File size: 3,146 Bytes
f1519d9 f4e6a28 15ca218 f4e6a28 15ca218 f4e6a28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
---
license: apache-2.0
datasets:
- competitions/aiornot
language:
- en
library_name: transformers
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
tags:
- SigLIP2
- AI-vs-Real
- art
---
# 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
```py
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
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
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. |