vit-face-expression
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| Model Type | Vision Transformer |
| Base Model | vit-base-patch16-224-in21k |
| Accuracy | 71.16% (Test Set) |
What is vit-face-expression?
vit-face-expression is a sophisticated Vision Transformer model specifically designed for facial emotion recognition. Fine-tuned on the FER2013 dataset, this model can accurately classify seven distinct emotional expressions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. Built upon the robust vit-base-patch16-224-in21k architecture, it represents a modern approach to emotion detection using transformer-based computer vision.
Implementation Details
The model employs advanced preprocessing techniques including image resizing and normalization. During training, data augmentation methods such as random rotations, flips, and zooms were utilized to enhance model robustness. The architecture leverages the Vision Transformer's capability to process images as sequences of patches, enabling effective feature extraction for emotion recognition.
- F32 tensor type for optimal precision
- Comprehensive preprocessing pipeline
- Data augmentation for improved generalization
- 71.13% validation accuracy
Core Capabilities
- Real-time emotion classification
- Support for seven distinct emotional states
- Robust performance across different facial orientations
- Efficient inference with ONNX support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out by applying the Vision Transformer architecture to emotion recognition, achieving competitive accuracy while maintaining the benefits of transformer-based attention mechanisms. Its integration of modern deep learning techniques with emotional analysis makes it particularly valuable for real-world applications.
Q: What are the recommended use cases?
The model is well-suited for applications in human-computer interaction, sentiment analysis, customer experience monitoring, and psychological research. However, users should be aware of potential data biases and consider the model's limitations in production environments.
