Hugging Face's logo

leoxs22
/
manga-panel-detector-yolo26n

Object Detection
LiteRT
manga
panel-detection
text-detection
yolo
yolo26
android
on-device
mobile
comic
manga109
Model card Files
xet
 Community
2
manga-panel-detector-yolo26n
File size: 4,643 Bytes
535bbe1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
---
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.