Man-Woman Face Image Detection Model
| Property | Value |
|---|---|
| Author | dima806 |
| Accuracy | 98.71% |
| Model URL | Hugging Face |
| Dataset Size | 102,124 images |
What is man_woman_face_image_detection?
This is a highly accurate deep learning model designed specifically for binary gender classification from facial images. Built using Vision Transformer (ViT) architecture, it achieves an impressive 98.7% accuracy in distinguishing between male and female faces, making it a reliable tool for automated gender detection applications.
Implementation Details
The model demonstrates exceptional balanced performance with nearly identical precision and recall metrics for both classes: 98.85% precision for male detection and 98.57% for female detection. It was trained and validated on a substantial dataset of over 102,000 face images, ensuring robust real-world performance.
- Precision: Man (98.85%), Woman (98.57%)
- Recall: Man (98.57%), Woman (98.85%)
- F1-Score: 98.71% for both classes
- Training Dataset: Balanced distribution with 51,062 images per class
Core Capabilities
- Binary gender classification from facial images
- High-confidence predictions with near-perfect accuracy
- Balanced performance across both gender categories
- Production-ready implementation via Hugging Face
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptionally high accuracy (98.7%) and perfectly balanced performance across gender categories, which is crucial for fair and unbiased deployment in real-world applications.
Q: What are the recommended use cases?
This model is ideal for applications requiring automated gender classification from facial images, such as demographic analysis, user experience personalization, and research applications. However, it should be used responsibly with consideration for privacy and ethical implications.
