Update README.md

README.md

@@ -15,6 +15,14 @@ tags:
 - art
 - synthetic
 ---
+# open-deepfake-detection
+
+> open-deepfake-detection is a vision-language encoder model fine-tuned from `siglip2-base-patch16-512` for binary image classification. It is trained to detect whether an image is fake (AI-generated) or real using the **OpenDeepfake-Preview** dataset. The model uses the `SiglipForImageClassification` architecture.
+
+> \[!note]
+> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
+> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
+
 
 ```py
 Classification Report:
@@ -28,4 +36,98 @@ Classification Report:
 weighted avg     0.9459    0.9444    0.9444     19999
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KIQGQnaSxrY1F2TQNpRLR.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KIQGQnaSxrY1F2TQNpRLR.png)
+
+---
+
+## Label Space: 2 Classes
+
+The model classifies an image as either:
+
+```
+Class 0: Fake  
+Class 1: Real
+```
+
+---
+
+## 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/open-deepfake-detection"  # Updated model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated label mapping
+id2label = {
+    "0": "Fake",
+    "1": "Real"
+}
+
+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="Deepfake Detection"),
+    title="open-deepfake-detection",
+    description="Upload an image to detect whether it is AI-generated (Fake) or a real photograph (Real), using the OpenDeepfake-Preview dataset."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Demo Inference
+
+> [!warning]
+real
+
+![Screenshot 2025-05-20 at 14-01-01 Deepfake Detection Model.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/0HPpoJmqIhHMqPo80ZIdc.png)
+![Screenshot 2025-05-20 at 14-01-41 Deepfake Detection Model.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/fHB6TCDTHFI5wI7OBNOPZ.png)
+
+> [!warning]
+real
+
+![Screenshot 2025-05-20 at 14-04-22 Deepfake Detection Model.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/wNS6sFeGKroHlPvMyDqJe.png)
+![Screenshot 2025-05-20 at 14-08-07 Deepfake Detection Model.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/sKKph7D8MLLhnfjtatnrw.png)
+
+## Intended Use
+
+`open-deepfake-detection` is designed for:
+
+* **Deepfake Detection** – Identify AI-generated or manipulated images.
+* **Content Moderation** – Flag synthetic or fake visual content.
+* **Dataset Curation** – Remove synthetic samples from mixed datasets.
+* **Visual Authenticity Verification** – Check the integrity of visual media.
+* **Digital Forensics** – Support image source verification and traceability.