Update README.md

README.md

@@ -5,8 +5,25 @@ datasets:
 language:
 - en
 library_name: transformers
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+tags:
+- SigLIP2
+- AI-vs-Real
+- art
 ---
 
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KhTM3MJls_zbF4q2EqxUO.png)
+
+# 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
@@ -21,3 +38,81 @@ weighted avg     0.9151    0.9149    0.9147     18618
 
 ![download (2).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/AtUeLO-22whnmrN-mJZDr.png)
 
+---
+
+## 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.