Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- prithivMLmods/Deepfake-vs-Real
+language:
+- en
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Deepfake
+base_model:
+- prithivMLmods/Deepfake-Detection-Exp-02-22
+---
+# **Deepfake-Detection-Exp-02-22-ONNX**
+
+Deepfake-Detection-Exp-02-22 is a minimalist, high-quality dataset trained on a ViT-based model for image classification, distinguishing between deepfake and real images. The model is based on Google's **`google/vit-base-patch32-224-in21k`**.
+
+```bitex
+Mapping of IDs to Labels: {0: 'Deepfake', 1: 'Real'} 
+
+Mapping of Labels to IDs: {'Deepfake': 0, 'Real': 1}
+```
+
+```python        
+Classification report:
+        
+                      precision    recall  f1-score   support
+        
+            Deepfake     0.9833    0.9187    0.9499      1600
+                Real     0.9238    0.9844    0.9531      1600
+        
+            accuracy                         0.9516      3200
+           macro avg     0.9535    0.9516    0.9515      3200
+        weighted avg     0.9535    0.9516    0.9515      3200
+```
+
+
+![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/6720824b15b6282a2464fc58/-25Oh3wureg_MI4nvjh7w.png)
+
+# **Inference with Hugging Face Pipeline**
+```python
+from transformers import pipeline
+
+# Load the model
+pipe = pipeline('image-classification', model="prithivMLmods/Deepfake-Detection-Exp-02-22", device=0)
+
+# Predict on an image
+result = pipe("path_to_image.jpg")
+print(result)
+```
+
+# **Inference with PyTorch**
+```python
+from transformers import ViTForImageClassification, ViTImageProcessor
+from PIL import Image
+import torch
+
+# Load the model and processor
+model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-22")
+processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-22")
+
+# Load and preprocess the image
+image = Image.open("path_to_image.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+# Map class index to label
+label = model.config.id2label[predicted_class]
+print(f"Predicted Label: {label}")
+```
+
+# **Limitations**  
+1. **Generalization Issues** – The model may not perform well on deepfake images generated by unseen or novel deepfake techniques.  
+2. **Dataset Bias** – The training data might not cover all variations of real and fake images, leading to biased predictions.  
+3. **Resolution Constraints** – Since the model is based on `vit-base-patch32-224-in21k`, it is optimized for 224x224 image resolution, which may limit its effectiveness on high-resolution images.  
+4. **Adversarial Vulnerabilities** – The model may be susceptible to adversarial attacks designed to fool vision transformers.  
+5. **False Positives & False Negatives** – The model may occasionally misclassify real images as deepfake and vice versa, requiring human validation in critical applications.  
+
+# **Intended Use**  
+1. **Deepfake Detection** – Designed for identifying deepfake images in media, social platforms, and forensic analysis.  
+2. **Research & Development** – Useful for researchers studying deepfake detection and improving ViT-based classification models.  
+3. **Content Moderation** – Can be integrated into platforms to detect and flag manipulated images.  
+4. **Security & Forensics** – Assists in cybersecurity applications where verifying the authenticity of images is crucial.  
+5. **Educational Purposes** – Can be used in training AI practitioners and students in the field of computer vision and deepfake detection.