Create README.md

README.md

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+---
+tags:
+- image-classification
+- vision
+- vit
+- deepfake
+- binary-classification
+pipeline_tag: image-classification
+language: en
+license: apache-2.0
+---
+
+# 🧠 Model1-v1-Rival — Deepfake Image Classifier
+
+This model is a fine-tuned **Vision Transformer (ViT)** for detecting whether a face image is **REAL** or **FAKE (Deepfake)**.
+
+It was trained using a mixed deepfake dataset with augmentations to ensure robustness across manipulation methods and compression artifacts.
+
+---
+
+## 📌 Model Details
+
+| Field | Value |
+|-------|-------|
+| Base Model | `google/vit-base-patch16-224-in21k` |
+| Task | Image Classification (Binary) |
+| Labels | `{0: Fake, 1: Real}` |
+| File Format | `safetensors` |
+| Optimizer | AdamW |
+| Epochs | 2 |
+| Learning Rate | `1e-6` |
+| Batch Size | 32 |
+
+---
+
+## 🏷️ Labels
+
+The model predicts:
+
+| Label | Meaning |
+|-------|---------|
+| `fake` | manipulated / deepfake image |
+| `real` | authentic human face |
+
+---
+
+## 🚀 Usage
+
+#### 🔧 With `transformers`
+
+```python
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch
+
+model_name = "alrivalda/Model1-v1-Rival"
+
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+img = Image.open("your_image.jpg")
+
+inputs = processor(img, return_tensors="pt")
+outputs = model(**inputs).logits
+probabilities = torch.softmax(outputs, dim=1)
+
+pred_id = torch.argmax(probabilities).item()
+label = model.config.id2label[pred_id]
+
+print("Prediction:", label)
+print("Confidence:", float(probabilities[0][pred_id]))