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README.md

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+---
+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.**