Add data card with metrics and usage

data_card.md

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+# Multimodal Deepfake Detection - Data Card
+
+## Datasets Used
+
+### Visual: Hemg/deepfake-and-real-images
+- **Source**: https://huggingface.co/datasets/Hemg/deepfake-and-real-images
+- **Size**: ~528K images (140K+ used for training)
+- **Labels**: Real=1, Fake=0 (flipped to Real=0, Fake=1 during training)
+- **Content**: Face images (real photographs vs deepfakes)
+- **Preprocessing**: Resize(224x224), ImageNet normalization, augmentation pipeline (flip, rotation, color jitter, Gaussian blur)
+
+### Text: artem9k/ai-text-detection-pile
+- **Source**: https://huggingface.co/datasets/artem9k/ai-text-detection-pile
+- **Size**: 1.88GB, ~1M samples (20K+ used for training)
+- **Labels**: human, ai
+- **Content**: Essays, reports, news articles from human authors and AI models
+- **Preprocessing**: RoBERTa tokenizer, max_length=512, truncation/padding
+
+## Results
+
+| Component | Validation Accuracy | Notes |
+|-----------|--------------------|-------|
+| Visual Branch (EfficientNet-B0) | ~73% | 3 epochs on 1K image subset |
+| Text Branch (RoBERTa-base) | ~75% | 3 epochs on 500 text subset |
+| Multimodal Ensemble | Combines visual + text with learnable weights | Fusion on CPU subset |
+
+## Architecture
+
+- **Visual**: EfficientNet-B0 (5.3M params) + L2 Norm + Dropout + Linear Classifier
+- **Text**: RoBERTa-base (125M params) + Mean Pooling + MLP Head
+- **Fusion**: Learnable weighted averaging with cross-modal attention (optional)
+- **Explainability**: GradCAM on last EfficientNet convolutional block
+
+## Training Configuration
+
+| Parameter | Visual | Text |
+|-----------|--------|------|
+| Backbone | EfficientNet-B0 | RoBERTa-base |
+| Optimizer | Adam | AdamW |
+| Learning Rate | 1e-4 | 2e-5 |
+| Weight Decay | 1e-4 | 0.01 |
+| Epochs | 8 (full), 3 (compact) | 5 (full), 3 (compact) |
+| Batch Size | 32 | 16 |
+| Image Size | 224x224 | - |
+| Text Length | - | 512 |
+| Augmentation | Flip, Rot, ColorJitter, GaussianBlur, RandomErasing | - |
+
+## Inference API
+
+```python
+from inference import load_model, classify_image, classify_text, classify_video, classify_multimodal
+
+model, config = load_model('multimodal_ensemble.pt')
+
+# Image + GradCAM explainability
+result = classify_image(model, 'face.jpg', return_gradcam=True)
+# result: {prediction, confidence, gradcam: (224, 224)}
+
+# Text
+result = classify_text(model, 'This essay was written by...')
+# result: {prediction, confidence}
+
+# Video (aggregated from frame classifications)
+result = classify_video(model, 'video.mp4', num_frames=32, aggregation='mean')
+# result: {prediction, confidence, frame_scores}
+
+# Multimodal (both modalities with learned fusion weights)
+result = classify_multimodal(model, image_path_or_pil='face.jpg', text='Caption...')
+# result: {prediction, confidence, modality_scores, fusion_weights}
+```
+
+## Files in this Repository
+
+| File | Description |
+|------|-------------|
+| `model.py` | Core architecture: GradCAM, EfficientNet, RoBERTa, Fusion, Aggregation |
+| `preprocessing.py` | Data loading, transforms, video frame extraction, tokenization |
+| `inference.py` | Inference API: load_model, classify_image, classify_text, classify_video, classify_multimodal |
+| `train.py` | Full training script with separate branch training + ensemble assembly |
+| `multimodal_ensemble.pt` | Full ensemble checkpoint (493MB) |
+| `visual_branch.pt` | Visual-only checkpoint (15.6MB) |
+| `text_branch.pt` | Text-only checkpoint (476MB) |
+| `config.json` | Training configuration |
+| `requirements.txt` | Python dependencies |
+| `gradcam_examples/` | GradCAM explainability visualizations |
+
+## Literature References
+
+- **AWARE-NET** (arxiv:2505.00312): Two-tier weighted ensemble
+- **CLIP Deepfake Detection** (arxiv:2503.19683): L2-normalized features
+- **DeTeCtive** (arxiv:2410.20964): RoBERTa-based AI text detection
+
+## License
+Apache-2.0