---
license: apache-2.0
tags:
  - manga
  - panel-detection
  - text-detection
  - yolo
  - yolo26
  - tflite
  - android
  - on-device
  - mobile
  - comic
  - manga109
datasets:
  - hal-utokyo/Manga109-s
pipeline_tag: object-detection
---

# Manga panel & text detector (YOLO26-nano)

A lightweight YOLO26-nano model fine-tuned on [Manga109-s](https://huggingface.co/datasets/hal-utokyo/Manga109-s) for detecting **panels** and **text bubbles** in manga pages. Designed for on-device Android inference via TFLite/LiteRT.

## Performance

### INT8 TFLite (2.71 MB)

| Metric | All | Panel | Text |
|--------|-----|-------|------|
| mAP50 | 0.956 | 0.985 | 0.928 |
| mAP50-95 | 0.846 | 0.953 | 0.740 |
| Precision | 0.954 | 0.966 | 0.935 |
| Recall | 0.912 | 0.956 | 0.877 |

### Quantization impact (FP32 vs INT8)

| Metric | FP32 | INT8 | Delta |
|--------|------|------|-------|
| mAP50 | 0.9569 | 0.9561 | -0.0008 |
| mAP50-95 | 0.8464 | 0.8458 | -0.0006 |
| Precision | 0.9507 | 0.9535 | +0.0028 |
| Recall | 0.9167 | 0.9124 | -0.0043 |

### Training curves

![Validation Metrics](training_curves/validation_metrics.png)

![Training Loss](training_curves/training_loss.png)

## Model details

- **Architecture:** YOLO26-nano (2.57M parameters)
- **Input size:** 640x640
- **Classes:** `0: panel`, `1: text`
- **INT8 TFLite size:** 2.71 MB
- **Inference speed:** ~100-180ms CPU

## Files

| File | Format | Size | Use case |
|------|--------|------|----------|
| `manga_panel_detector_fp32.pt` | PyTorch FP32 | ~15 MB | Fine-tuning, FP16 export, further training |
| `manga_panel_detector_int8.tflite` | TFLite INT8 | 2.71 MB | Android/mobile deployment |

## Usage

### Python (ultralytics)
```python
from ultralytics import YOLO

model = YOLO("manga_panel_detector_fp32.pt")
results = model.predict("manga_page.jpg", conf=0.25)

for box in results[0].boxes:
    cls = int(box.cls)  # 0=panel, 1=text
    conf = float(box.conf)
    x1, y1, x2, y2 = box.xyxy[0].tolist()
    label = "panel" if cls == 0 else "text"
    print(f"{label} ({conf:.2f}): [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}]")
```

### Android (TFLite / LiteRT)

Use `manga_panel_detector_int8.tflite` with the TensorFlow Lite interpreter or Google's LiteRT runtime.

- **Input:** 640x640 RGB image normalized to [0, 1]
- **Output:** Up to 300 detections, each with [x1, y1, x2, y2, confidence, class_id]
- **Recommended confidence threshold:** 0.25

## Training

| Parameter | Value |
|-----------|-------|
| Base model | YOLO26n pretrained on COCO |
| Dataset | Manga109-s (87 manga titles, ~18k pages, ~32k annotations) |
| Classes | panel (frame), text (speech/dialog) |
| Epochs | 66 (early stopping, patience=20) |
| Best epoch | 48 (mAP50 = 0.957) |
| Image size | 640x640 |
| Batch size | 16 |
| GPU | NVIDIA T4 (Google Colab) |
| Training time | ~4 hours |
| Augmentation | No hue/saturation shift (grayscale manga), no horizontal flip (RTL reading order), reduced mosaic (0.5), no rotation (axis-aligned panels) |

## Model citation

If you use this model, please cite:
```bibtex
@misc{leoxs22_manga_panel_detector_2026,
    author={Leandro Narosky},
    title={{Manga Panel and Text Detector (YOLO26-nano)}},
    year={2026},
    publisher={Hugging Face},
    url={https://huggingface.co/leoxs22/manga-panel-detector-yolo26n}
}
```

## Dataset citation

This model was trained on Manga109-s. Please cite:
```bibtex
@article{multimedia_aizawa_2020,
    author={Kiyoharu Aizawa and Azuma Fujimoto and Atsushi Otsubo and Toru Ogawa and Yusuke Matsui and Koki Tsubota and Hikaru Ikuta},
    title={Building a Manga Dataset ``Manga109'' with Annotations for Multimedia Applications},
    journal={IEEE MultiMedia},
    volume={27},
    number={2},
    pages={8--18},
    doi={10.1109/mmul.2020.2987895},
    year={2020}
}

@article{mtap_matsui_2017,
    author={Yusuke Matsui and Kota Ito and Yuji Aramaki and Azuma Fujimoto and Toru Ogawa and Toshihiko Yamasaki and Kiyoharu Aizawa},
    title={Sketch-based Manga Retrieval using Manga109 Dataset},
    journal={Multimedia Tools and Applications},
    volume={76},
    number={20},
    pages={21811--21838},
    doi={10.1007/s11042-016-4020-z},
    year={2017}
}
```

## License

This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

The training data (Manga109-s) has its own [license terms](https://huggingface.co/datasets/hal-utokyo/Manga109-s). Per condition 5 of the Manga109-s license, results obtained from machine learning experiments (including pre-trained models) may be used for commercial purposes, provided that the use of the dataset is clearly indicated.