| Property | Value |
|---|---|
| Base Model | google/vit-base-patch16-224-in21k |
| Training Loss | 0.2771 |
| Framework | PyTorch 1.13.1 |
| Author | ivensamdh |
What is genderage2?
genderage2 is a fine-tuned Vision Transformer (ViT) model specifically adapted for gender and age detection tasks. Built upon Google's ViT-base architecture, this model demonstrates robust performance with a final validation loss of 0.2771 after comprehensive training.
Implementation Details
The model utilizes a sophisticated training approach with carefully selected hyperparameters. Training was conducted using the Adam optimizer with custom beta values (0.9, 0.999) and epsilon of 1e-08. The implementation leverages Native AMP for mixed precision training, optimizing both performance and resource utilization.
- Learning rate: 0.0001 with linear scheduler
- Batch sizes: 11 (training) and 8 (evaluation)
- Training duration: 8 epochs
- Seed value: 42 for reproducibility
Core Capabilities
- Gender and age detection from images
- Efficient processing with patch-based image analysis
- Stable training progression with consistent loss reduction
- Production-ready performance metrics
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized fine-tuning of the ViT architecture for gender and age detection, achieving impressive validation loss metrics while maintaining efficient processing capabilities.
Q: What are the recommended use cases?
This model is well-suited for applications requiring gender and age detection from images, such as demographic analysis, user verification systems, and content personalization platforms.