Watermark Remover

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

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 — AI tools for creators.

segmenter_line_pattern.pth

pth · 80,1 MB · SHA-256 0268cccdec88…7edf · Hugging Face

--- 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.