README.md

---
language: en
license: mit
tags:
- facial-expression-recognition
- emotion-detection
- mental-health
- swin-transformer
- pytorch
- computer-vision
datasets:
- FER2013
metrics:
- accuracy
- f1-score
library_name: pytorch
---

# Facial Expression Recognition for Mental Health Detection

## Model Description

This model is a **Swin Transformer** fine-tuned for facial expression recognition (FER) with applications in mental health detection. It can classify facial expressions into 7 categories and provide depression risk analysis based on emotional patterns.

### Model Architecture

- **Base Model**: Swin Transformer (swin_base_patch4_window7_224)
- **Custom Classifier**: 
  - Linear layer (backbone features → 512)
  - ReLU activation
  - Dropout (p=0.6)
  - Linear layer (512 → 7 classes)

### Emotion Classes

The model predicts 7 facial expressions:
1. **Angry** 😠
2. **Disgust** 🤢
3. **Fear** 😨
4. **Happy** 😊
5. **Neutral** 😐
6. **Sad** 😢
7. **Surprise** 😲

## Training Details

### Dataset

- **Name**: FER2013 (Facial Expression Recognition 2013)
- **Size**: ~35,000 grayscale images (48x48 pixels)
- **Split**: Train/Validation/Test

### Training Configuration


- **Optimizer**: AdamW
- **Learning Rate**: 1e-4 with cosine annealing
- **Batch Size**: 32
- **Epochs**: 5
- **Image Size**: 224x224
- **Data Augmentation**: Random horizontal flip, rotation, color jitter
- **Loss Function**: Cross-Entropy Loss


## Usage

### Installation

```bash
pip install torch torchvision timm huggingface_hub
```

### Load Model

```python
import torch
import timm
from huggingface_hub import hf_hub_download

class CustomSwinTransformer(torch.nn.Module):
    def __init__(self, pretrained=True, num_classes=7):
        super(CustomSwinTransformer, self).__init__()
        self.backbone = timm.create_model('swin_base_patch4_window7_224', 
                                         pretrained=pretrained, num_classes=0)
        self.classifier = torch.nn.Sequential(
            torch.nn.Linear(self.backbone.num_features, 512),
            torch.nn.ReLU(),
            torch.nn.Dropout(p=0.6),
            torch.nn.Linear(512, num_classes)
        )

    def forward(self, x):
        x = self.backbone(x)
        return self.classifier(x)

# Download and load model
model_path = hf_hub_download(repo_id="SEARO1/FER_model", filename="best_model.pth")
model = CustomSwinTransformer(pretrained=False, num_classes=7)
model.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False)
model.eval()
```

### Inference Example

```python
from torchvision import transforms
from PIL import Image

# Prepare image
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

image = Image.open("face.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(input_tensor)
    probabilities = torch.nn.functional.softmax(output, dim=1)
    predicted_class = torch.argmax(probabilities, dim=1)

emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
print(f"Predicted Emotion: {emotions[predicted_class.item()]}")
print(f"Confidence: {probabilities[0][predicted_class].item()*100:.2f}%")
```

## Mental Health Application

This model can be used for depression risk analysis by analyzing emotional patterns:

### Depression Risk Calculation

```python
def analyze_depression_risk(emotion_probs):
    sad_score = emotion_probs[5]  # Sad
    fear_score = emotion_probs[2]  # Fear
    angry_score = emotion_probs[0]  # Angry
    happy_score = emotion_probs[3]  # Happy
    
    negative_emotions = (sad_score * 0.4 + fear_score * 0.3 + angry_score * 0.3)
    positive_emotions = happy_score
    
    depression_risk = (negative_emotions * 100) - (positive_emotions * 20)
    depression_risk = max(0, min(100, depression_risk))
    
    if depression_risk < 30:
        return "Low Risk"
    elif depression_risk < 60:
        return "Moderate Risk"
    else:
        return "High Risk"
```

⚠️ **Important**: This is an educational tool and should NOT replace professional medical advice or diagnosis.

## Performance

The model achieves competitive performance on the FER2013 dataset. See the training logs for detailed metrics.

## Limitations

- Trained on FER2013 dataset which may not represent all demographics equally
- Performance may vary with different lighting conditions, angles, and image quality
- Should not be used as the sole basis for mental health diagnosis
- Requires frontal face images for best results

## Citation

If you use this model, please cite:

```bibtex
@misc{fer-mental-health-2024,
  author = {Your Name},
  title = {Facial Expression Recognition for Mental Health Detection},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/SEARO1/FER_model}}
}
```

## License

MIT License - See LICENSE file for details

## Contact

For questions or issues, please open an issue on the model repository.

---

**Developed for educational and research purposes in mental health technology.**