vit-Facial-Expression-Recognition
| Property | Value |
|---|---|
| Base Model | google/vit-base-patch16-224-in21k |
| Accuracy | 84.34% |
| Training Datasets | FER2013, MMI Facial Expression, AffectNet |
| Model Hub | HuggingFace |
What is vit-Facial-Expression-Recognition?
This is a Vision Transformer (ViT) model specifically fine-tuned for facial emotion recognition. It builds upon the google/vit-base-patch16-224-in21k architecture and has been trained to recognize seven distinct emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The model achieves an impressive accuracy of 84.34% on the evaluation set.
Implementation Details
The model utilizes a sophisticated training pipeline with carefully chosen hyperparameters. Training was conducted using the Adam optimizer with a learning rate of 3e-05, implementing a cosine learning rate scheduler with 1000 warmup steps. The model was trained for 3 epochs with a total batch size of 256.
- Comprehensive preprocessing pipeline including image resizing and normalization
- Data augmentation techniques including random rotations, flips, and zooms
- Gradient accumulation steps: 8
- Train/eval batch size: 32
Core Capabilities
- Multi-emotion classification across 7 distinct categories
- Robust performance with 84.34% accuracy
- Efficient processing of facial images
- Real-time emotion recognition capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its use of the Vision Transformer architecture in emotion recognition, achieving high accuracy while maintaining efficient processing. The combination of three different datasets (FER2013, MMI, and AffectNet) for training ensures robust performance across various scenarios.
Q: What are the recommended use cases?
The model is ideal for applications requiring facial emotion analysis, such as: sentiment analysis in video content, human-computer interaction systems, market research for consumer reactions, and automated mental health monitoring systems.
