is required.

# 1. Install Ultralytics
!pip install ultralytics

from ultralytics import YOLO
from PIL import Image

# 2. Load the fine-tuned model
# Make sure 'best.pt' is in your current directory
model = YOLO('best.pt')

# 3. Run inference on an image
image_path = 'path/to/your/comic_page.jpg'
results = model.predict(source=image_path)

# 4. Process and visualize results
# The 'results' object contains bounding boxes, classes, and confidence scores
for result in results:
    # Plotting will draw the bounding boxes on the image
    im_array = result.plot()
    im = Image.fromarray(im_array[..., ::-1]) # Convert BGR to RGB
    im.show() # Display the image
    # or
    # im.save('prediction_result.jpg')

# You can also access bounding box data directly
for box in results[0].boxes:
    print("Class:", model.names[int(box.cls)])
    print("Confidence:", box.conf.item())
    print("Coordinates (xyxy):", box.xyxy[0].tolist())
    print("-" * 20)

Training

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