---
license: apache-2.0
tags:
  - watermark-removal
  - image-segmentation
  - unet++
  - pytorch
---

# Watermark Remover — Multi-Model v2

6 specialized UNet++ segmentation models for watermark detection, combined with pixel-wise max fusion.

## Models

| File | Specialty | Encoder | Best Val IoU | Size |
|------|-----------|---------|-------------|------|
| segmenter_centered_text.pth | centered_text | efficientnet-b4 | N/A | 84.0 MB |
| segmenter_line_pattern.pth | line_pattern | efficientnet-b4 | N/A | 84.0 MB |
| segmenter_logo.pth | logo | efficientnet-b4 | N/A | 84.0 MB |
| segmenter_overlay_text.pth | overlay_text | efficientnet-b4 | N/A | 84.0 MB |
| segmenter_repeated_text.pth | repeated_text | efficientnet-b4 | N/A | 84.0 MB |
| segmenter_tiny_corner.pth | tiny_corner | efficientnet-b4 | N/A | 84.0 MB |


## Architecture

```
Image → [Model A, B, C, D, E, F] → pixel-wise max(masks) → LaMa inpainting → Clean image
```

Each model is trained on synthetic watermarks matching its specialty.
Fusion strategy: `pixel_max` (logical OR of all masks).

## Usage

```python
import segmentation_models_pytorch as smp
import torch

model = smp.UnetPlusPlus(encoder_name="efficientnet-b4", encoder_weights=None, in_channels=3, classes=1)
state_dict = torch.load("segmenter_repeated_text.pth", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()
```

## License

Apache-2.0

## Author

[DevynLabs](https://devynlabs.com) — AI tools for creators.