jihedjabnoun/vit-face-emotion-recognition

Vision Transformer for Face Emotion Recognition

Model Description

This model is a fine-tuned Vision Transformer (ViT) for facial emotion recognition. It can classify images into 7 different emotions: Anger, Disgust, Fear, Happiness, Neutral, Sadness, and Surprise.

Model Details

• —Model Type: Vision Transformer (ViT)
• —Base Model: google/vit-base-patch16-224
• —Task: Image Classification (Emotion Recognition)
• —Languages: Not applicable (image-based)
• —License: Apache 2.0

Training Data

The model was trained on a combination of four popular emotion recognition datasets:

1. 1.EmoSet: 21,420 training images, 3,780 validation images
2. 2.FER2013: 24,038 training images, 4,235 validation images
3. 3.RAF-DB: 13,038 training images, 2,301 validation images
4. 4.RAVDESS: 3,247 training images, 593 validation images

Total: 61,743 training images, 10,909 validation images

Emotion Distribution (Training Set):

• —Anger: 8,540 images
• —Disgust: 3,760 images
• —Fear: 7,661 images
• —Happiness: 14,395 images
• —Neutral: 10,114 images
• —Sadness: 10,350 images
• —Surprise: 6,923 images

Performance

The model achieves the following performance on the validation set:

• —Overall Accuracy: 0.8295
• —Macro F1-Score: 0.8251
• —Macro Precision: 0.8288
• —Macro Recall: 0.8222

Per-Emotion Performance:

Detailed Classification Report:

                precision    recall  f1-score   support

       Anger      0.827    0.799      0.813      1570
     Disgust      0.869    0.835      0.852       629
        Fear      0.786    0.743      0.764      1374
   Happiness      0.926    0.914      0.920      2579
     Neutral      0.767    0.820      0.793      1738
     Sadness      0.760    0.798      0.779      1851
    Surprise      0.867    0.846      0.856      1168

   macro avg      0.829    0.822      0.825     10909
weighted avg      0.831    0.829      0.830     10909

Usage

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

# Load model and processor
model = ViTForImageClassification.from_pretrained("jihedjabnoun/vit-face-emotion-recognition")
processor = ViTImageProcessor.from_pretrained("jihedjabnoun/vit-face-emotion-recognition")

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

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = logits.argmax().item()

# Get emotion labels
emotions = ['Anger', 'Disgust', 'Fear', 'Happiness', 'Neutral', 'Sadness', 'Surprise']
predicted_emotion = emotions[predicted_class_id]

# Get confidence scores
probabilities = torch.nn.functional.softmax(logits, dim=-1)[0]
confidence = probabilities[predicted_class_id].item()

print(f"Predicted Emotion: {predicted_emotion}")
print(f"Confidence: {confidence:.3f}")

# Show all emotion probabilities
for i, emotion in enumerate(emotions):
    prob = probabilities[i].item()
    print(f"{emotion}: {prob:.3f}")

Training Procedure

Training Hyperparameters

• —Optimizer: AdamW
• —Learning Rate: 2e-5
• —Batch Size: 16 (training), 32 (evaluation)
• —Number of Epochs: 4
• —Weight Decay: 0.01
• —Warmup Steps: 500

Hardware

• —GPU: NVIDIA Tesla T4 (or similar)
• —Training Time: Approximately 2.5 hours

Limitations

• —The model may have biases inherited from the training datasets
• —Performance may vary across different demographic groups
• —Images should be clear facial expressions for best results
• —The model expects RGB images with faces prominently featured

Ethical Considerations

• —This model should not be used for surveillance or monitoring without proper consent
• —Be aware of potential biases in emotion recognition across different demographic groups
• —Consider privacy implications when processing facial images

Citation

If you use this model, please cite:

@misc{jihed-vit-emotion-2025,
  author = {Jihed Jabnoun},
  title = {Vision Transformer for Face Emotion Recognition},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/jihedjabnoun/vit-face-emotion-recognition}}
}

Model Card Contact

For questions or feedback about this model, please contact the model author through Hugging Face.