Facial Emotions Image Detection
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| Model Type | Vision Transformer (ViT) |
| License | Apache 2.0 |
| Base Model | google/vit-base-patch16-224-in21k |
| Accuracy | 90.92% |
What is facial_emotions_image_detection?
This is a sophisticated emotion recognition model built on Vision Transformer architecture, capable of detecting seven distinct facial emotions with remarkable accuracy. Developed by dima806, it leverages the power of the ViT-base model to achieve state-of-the-art performance in emotion classification.
Implementation Details
The model is implemented using PyTorch and Transformers, utilizing a Vision Transformer architecture with 85.8M parameters. It processes images through 16x16 patches and employs the proven google/vit-base-patch16-224-in21k as its foundation.
- F32 tensor type for precise emotion detection
- Supports seven emotion classes: sad, disgust, angry, neutral, fear, surprise, and happy
- Achieves exceptional performance metrics, particularly for disgust (99.54% F1-score) and surprise (94.63% F1-score)
Core Capabilities
- High-precision emotion classification with 90.92% overall accuracy
- Balanced performance across all emotion categories
- Particularly strong in detecting disgust and surprise emotions
- Production-ready with Inference Endpoints support
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its exceptional balance of accuracy across different emotions, with particularly high precision for disgust (99.09%) and surprise (94.76%). It's built on a proven ViT architecture, making it both reliable and state-of-the-art.
Q: What are the recommended use cases?
This model is ideal for applications in human-computer interaction, sentiment analysis, market research, and psychological studies where accurate emotion detection from facial images is crucial. It's particularly effective in scenarios requiring distinction between subtle emotional expressions.
