Update README.md

README.md

@@ -18,6 +18,16 @@ tags:
 - SigLIP2
 ---
 
+# OpenSDI-SD3-SigLIP2
+
+> OpenSDI-SD3-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 a real photograph or generated using Stable Diffusion 3 (SD3), 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
+
+> [!note]
+*OpenSDI: Spotting Diffusion-Generated Images in the Open World* https://arxiv.org/pdf/2503.19653, OpenSDI SD3 SigLIP2 works best with crisp and high-quality images. Noisy images are not recommended for validation.
+
 ```py
 Classification Report:
                precision    recall  f1-score   support
@@ -31,3 +41,82 @@ SD3_Generated     0.8864    0.8458    0.8656     10000
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MKSJZ_cv6QI5FVEtkX5R6.png)
+
+---
+
+## Label Space: 2 Classes
+
+The model classifies an image as either:
+
+```
+Class 0: Real_Image
+Class 1: SD3_Generated
+```
+
+---
+
+## 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/OpenSDI-SD3-SigLIP2"  # Update with the correct model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "Real_Image",
+    "1": "SD3_Generated"
+}
+
+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="SD3 Image Detection"),
+    title="OpenSDI-SD3-SigLIP2",
+    description="Upload an image to determine whether it is a real photograph or generated by Stable Diffusion 3 (SD3)."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+OpenSDI-SD3-SigLIP2 is designed for tasks such as:
+
+* Generative Image Analysis – Identify SD3-generated images for benchmarking and quality inspection.
+* Dataset Validation – Ensure training or evaluation datasets are free from unintended generative artifacts.
+* Content Authenticity – Verify whether visual media originates from real-world photography or AI generation.
+* Digital Forensics – Aid in determining the origin of visual content in investigative scenarios.