vit-base-uppercase-english-characters
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch 1.13.0 |
| Base Model | google/vit-base-patch16-224-in21k |
| Accuracy | 95.73% |
What is vit-base-uppercase-english-characters?
This is a specialized Vision Transformer (ViT) model fine-tuned for recognizing uppercase English characters. Built upon Google's ViT base architecture, it demonstrates impressive accuracy in character recognition tasks through transfer learning.
Implementation Details
The model utilizes the Vision Transformer architecture with a native AMP mixed precision training approach. Training was conducted over 4 epochs using the Adam optimizer with careful hyperparameter tuning, including a learning rate of 0.0002 and a linear scheduler.
- Batch sizes: 32 for training, 16 for evaluation
- Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-08)
- Training duration: 4 epochs
- Final validation loss: 0.3160
Core Capabilities
- High-accuracy uppercase English character recognition
- Efficient processing of image inputs
- Robust performance with 95.73% accuracy on evaluation set
- Suitable for character recognition tasks in various applications
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful ViT architecture with specialized training for uppercase English character recognition, achieving high accuracy while maintaining efficient processing capabilities.
Q: What are the recommended use cases?
The model is ideal for applications requiring uppercase English character recognition, such as OCR systems, document processing, and automated text extraction from images.
