Update README.md

README.md

@@ -19,14 +19,27 @@ language:
 
 # deepfake-detector-model
 
-> deepfake-detector-model is a vision-language model fine-tuned from `google/vit-base-patch16-224-in21k` for binary image classification. It is trained to detect whether an image is fake or real using the *OpenDeepfake-Preview* dataset. The model uses the `ViTForImageClassification` architecture.
+> `deepfake-detector-model` is a vision-language encoder model fine-tuned from [`siglip2-base-patch16-512`](https://huggingface.co/google/siglip-base-patch16-512) for binary deepfake image classification. It is trained to detect whether an image is real or generated using synthetic media techniques. The model uses the `SiglipForImageClassification` architecture.
 
+```py
+Classification Report:
+              precision    recall  f1-score   support
+
+        fake     0.9916    0.9971    0.9944     10000
+        real     0.9971    0.9916    0.9943      9999
+
+    accuracy                         0.9943     19999
+   macro avg     0.9944    0.9943    0.9943     19999
+weighted avg     0.9944    0.9943    0.9943     19999
+```
+
+![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-vgymp8TqWhaLiIYH0vDk.png)
 
 ---
 
 ## Label Space: 2 Classes
 
-The model classifies an image as either:
+The model classifies an image as one of the following:
 
 ```
 Class 0: fake  
@@ -47,17 +60,20 @@ pip install -q transformers torch pillow gradio hf_xet
 
 ```python
 import gradio as gr
-from transformers import ViTImageProcessor, ViTForImageClassification
+from transformers import AutoImageProcessor, SiglipForImageClassification
 from PIL import Image
 import torch
 
 # Load model and processor
-model_name = "prithivMLmods/deepfake-detector-model"
-model = ViTForImageClassification.from_pretrained(model_name)
-processor = ViTImageProcessor.from_pretrained(model_name)
+model_name = "prithivMLmods/deepfake-detector-model"  # Updated model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
 
 # Updated label mapping
-labels_list = ['fake', 'real']
+id2label = {
+    "0": "fake",
+    "1": "real"
+}
 
 def classify_image(image):
     image = Image.fromarray(image).convert("RGB")
@@ -69,7 +85,7 @@ def classify_image(image):
         probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 
     prediction = {
-        labels_list[i]: round(probs[i], 3) for i in range(len(probs))
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
     }
 
     return prediction
@@ -78,9 +94,9 @@ def classify_image(image):
 iface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="numpy"),
-    outputs=gr.Label(num_top_classes=2, label="Deepfake Detection"),
+    outputs=gr.Label(num_top_classes=2, label="Deepfake Classification"),
     title="deepfake-detector-model",
-    description="Upload an image to detect whether it is AI-generated (fake) or a real photograph (real), using the OpenDeepfake-Preview dataset."
+    description="Upload an image to classify whether it is real or fake using a deepfake detection model."
 )
 
 if __name__ == "__main__":
@@ -93,8 +109,8 @@ if __name__ == "__main__":
 
 `deepfake-detector-model` 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.
+* **Deepfake Detection** – Accurately identify fake images generated by AI.
+* **Media Authentication** – Verify the authenticity of digital visual content.
+* **Content Moderation** – Assist in filtering synthetic media in online platforms.
+* **Forensic Analysis** – Support digital forensics by detecting manipulated visual data.
+* **Security Applications** – Integrate into surveillance systems for authenticity verification.