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README.md

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+# Vision Transformer for Face Anti-Spoofing (CelebA Spoof PDA)
+
+This repository contains a fine-tuned **Vision Transformer (ViT-Base-Patch16-224)** model for **face anti-spoofing** on the **CelebA Spoof (PDA)** dataset.  
+The model was trained on the first 18 splits of the dataset and evaluated on splits **19–21**, following the standard CelebA Spoof partitioning strategy.
+
+---
+
+## Overview
+
+The objective of this project is to develop a robust deep learning–based system capable of distinguishing **live** from **spoofed** faces in real-world conditions.  
+The model leverages the **ViT architecture** fine-tuned on GPU-augmented CelebA Spoof data with advanced training techniques, including:
+
+- Focal Loss for class imbalance
+- Threshold optimization
+- Weighted regularization
+- Early stopping
+- Hyperparameter tuning (via W&B sweeps)
+
+---
+
+## Dataset
+
+**Dataset:** [CelebA Spoof (PDA)](https://github.com/Davidzhangyuanhan/CelebA-Spoof)
+
+- **Training splits:** 1–18  
+- **Testing splits:** 19–21  
+- **Classes:** Binary classification (Live vs Spoof)
+- **Total test samples:** 1,747  
+  - Live: 1,076  
+  - Spoof: 671  
+
+---
+
+## Data Augmentation Pipeline
+
+The augmentation process was GPU-accelerated using **Kornia** and executed on an **NVIDIA RTX A5000** (32 vCPU).  
+Augmentation was designed to improve model generalization across lighting, pose, and spoof mediums.
+
+**Augmentation strategy:**
+
+| Class | Augmentations per image | Techniques |
+|-------|--------------------------|-------------|
+| Live  | 8× | Random flip, rotation, color jitter, Gaussian blur/noise, perspective, elastic transform, sharpness adjustment |
+| Spoof | 2× | Same set, applied with lower probability |
+
+**Core augmentation methods:**
+- Heavy, medium, and light pipelines (with variable transform intensity)
+- GPU-based batch processing with Kornia
+- Normalization aligned with ViT preprocessing (`mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`)
+
+The complete augmentation logic is implemented in [`augument_data.py`](./augument_data.py):contentReference[oaicite:0]{index=0}.
+
+---
+
+## Model Architecture
+
+The base model is a **ViT-Base-Patch16-224**, initialized with pretrained ImageNet weights and fine-tuned for binary classification.  
+A custom classification head was added:
+
+```python
+LayerNorm(embed_dim) → Dropout(0.1) → Linear(512) → GELU → Dropout(0.1) → Linear(2)
+````
+
+**Model configuration:**
+
+* Patch size: 16
+* Dropout: 0.1
+* Optimizer: `AdamW`
+* Scheduler: Cosine Annealing with warm-up
+* Batch size: 128
+* Mixed precision: Enabled (AMP)
+* Early stopping and F1-based checkpointing
+
+The full training procedure is implemented in [`train_advanced.py`](./train_advanced.py).
+
+---
+
+## Training Details
+
+| Parameter              | Value                                |
+| ---------------------- | ------------------------------------ |
+| Dataset                | Augmented CelebA Spoof (Splits 1–18) |
+| Optimizer              | AdamW                                |
+| Learning Rate          | 3e-4 (swept)                         |
+| Weight Decay           | 0.05                                 |
+| Batch Size             | 128                                  |
+| Epochs                 | 50                                   |
+| Loss                   | Focal Loss (α=0.25, γ=2.0)           |
+| Early Stopping         | Patience = 10, Δ = 0.001             |
+| Threshold Optimization | Enabled                              |
+| Scheduler              | CosineAnnealingLR                    |
+| Mixed Precision        | True                                 |
+| Device                 | NVIDIA RTX A5000                     |
+
+Training and validation metrics were tracked using **Weights & Biases** for all runs.
+
+---
+
+## Testing Procedure
+
+Testing was conducted on **splits 19–21**, following the CelebA Spoof PDA protocol.
+The testing pipeline (`test.py`) evaluates the model on per-image and per-subject levels, generating:
+
+* Accuracy, F1, AUC
+* Precision, Recall, Specificity, NPV
+* FAR, FRR, and EER
+* Confusion Matrix
+* ROC Curve
+
+Results and plots are automatically exported to disk during testing.
+
+---
+
+## Results
+
+### Overall Performance
+
+| Metric       | Score      |
+| ------------ | ---------- |
+| **Accuracy** | **83.29%** |
+| **AUC-ROC**  | **0.9561** |
+| **F1-Score** | **0.8780** |
+
+### Detection Metrics
+
+| Metric          | Value  |
+| --------------- | ------ |
+| Precision (PPV) | 0.7974 |
+| Recall (TPR)    | 0.9768 |
+| Specificity     | 0.6021 |
+| NPV             | 0.9417 |
+
+### Error Rates
+
+| Metric                      | Value  |
+| --------------------------- | ------ |
+| False Acceptance Rate (FAR) | 0.3979 |
+| False Rejection Rate (FRR)  | 0.0232 |
+| Equal Error Rate (EER)      | 0.1083 |
+
+---
+
+## Confusion Matrix
+
+|                  | Predicted Spoof | Predicted Live |
+| ---------------- | --------------- | -------------- |
+| **Actual Spoof** | 404             | 267            |
+| **Actual Live**  | 25              | 1051           |

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