abhilash88/face-emotion-detection

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

[image]

Person looking sad

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

[image]

Serious expression

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

[image]

Surprised expression

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

[image]

Concerned look

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

[image]

Neutral expression

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

[image]

Unpleasant expression

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

[image]

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

Built with ❤️ using Vision Transformers and PyTorch