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Watermark-Detection-SigLIP2

Watermark-Detection-SigLIP2 is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for binary image classification. It is trained to detect whether an image contains a watermark or not, using the SiglipForImageClassification architecture.

Watermark detection works best with crisp and high-quality images. Noisy images are not recommended for validation.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

Classification Report:
 precision recall f1-score support

No Watermark 0.9290 0.9722 0.9501 12779
 Watermark 0.9622 0.9048 0.9326 9983

 accuracy 0.9427 22762
 macro avg 0.9456 0.9385 0.9414 22762
weighted avg 0.9435 0.9427 0.9424 22762

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Label Space: 2 Classes

The model classifies an image as either:

Class 0: "No Watermark"
Class 1: "Watermark"

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Install dependencies

pip install -q transformers torch pillow gradio

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Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Watermark-Detection-SigLIP2" # Update this if using a different path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
 "0": "No Watermark",
 "1": "Watermark"
}

def classify_watermark(image):
 image = Image.fromarray(image).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")
 
 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 
 prediction = {
 id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
 }

 return prediction

# Gradio Interface
iface = gr.Interface(
 fn=classify_watermark,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=2, label="Watermark Detection"),
 title="Watermark-Detection-SigLIP2",
 description="Upload an image to detect whether it contains a watermark."
)

if __name__ == "__main__":
 iface.launch()

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Demo Inference

Watermark

👁 Image	👁 Image	👁 Image
👁 Image	👁 Image	👁 Image
👁 Image	👁 Image	👁 Image

No Watermark

👁 Image	👁 Image	👁 Image
👁 Image	👁 Image	👁 Image
👁 Image	👁 Image	👁 Image

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Intended Use

Watermark-Detection-SigLIP2 is useful in scenarios such as:

• Content Moderation – Automatically detect watermarked content on image sharing platforms.
• Dataset Cleaning – Filter out watermarked images from training datasets.
• Copyright Enforcement – Monitor and flag usage of watermarked media.
• Digital Forensics – Support analysis of tampered or protected media assets.

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 7 items • Updated 1 day ago • 3

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