https://github.com/mosesab/yolov12-comic-panel-detection
A complete workflow for fine-tuning a YOLOv12 model to detect panels in comic book pages
https://github.com/mosesab/yolov12-comic-panel-detection
Last synced: 12 months ago
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A complete workflow for fine-tuning a YOLOv12 model to detect panels in comic book pages
- Host: GitHub
- URL: https://github.com/mosesab/yolov12-comic-panel-detection
- Owner: mosesab
- License: mit
- Created: 2025-06-30T16:48:04.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-30T17:42:52.000Z (about 1 year ago)
- Last Synced: 2025-06-30T18:43:31.299Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 13.5 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YOLOv12 Comic Panel Detection
This repository contains a complete workflow for fine-tuning a **YOLOv12** model to detect panels in comic book pages. The project uses the powerful Ultralytics library and a custom dataset managed on Roboflow.
The primary goal is to demonstrate an end-to-end process: from setting up the environment and loading a pre-trained model to training, validating, and performing inference on new images.
*Visit this space to try out the model right now: [`The_Best_Comic_Panel_Detection`](https://huggingface.co/spaces/mosesb/best-comic-panel-detection).*
## Key Features
* **State-of-the-Art Model:** Fine-tunes the `yolo12x` model, the largest and most performant variant of the YOLOv12 series.
* **Custom Dataset:** Integrates with Roboflow for easy dataset download, versioning, and management.
* **End-to-End Workflow:** The entire process is detailed in a single Jupyter Notebook (`comic-boundary-detection.ipynb`).
* **Comprehensive Evaluation:** Generates and displays key performance metrics, including loss curves, confusion matrices, and precision-recall curves.
* **Reproducibility:** Aims for easy setup and execution, allowing others to replicate the results.
## File Structure
The repository is organized as follows:
```
.
├── comic-boundary-detection.ipynb # The main notebook with all the code
├── my_yolo_train/ # Directory for all training outputs
│ ├── demonstration/ # Output from the initial model test
│ ├── finetuning/ # All artifacts from the fine-tuning process
│ │ ├── args.yaml # Training configuration
│ │ ├── results.csv # Training metrics per epoch
│ │ ├── results.png # Plot of training/validation loss & metrics
│ │ ├── confusion_matrix.png # Performance visualization
│ │ └── weights/
│ │ ├── best.pt # Best performing model checkpoint (recommended for inference)
│ │ └── last.pt # Model checkpoint from the final epoch
│ ├── prediction/ # Inference output on a sample test image
│ └── validation/ # Validation results on the test set
└── ... # Other project files
```
## Training Results
The model was trained for 200 epochs, achieving high performance on the validation set. Below are the key training metrics over time.
### Val Pred Visualization
## Model Checkpoints
The trained model weights are saved in the `my_yolo_train/finetuning/weights/` directory.
* `best.pt`: The model checkpoint with the best mAP50-95 score on the validation set. **This is the recommended model for inference.**
* `last.pt`: The model checkpoint saved at the end of the final training epoch.
## Acknowledgements
* **Ultralytics** for the amazing [YOLOv12 model](https://github.com/ultralytics/ultralytics) and library.
* **Roboflow:** for their dataset hosting platform and **custom-workflow-3-object-detection-g24r5-fmfkb** for compiling and annotating this incredible dataset.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.