Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+metrics:
+- accuracy
+base_model:
+- google/vit-base-patch16-224-in21k
+pipeline_tag: image-classification
+---
+# Deepfake Image Detection Using Fine-Tuned Vision Transformer (ViT)
+This project focuses on detecting **deepfake images** using a fine-tuned version of the pre-trained model `google/vit-base-patch16-224-in21k`. The approach leverages the power of Vision Transformers (ViT) to classify images as real or fake.
+
+## **Model Overview**
+- **Base Model**: [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
+- **Dataset**: [deepfake and real images](https://www.kaggle.com/datasets/manjilkarki/deepfake-and-real-images).
+- **Classes**: Binary classification (`Fake`, `Real`)
+- **Performance**:
+  - **Validation Accuracy**: 97%
+  - **Test Accuracy**: 92%
+
+*Figure 1: Confusion matrix for test data*
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ab80ef59559493941225/Qz4oHFhs8FQNFkf5c97Sg.png)
+
+*Figure 2: Confusion matrix for validation data*
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ab80ef59559493941225/mYtuHWMIJOVFk8uI_RlPU.png)
+
+### How to Use the Model
+
+Below is an example of how to load and use the model for deepfake classification:
+
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassificationimport torch
+import torch
+from PIL import Image
+
+# Load the image_processor and model
+image_processor = AutoImageProcessor.from_pretrained('ashish-001/deepfake-detection-using-ViT')
+model = AutoModelForImageClassification.from_pretrained('ashish-001/deepfake-detection-using-ViT')
+# Example usage
+image = Image.open('path of the image')
+inputs = image_processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+pred = torch.argmax(logits, dim=1).item()
+label = 'Real' if pred == 1 else 'Fake'
+print(f"Predicted type: {Label}")
+
+