Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: video-classification
+tags:
+- cvit
+- deepfake-detection
+- video-classification
+- computer-vision
+- vision-transformer
+- binary-classification
+---
+
+# 🔍 Convolutional Vision Transformer (CViT) for Deepfake Detection
+
+The **Convolutional Vision Transformer (CViT)** is a hybrid architecture combining the powerful spatial feature extraction capabilities of CNNs with the long-range dependency modeling of Vision Transformers (ViT). This model is purpose-built for detecting deepfake videos and is trained on DFDC.
+
+---
+
+## Model Architecture
+
+### 1. Feature Learning (FL) Module - CNN Backbone
+- Composed of **17 convolutional operations**.
+- Unlike traditional VGG architectures, **FL focuses purely on feature extraction**, not classification.
+- Accepts input of size **224 × 224 × 3 (RGB image)**.
+- Outputs a **512 × 7 × 7** feature map.
+- Contains **10.8 million learnable parameters**.
+
+### 2. Vision Transformer (ViT) Module
+- Receives CNN output (**512 × 7 × 7**) as its input.
+- Converts the 7×7 patches into a **1 × 1024** sequence using linear embedding.
+- Adds **positional embeddings** of shape **(2 × 1024)**.
+- ViT Encoder uses:
+  - **Multi-Head Self Attention (MSA)** with **8 attention heads**.
+  - **MLP blocks** with:
+    - First linear layer of **2048** units.
+    - Final linear layer of **2 units** (binary classification: Fake / Real).
+    - **ReLU activation** and **Softmax** for final probabilities.
+
+---
+
+## 🧪 Experimental Results
+
+The CViT model was tested and evaluated across multiple deepfake datasets:
+
+### 📊 FaceForensics++ Accuracy
+| Dataset                               | Accuracy |
+|--------------------------------------|----------|
+| FaceForensics++ FaceSwap             | 69%      |
+| FaceForensics++ DeepFakeDetection    | 91%      |
+| FaceForensics++ Deepfake             | 93%      |
+| FaceForensics++ FaceShifter          | 46%      |
+| FaceForensics++ NeuralTextures       | 60%      |
+
+> **Note**: Poor performance on the FaceShifter dataset is attributed to the model's difficulty in learning subtle visual artifacts.
+
+---
+
+### 🧪 DFDC Evaluation
+
+| Model               | Validation | Test   |
+|---------------------|------------|--------|
+| **CViT**            | 87.25%     | **91.5%** |
+
+- **Unseen DFDC test videos**: 400
+- **Accuracy**: 91.5%
+- **AUC Score**: 0.91
+
+---
+
+### 🧪 UADFV AUC Comparison
+
+| Model         | Validation | FaceSwap | Face2Face |
+|---------------|------------|----------|-----------|
+| **CViT**      | **93.75%** | 69.69%   | 69.39%    |
+
+---
+
+## ⚙️ Training Configuration
+
+- **Loss Function**: Binary Cross Entropy (BCE)
+- **Optimizer**: Adam
+- **Learning Rate**: 1e-4  
+- **Weight Decay**: 1e-6  
+- **Batch Size**: 32
+- **Epochs**: 50
+- **Learning Rate Scheduler**: Reduces LR by factor of 0.1 every 15 epochs
+- **Normalization**:
+  - Mean: `[0.485, 0.456, 0.406]`
+  - Std: `[0.229, 0.224, 0.225]`
+
+---
+
+## 🧪 Inference Setup
+
+- **Input**: 30 normalized facial images (per video)
+- **Classification**:
+  - Uses **log loss function** to compute confidence.
+  - Output is a probability `y ∈ [0, 1]`
+    - `0 < y < 0.5`: Real
+    - `0.5 ≤ y ≤ 1`: Fake
+- Log loss penalizes:
+  - Random guesses
+  - Confident but incorrect predictions
+
+---
+
+## 🛠 Inference Example
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="mhamza-007/cvit_deepfake_detection",
+    filename="cvit2_deepfake_detection_ep_50.pth"
+)
+
+# Load model (example)
+model = torch.load(model_path, map_location='cpu')
+model.eval()