Update README.md

README.md

@@ -1,3 +1,95 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Deepfake
+- Quality
+- Classifier
+- SigLIP2
+---
+![14.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/IgM9DtsT7Pn8IfELuXmta.png)
+
+# **Deepfake-Quality-Classifier2-SigLIP2**
+> **Deepfake-Quality-Classifier2-SigLIP2** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to assess the quality of deepfake images using the **SiglipForImageClassification** architecture.  
+
+
+```python
+Classification Report:
+                       precision    recall  f1-score   support
+
+    Issue In Deepfake     0.8352    0.7800    0.8067      5000
+High Quality Deepfake     0.7951    0.8500    0.8217      5000
+
+             accuracy                         0.8245     10000
+            macro avg     0.8152    0.8245    0.8142     10000
+         weighted avg     0.8152    0.8245    0.8142     10000
+
+```
+
+The model categorizes images into two classes:  
+- **Class 0:** "Issue In Deepfake" – indicating that the deepfake image has noticeable flaws or inconsistencies.  
+- **Class 1:** "High Quality Deepfake" – indicating that the deepfake image is of high quality and appears more realistic.
+
+# **Run with Transformers🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Deepfake-Quality-Classifier2-SigLIP2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def deepfake_detection(image):
+    """Predicts deepfake probability scores for an 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()
+    
+    labels = {"0": "Issue In Deepfake", "1": "High Quality Deepfake"}
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=deepfake_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Deepfake Quality Detection",
+    description="Upload an image to check its deepfake probability scores."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Deepfake-Quality-Classifier2-SigLIP2** model is designed to evaluate the quality of deepfake images. It helps distinguish between high-quality deepfakes and those with noticeable issues. Potential use cases include:  
+
+- **Deepfake Quality Assessment:** Identifying whether a generated deepfake meets high-quality standards or contains artifacts and inconsistencies.  
+- **Content Moderation:** Assisting in filtering low-quality deepfake images in digital media platforms.  
+- **Forensic Analysis:** Supporting researchers and analysts in assessing the credibility of synthetic images.  
+- **Deepfake Model Benchmarking:** Helping developers compare and improve deepfake generation models.
+
+