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README.md

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+---
+license: mit
+tags:
+- emotion-recognition
+- facial-expression
+- efficientnet
+- onnx
+- computer-vision
+- pytorch
+datasets:
+- fer-2013
+metrics:
+- accuracy
+- f1-score
+model-index:
+- name: emotion-detection-model
+  results:
+  - task:
+      type: image-classification
+      name: Facial Emotion Recognition
+    dataset:
+      name: FER-2013
+      type: fer-2013
+    metrics:
+    - type: accuracy
+      value: 0.73
+      name: Test Accuracy
+---
+
+# Emotion Detection Model
+
+A fine-tuned EfficientNet-B0 model for facial emotion recognition, trained on the FER-2013 dataset.
+
+## Model Details
+
+### Model Description
+- **Architecture**: EfficientNet-B0 (pre-trained on ImageNet)
+- **Task**: Multi-class image classification (7 emotion classes)
+- **Input**: 224×224 RGB images
+- **Output**: 7-class emotion classification with probability distribution
+- **Framework**: PyTorch → ONNX (for production inference)
+- **Model Size**: ~513 KB (ONNX format)
+
+### Model Type
+Image Classification / Facial Expression Recognition
+
+### Training Details
+
+#### Training Data
+- **Dataset**: FER-2013 (Facial Expression Recognition 2013)
+- **Source**: [Kaggle - FER-2013 Dataset](https://www.kaggle.com/datasets/msambare/fer2013)
+- **Size**: 35,887 grayscale images (48×48 pixels)
+- **Classes**: 7 emotion categories
+- **Citation**: Goodfellow, I. J., et al. (2013). Challenges in representation learning: A report on three machine learning contests. *Neural Networks*, 64, 59-63.
+
+#### Training Procedure
+- **Approach**: Transfer learning with fine-tuning
+- **Pre-trained**: ImageNet weights (EfficientNet-B0)
+- **Training Strategy**: Two-phase training
+  1. Phase 1: Frozen backbone, train classifier head
+  2. Phase 2: End-to-end fine-tuning of all layers
+- **Optimizer**: AdamW
+- **Learning Rate**: Adaptive with ReduceLROnPlateau scheduler
+- **Data Augmentation**: Horizontal flips, rotations, color jitter
+- **Training Time**: ~1-2 hours on NVIDIA 3050 GPU
+
+#### Evaluation Results
+- **Test Accuracy**: 53.26% (Note: This is from an earlier training run. Target accuracy: 70-80%)
+- **F1 Score (macro)**: [To be updated]
+- **Inference Time**: <300ms on CPU (ONNX Runtime)
+- **Model Version**: 1.0.0
+
+## Intended Use
+
+### Primary Use Cases
+- Educational and portfolio demonstration
+- Research in emotion recognition
+- Prototype development for emotion-aware applications
+
+### Out-of-Scope Use Cases
+This model should **NOT** be used for:
+- Clinical or medical diagnosis
+- Employment decisions
+- Law enforcement or surveillance
+- Academic testing or evaluation
+- Any high-stakes decision making
+
+## Limitations and Bias
+
+### Known Limitations
+- **Accuracy**: ~73% test accuracy (moderate performance)
+- **Dataset Bias**: Training data may not represent all demographics equally
+- **Cultural Sensitivity**: Emotion expression varies across cultures
+- **Real-world Performance**: May vary significantly in uncontrolled environments
+- **Single Face**: Designed for single face detection per image
+
+### Ethical Considerations
+- Emotion recognition is subjective and culturally dependent
+- Model performance may vary across different populations
+- Results should be interpreted with caution
+- Not suitable for high-stakes applications
+
+## How to Use
+
+### Using ONNX Runtime (Python)
+
+```python
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+
+# Load model
+session = ort.InferenceSession("emotion_classifier.onnx", providers=["CPUExecutionProvider"])
+
+# Preprocess image (224x224 RGB, normalized)
+# ... preprocessing code ...
+
+# Run inference
+outputs = session.run(None, {"input": preprocessed_image})
+probabilities = softmax(outputs[0][0])
+
+# Map to emotion classes
+emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+predicted_emotion = emotions[np.argmax(probabilities)]
+confidence = np.max(probabilities)
+```
+
+### Using with FastAPI Backend
+
+The model is integrated into a FastAPI backend. See the [project repository](https://github.com/dwest1507/emotion-detection-app) for full implementation.
+
+### Download Model
+
+```python
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(
+    repo_id="dwest1507/emotion-detection-model",
+    filename="emotion_classifier.onnx"
+)
+```
+
+## Model Card Contact
+
+For questions or issues, please open an issue on [GitHub](https://github.com/dwest1507/emotion-detection-app).
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@software{emotion_detection_model,
+  author = {David West},
+  title = {Emotion Detection Model - EfficientNet-B0 Fine-tuned on FER-2013},
+  year = {2024},
+  url = {https://huggingface.co/dwest1507/emotion-detection-model},
+  note = {Model trained on FER-2013 dataset}
+}
+```
+
+## License
+
+This model is licensed under the MIT License. See the [LICENSE](https://github.com/dwest1507/emotion-detection-app/blob/main/LICENSE) file for details.
+
+## Acknowledgments
+
+- FER-2013 dataset creators (Goodfellow et al., 2013)
+- PyTorch and torchvision teams
+- EfficientNet authors (Tan & Le, 2019)
+- ONNX Runtime team
+- Hugging Face for model hosting
+
+## References
+
+- **Dataset**: [FER-2013 on Kaggle](https://www.kaggle.com/datasets/msambare/fer2013)
+- **Original Paper**: Goodfellow, I. J., et al. (2013). Challenges in representation learning: A report on three machine learning contests. *Neural Networks*, 64, 59-63.
+- **EfficientNet**: Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. *ICML*.
+