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README.md
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language:
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- en
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library_name: transformers
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---
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```py
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Classification Report:
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precision recall f1-score support
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language:
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- en
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library_name: transformers
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base_model:
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- google/siglip2-base-patch16-224
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pipeline_tag: image-classification
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tags:
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- SigLIP2
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- AI-vs-Real
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- art
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---
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# AIorNot-SigLIP2
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> 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.
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> [!note]
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*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
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```py
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Classification Report:
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precision recall f1-score support
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---
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## Label Space: 2 Classes
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The model classifies an image as either:
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```
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Class 0: Real
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Class 1: AI
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```
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---
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## Install Dependencies
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```bash
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pip install -q transformers torch pillow gradio hf_xet
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```
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---
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## Inference Code
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```python
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import gradio as gr
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from transformers import AutoImageProcessor, SiglipForImageClassification
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from PIL import Image
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import torch
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# Load model and processor
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model_name = "prithivMLmods/AIorNot-SigLIP2" # Replace with your model path
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model = SiglipForImageClassification.from_pretrained(model_name)
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processor = AutoImageProcessor.from_pretrained(model_name)
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# Label mapping
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id2label = {
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"0": "Real",
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"1": "AI"
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}
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def classify_image(image):
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image = Image.fromarray(image).convert("RGB")
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inputs = processor(images=image, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
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prediction = {
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
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}
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return prediction
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# Gradio Interface
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iface = gr.Interface(
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fn=classify_image,
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inputs=gr.Image(type="numpy"),
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outputs=gr.Label(num_top_classes=2, label="AI or Real Detection"),
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title="AIorNot-SigLIP2",
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description="Upload an image to classify whether it is AI-generated or Real."
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)
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if __name__ == "__main__":
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iface.launch()
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```
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---
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## Intended Use
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AIorNot-SigLIP2 is useful in scenarios such as:
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* AI Content Detection – Identify AI-generated images for social platforms or media verification.
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* Digital Media Forensics – Assist in distinguishing synthetic from real-world imagery.
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* Dataset Filtering – Clean datasets by separating real photographs from AI-synthesized ones.
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* Research & Development – Benchmark performance of image authenticity detectors.
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