fashion-images-gender-age-vit-large-patch16-224-in21k-v3
| Property | Value |
|---|---|
| Base Model | google/vit-large-patch16-224-in21k |
| License | Apache 2.0 |
| Training Accuracy | 99.60% |
| Downloads | 14,954 |
What is fashion-images-gender-age-vit-large-patch16-224-in21k-v3?
This is a specialized Vision Transformer (ViT) model fine-tuned for analyzing fashion images to determine gender and age characteristics. Built upon Google's ViT-large architecture, it demonstrates exceptional accuracy of 99.60% on its evaluation dataset.
Implementation Details
The model utilizes a large Vision Transformer architecture with 16x16 pixel patches and has been trained using carefully selected hyperparameters including a learning rate of 2e-05 and the Adam optimizer. The training process spanned 5 epochs with both training and evaluation batch sizes of 8.
- Built on ViT-large-patch16-224-in21k architecture
- Trained using linear learning rate scheduler
- Achieves 0.0223 validation loss
- Implemented using PyTorch framework
Core Capabilities
- High-accuracy gender and age classification from fashion images
- Efficient processing of 224x224 pixel images
- Robust performance with 99.60% accuracy on validation set
- Suitable for fashion analytics and customer segmentation
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional accuracy (99.60%) in gender and age classification from fashion images, utilizing the powerful ViT architecture with carefully optimized training parameters.
Q: What are the recommended use cases?
This model is ideal for fashion retailers, e-commerce platforms, and marketing analytics teams looking to automatically categorize fashion images by gender and age groups, enabling better customer targeting and inventory management.
