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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.

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

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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.

Base model