Guard-Against-Unsafe-Content2-Siglip2
Guard-Against-Unsafe-Content2-Siglip2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a binary classification task. It is designed to classify images as either "normal" or "nsfw" using the SiglipForImageClassification architecture.
[!WARNING] Experimental: This NSFW filter is an experimental model. Since I haven't found a better dataset to improve it, its performance may be inconsistent in some cases. I am currently looking for a better open dataset to enhance its effectiveness in a multi-label classification problem.
Classification Report:
precision recall f1-score support
normal 0.9975 0.9988 0.9981 4000
nsfw 0.9992 0.9983 0.9987 6000
accuracy 0.9985 10000
macro avg 0.9983 0.9985 0.9984 10000
weighted avg 0.9985 0.9985 0.9985 10000
The model categorizes images into 2 classes:
- Class 0: "normal"
- Class 1: "nsfw"
Run with Transformers🤗
!pip install -q transformers torch pillow gradio
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Guard-Against-Unsafe-Content2-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def nsfw_classification(image):
"""Predicts whether an image is NSFW or normal."""
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()
labels = {
"0": "normal",
"1": "nsfw"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=nsfw_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="NSFW Image Classification",
description="Upload an image to classify whether it is normal or NSFW."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
Intended Use:
The Guard-Against-Unsafe-Content2-Siglip2 model is designed to classify images as either "normal" or "nsfw". Potential use cases include:
- Content Moderation: Automatically filtering explicit images on social media, forums, and messaging apps.
- Parental Control: Ensuring children are not exposed to inappropriate content.
- Image Search Filtering: Improving search engine safety by removing NSFW images.
- Automated Image Analysis: Enhancing AI-driven applications that require content safety monitoring.