MA-Net Speech Bubble Segmentation
MA-Net model for segmenting speech bubbles in webtoon and manga images.
This checkpoint was trained for use in the public TOONTRA reconstruction.
Model
- Architecture: MA-Net
- Encoder: ResNet34
- Framework: PyTorch
- Implementation:
segmentation-models-pytorch
- Input size: 512 × 512
- Output: Binary speech-bubble mask
- Inference threshold: 0.45
Training Data
The model was trained on:
Roboflow manga-segment_v2, version 5
The dataset is not redistributed in this repository and remains subject to its original license and terms.
Training Configuration
| Parameter | Value |
|---|
| Architecture | MA-Net |
| Encoder | ResNet34 |
| Input size | 512 × 512 |
| Crop padding | 15% |
| Loss | Dice + BCE |
| Optimizer | AdamW |
| Batch size | 8 |
| Best epoch | 18 |
| Threshold | 0.45 |
Evaluation
| Metric | Score |
|---|
| Validation Dice | 0.9828 |
| Test Dice | 0.9805 |
| Test IoU | 0.9622 |
Loading the Model
1import torch
2import segmentation_models_pytorch as smp
3
4checkpoint = torch.load(
5 "manet_bubble_segmentation.pth",
6 map_location="cpu",
7 weights_only=True,
8)
9
10model = smp.MAnet(
11 encoder_name="resnet34",
12 encoder_weights=None,
13 in_channels=3,
14 classes=1,
15)
16
17model.load_state_dict(checkpoint["state_dict"], strict=True)
18model.eval()
19
20threshold = float(checkpoint["threshold"])
The model expects RGB speech-bubble crops. Preserve the crop aspect ratio,
resize it to fit inside a 512 × 512 canvas, and center it on a white
background. Normalize the image with ImageNet mean
[0.485, 0.456, 0.406] and standard deviation
[0.229, 0.224, 0.225].
Apply sigmoid to the model output and use the checkpoint threshold (0.45)
to obtain the binary mask. Remove the letterbox padding and resize the mask
back to the original crop size with nearest-neighbor interpolation.
Intended Use
The model is intended for speech-bubble segmentation in webtoon and manga images.
It expects an approximate speech-bubble crop rather than a complete long webtoon page. A separate detector can be used to locate the bubble before segmentation.
Limitations
Performance may be lower for unusual bubble styles, transparent bubbles, poorly defined boundaries, or image styles substantially different from the training data.
License
The checkpoint is released under the Apache License 2.0.
Third-party datasets and libraries remain subject to their respective licenses and terms.
