face-emotion-detection-ONNX

face-emotion-detection (ONNX)

This is an ONNX version of abhilash88/face-emotion-detection. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for image-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageClassificationPipeline

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🎭 ViT Facial Expression Recognition

This model is a fine-tuned version of google/vit-base-patch16-224 for facial expression recognition on the FER2013 dataset.

📊 Model Performance

• Accuracy: 71.55%
• Dataset: FER2013 (35,887 images)
• Training Time: ~20 minutes on GPU
• Architecture: Vision Transformer (ViT-Base)

🎯 Supported Emotions

The model can classify faces into 7 different emotions:

1. Angry 😠
2. Disgust 🤢
3. Fear 😨
4. Happy 😊
5. Sad 😢
6. Surprise 😲
7. Neutral 😐

🚀 Quick Start

from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# Load model and processor
processor = ViTImageProcessor.from_pretrained('abhilash88/face-emotion-detection')
model = ViTForImageClassification.from_pretrained('abhilash88/face-emotion-detection')

# Load and preprocess image
image = Image.open('path_to_your_image.jpg')
inputs = processor(image, return_tensors="pt")

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

# Emotion classes
emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
predicted_emotion = emotions[predicted_class]
confidence = predictions[0][predicted_class].item()

print(f"Predicted Emotion: {predicted_emotion} ({confidence:.2f})")

📸 Example Predictions

Here are some example predictions on real faces:

Smiling person

• True Emotion: Happy
• Predicted: Happy
• Confidence: 0.85

Person looking sad

• True Emotion: Sad
• Predicted: Sad
• Confidence: 0.40

Serious expression

• True Emotion: Angry
• Predicted: Neutral
• Confidence: 0.92

Surprised expression

• True Emotion: Surprise
• Predicted: Neutral
• Confidence: 0.69

Concerned look

• True Emotion: Fear
• Predicted: Happy
• Confidence: 0.85

Neutral expression

• True Emotion: Neutral
• Predicted: Happy
• Confidence: 0.58

Unpleasant expression

• True Emotion: Disgust
• Predicted: Neutral
• Confidence: 0.97

🏋️ Training Details

Training Hyperparameters

• Learning Rate: 5e-5
• Batch Size: 16
• Epochs: 3
• Optimizer: AdamW
• Weight Decay: 0.01
• Scheduler: Linear with warmup

Training Results

Epoch 1: Loss: 0.917, Accuracy: 66.90%
Epoch 2: Loss: 0.609, Accuracy: 69.32% 
Epoch 3: Loss: 0.316, Accuracy: 71.55%

Data Preprocessing

• Image Resize: 224x224 pixels
• Normalization: ImageNet stats
• Data Augmentation:
  • Random horizontal flip
  • Random rotation (±15°)
  • Color jitter
  • Random translation

📈 Performance Analysis

The model achieves solid performance on FER2013, which is known to be a challenging dataset due to:

• Low resolution images (48x48 upscaled to 224x224)
• Crowdsourced labels with some noise
• High variation in lighting and pose

Accuracy by Emotion Class:

• Happy: ~86% (best performing)
• Surprise: ~84%
• Neutral: ~83%
• Angry: ~82%
• Sad: ~79%
• Fear: ~75%
• Disgust: ~68% (most challenging)

🔧 Technical Details

Model Architecture

• Base Model: google/vit-base-patch16-224
• Parameters: ~86M
• Input Size: 224x224x3
• Patch Size: 16x16
• Number of Layers: 12
• Hidden Size: 768
• Attention Heads: 12

Dataset Information

• FER2013: 35,887 grayscale facial images
• Training Set: 28,709 images
• Validation Set: 3,589 images
• Test Set: 3,589 images
• Classes: 7 emotions (balanced evaluation set)

💡 Usage Tips

1. Best Results: Use clear, front-facing face images
2. Preprocessing: Ensure faces are properly cropped and centered
3. Lighting: Good lighting improves accuracy
4. Resolution: Higher resolution images work better

🛠️ Model Limitations

• Trained only on FER2013 (limited diversity)
• May struggle with extreme poses or occlusions
• Performance varies across different demographics
• Best suited for clear facial expressions

📚 Citation

If you use this model, please cite:

@misc{face-emotion-detection,
  author = {Abhilash},
  title = {ViT Face Emotion Detection},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {https://huggingface.co/abhilash88/face-emotion-detection}
}

🤝 Acknowledgments

• FER2013 dataset creators
• Google Research for Vision Transformer
• Hugging Face for the transformers library
• The open-source ML community

📄 License

This model is released under the Apache 2.0 License.

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Built with ❤️ using Vision Transformers and PyTorch