Duplicate from huyvux3005/manga109-segmentation-bubble

Co-authored-by: Vũ tiến huy <huyvux3005@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+language:
+  - en
+  - ja
+tags:
+  - yolo
+  - yolov11
+  - ultralytics
+  - manga
+  - speech-bubble
+  - segmentation
+  - object-detection
+  - computer-vision
+  - image-segmentation
+datasets:
+  - MS92/MangaSegmentation
+  - manga109
+pipeline_tag: image-segmentation
+library_name: ultralytics
+---
+
+# 🎯 MangaLens - Manga Speech Bubble Segmentation
+
+<div align="center">
+
+![Model](https://img.shields.io/badge/Model-YOLOv11n--seg-blue)
+![Task](https://img.shields.io/badge/Task-Instance%20Segmentation-green)
+![mAP50](https://img.shields.io/badge/mAP50-99.1%25-brightgreen)
+![License](https://img.shields.io/badge/License-Apache%202.0-orange)
+
+</div>
+
+A high-performance YOLO11n instance segmentation model fine-tuned for detecting and segmenting speech bubbles in manga/comic images.
+
+## �️ Demo Results
+
+<div align="center">
+
+| Detection on Various Manga Styles |
+|:--:|
+| ![Demo 1](assets/demo1.jpg) |
+| *Speech bubble detection on action manga with multiple bubbles* |
+
+| ![Demo 2](assets/demo2.jpg) |
+|:--:|
+| *Detection on slice-of-life manga style* |
+
+</div>
+
+## �📊 Model Performance
+
+### Final Evaluation Results (Epoch 44)
+
+| Metric | Box Detection | Mask Segmentation |
+|--------|---------------|-------------------|
+| **Precision** | 97.55% | 97.66% |
+| **Recall** | 97.03% | 97.15% |
+| **mAP@50** | 99.10% | 99.13% |
+| **mAP@50-95** | 96.67% | 94.69% |
+
+### Training Curves
+
+<div align="center">
+
+![Training Curves](assets/Code_Generated_Image.png)
+*Left: Segmentation Loss (Train vs Val) | Right: Mask mAP Metrics over epochs*
+
+</div>
+
+| Loss Type | Final Value |
+|-----------|-------------|
+| Box Loss | 0.2499 |
+| Segmentation Loss | 0.2762 |
+| Classification Loss | 0.2109 |
+| DFL Loss | 0.8064 |
+
+## 🎓 Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | `yolo11n-seg.pt` |
+| Image Size | 1600×1600 |
+| Batch Size | 8 |
+| Epochs | 100 (Early stopped at 44) |
+| Optimizer | Auto (AdamW) |
+| Learning Rate | 0.01 |
+| Weight Decay | 0.0005 |
+| Patience | 10 |
+| AMP | Enabled |
+
+### Data Augmentation
+
+- HSV Augmentation: H=0.015, S=0.7, V=0.4
+- Mosaic: 1.0
+- Flip Left-Right: 0.5
+- Scale: 0.5
+- Translate: 0.1
+
+## 📚 Training Data
+
+This model was trained on a combined dataset of:
+
+1. **[MS92/MangaSegmentation](https://huggingface.co/datasets/MS92/MangaSegmentation)** - Manga panel and bubble segmentation dataset
+2. **[Manga109](http://www.manga109.org/)** - Large-scale manga dataset with speech bubble annotations
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install ultralytics>=8.0.0
+```
+
+### Inference
+
+```python
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO("best.pt")
+
+# Run inference on an image
+results = model("manga_page.jpg")
+
+# Process results
+for result in results:
+    # Get bounding boxes
+    boxes = result.boxes
+    
+    # Get segmentation masks
+    masks = result.masks
+    
+    # Visualize results
+    result.show()
+    
+    # Save results
+    result.save("output.jpg")
+```
+
+### Batch Processing
+
+```python
+from ultralytics import YOLO
+from pathlib import Path
+
+model = YOLO("best.pt")
+
+# Process multiple images
+image_folder = Path("manga_pages/")
+results = model(list(image_folder.glob("*.jpg")), stream=True)
+
+for i, result in enumerate(results):
+    result.save(f"output_{i}.jpg")
+```
+
+### Extract Bubble Regions
+
+```python
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+model = YOLO("best.pt")
+image = cv2.imread("manga_page.jpg")
+results = model(image)[0]
+
+# Extract each bubble as a separate image
+for i, mask in enumerate(results.masks.data):
+    mask_np = mask.cpu().numpy()
+    mask_resized = cv2.resize(mask_np, (image.shape[1], image.shape[0]))
+    
+    # Apply mask
+    bubble = image.copy()
+    bubble[mask_resized < 0.5] = 0
+    
+    # Get bounding box and crop
+    coords = np.where(mask_resized >= 0.5)
+    if len(coords[0]) > 0:
+        y_min, y_max = coords[0].min(), coords[0].max()
+        x_min, x_max = coords[1].min(), coords[1].max()
+        cropped = bubble[y_min:y_max, x_min:x_max]
+        cv2.imwrite(f"bubble_{i}.png", cropped)
+```
+
+## 📁 Model Files
+
+```
+weights/
+├── best.pt      # Best checkpoint (recommended)
+└── last.pt      # Last training checkpoint
+```
+
+## 🎯 Use Cases
+
+- **Manga Translation**: Automatically detect speech bubbles for text extraction and translation
+- **Manga Analysis**: Study panel layouts and dialogue distribution
+- **Content Moderation**: Identify and process text regions in comics
+- **Accessibility**: Enable text-to-speech for manga readers
+- **Dataset Creation**: Generate annotations for manga datasets
+
+## ⚙️ Technical Details
+
+### Model Architecture
+
+- **Backbone**: YOLO11n (Nano variant)
+- **Task**: Instance Segmentation
+- **Classes**: 1 (Speech Bubble)
+- **Input**: RGB images (any size, recommended 1600×1600)
+- **Output**: Bounding boxes + Instance masks
+
+### Inference Speed
+
+| Device | Speed (ms/image) |
+|--------|------------------|
+| GPU (T4) | ~15-25 ms |
+| GPU (V100) | ~8-12 ms |
+| CPU | ~200-400 ms |
+
+## 📝 Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{mangalens2024,
+  title={MangaLens: YOLO11n Speech Bubble Segmentation Model},
+  author={MangaLens Team},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/your-username/mangalens-bubble-segmentation}
+}
+```
+
+## 📜 License
+
+This model is released under the [Apache 2.0 License](LICENSE).
+
+## 🙏 Acknowledgements
+
+- [Ultralytics](https://ultralytics.com/) for the YOLO framework
+- [MS92/MangaSegmentation](https://huggingface.co/datasets/MS92/MangaSegmentation) dataset
+- [Manga109](http://www.manga109.org/) dataset
+
+---
+
+<div align="center">
+  <b>Made with ❤️ for the manga community</b>
+</div>

args.yaml

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+task: segment
+mode: train
+model: yolo11n-seg.pt
+data: /content/dataset/data/YOLO_data/dataset.yaml
+epochs: 100
+time: null
+patience: 10
+batch: 8
+imgsz: 1600
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: /content/dataset/models/bubble-detection/YOLOv11n_Training_Results
+name: balloon_segmentation_run1
+exist_ok: true
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
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+cos_lr: false
+close_mosaic: 10
+resume: false
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+fraction: 1.0
+profile: false
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+multi_scale: false
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
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+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
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+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: /content/dataset/models/bubble-detection/YOLOv11n_Training_Results/balloon_segmentation_run1

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