SigLIP2 Content Filters 042025 Final
Collection
Moderation, Balance, Classifiers
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7 items
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Updated
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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
The model classifies an image as either:
Class 0: "No Watermark"
Class 1: "Watermark"
pip install -q transformers torch pillow gradio
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()
Watermark
No Watermark
Watermark-Detection-SigLIP2 is useful in scenarios such as:
Base model
google/siglip2-base-patch16-224