AIorNot-SigLIP2
AIorNot-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 is generated by AI or is a real photograph using the SiglipForImageClassification architecture.
[!note] 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
Real 0.9215 0.8842 0.9025 8288
AI 0.9100 0.9396 0.9246 10330
accuracy 0.9149 18618
macro avg 0.9158 0.9119 0.9135 18618
weighted avg 0.9151 0.9149 0.9147 18618
Label Space: 2 Classes
The model classifies an image as either:
Class 0: Real
Class 1: AI
Install Dependencies
pip install -q transformers torch pillow gradio hf_xet
Inference Code
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/AIorNot-SigLIP2" # Replace with your model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "Real",
"1": "AI"
}
def classify_image(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_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="AI or Real Detection"),
title="AIorNot-SigLIP2",
description="Upload an image to classify whether it is AI-generated or Real."
)
if __name__ == "__main__":
iface.launch()
Intended Use
AIorNot-SigLIP2 is useful in scenarios such as:
- AI Content Detection – Identify AI-generated images for social platforms or media verification.
- Digital Media Forensics – Assist in distinguishing synthetic from real-world imagery.
- Dataset Filtering – Clean datasets by separating real photographs from AI-synthesized ones.
- Research & Development – Benchmark performance of image authenticity detectors.