mosesb/best-comic-panel-detection

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`](https://huggingface.co/spaces/mosesb/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 Procedure

The model was fine-tuned using transfer learning from a YOLOv12x checkpoint pre-trained on the COCO dataset.

Training Hyperparameters

• —Image Size: 640x640
• —Batch Size: 16
• —Optimizer: AdamW (lr=0.002)
• —Epochs: 200
• —Patience: 100 epochs for early stopping

[image]

Evaluation

The model's performance was evaluated on the validation set during training. The final metrics are based on the checkpoint that achieved the highest mAP50-95.

Key Performance Metrics

The model achieves near-perfect precision and recall on the validation data, indicating a strong ability to correctly identify comic panels within the styles present in the dataset.

[image]

Qualitative Results

The model correctly identifies panels of various sizes and layouts in the validation set.

[image]

Intended Use and Limitations

This model is intended for applications requiring the segmentation of comic book pages into their constituent panels. This can be a pre-processing step for:

• —Creating structured digital reading experiences.
• —Extracting text or characters from individual panels.
• —Analyzing comic book layouts and artistic styles.

The model has been tested in real world applications and has shown promising results.

Limitations

• —Non-Rectangular Panels: The model is trained to detect rectangular bounding boxes and may struggle with highly irregular or overlapping panel shapes.

Acknowledgements

• —Ultralytics for the amazing YOLOv12 model and library.
• —Roboflow: for their dataset hosting platform and custom-workflow-3-object-detection-g24r5-fmfkb for compiling and annotating this incredible dataset.

This model card is based on the training notebook [`YOLOV12-Comic-Panel-Detection`](https://github.com/mosesab/YOLOV12-Comic-Panel-Detection).