Pedestrian Age Recognition Model
| Property | Value |
|---|---|
| Base Model | microsoft/beit-base-patch16-224-pt22k-ft22k |
| Final Accuracy | 80.73% |
| Framework | PyTorch 1.12.1 |
| Author | NTQAI |
| Contact | Nha Nguyen Van (nha282@gmail.com) |
What is pedestrian_age_recognition?
This is a specialized computer vision model fine-tuned for recognizing and classifying pedestrian ages. Built upon Microsoft's BEiT architecture, it achieves an impressive accuracy of 80.73% on the evaluation dataset. The model represents a significant advancement in automated age recognition systems for pedestrian analysis.
Implementation Details
The model was trained using a carefully optimized process over 10 epochs, utilizing the Adam optimizer with a learning rate of 2e-05. The training procedure showed consistent improvement, starting from 68.07% accuracy in the first epoch and reaching 80.73% in the final epoch.
- Batch size: 8 for both training and evaluation
- Learning rate scheduler: Linear
- Optimization: Adam (β1=0.9, β2=0.999, ε=1e-08)
- Training duration: 10 epochs with 20,080 total steps
Core Capabilities
- High-accuracy age recognition for pedestrians
- Robust performance with 80.73% accuracy
- Optimized for real-world applications
- Built on state-of-the-art BEiT architecture
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful BEiT architecture with specialized training for age recognition, achieving high accuracy (80.73%) through careful optimization and extensive training.
Q: What are the recommended use cases?
The model is particularly suitable for pedestrian monitoring systems, demographic analysis, and age-based analytics in public spaces. It can be integrated into larger systems for crowd analysis and demographic studies.
