ViT-Small-Patch16-224
| Property | Value |
|---|---|
| Author | WinKawaks |
| Model Type | Vision Transformer (ViT) |
| Source | Converted from timm repository |
| Requirements | PyTorch 2.0+ (for safetensors) |
What is vit-small-patch16-224?
vit-small-patch16-224 is a lightweight implementation of the Vision Transformer architecture, specifically designed for processing 224x224 pixel images using 16x16 pixel patches. This model represents a more efficient alternative to the larger ViT-base model, making it suitable for scenarios where computational resources are limited but transformer-based vision processing is desired.
Implementation Details
The model has been carefully converted from the timm repository to ensure compatibility with the Hugging Face ecosystem. It maintains the core Vision Transformer architecture while operating at a smaller scale than its larger counterparts.
- Input Resolution: 224x224 pixels
- Patch Size: 16x16 pixels
- Model Format: Available in safetensors format
- Framework Compatibility: Requires PyTorch 2.0 or higher
Core Capabilities
- Efficient image classification and feature extraction
- Reduced computational overhead compared to ViT-base
- Maintains transformer-based vision processing benefits
- Compatible with standard image processing pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model fills a crucial gap in the available ViT models by providing a smaller, more efficient alternative to ViT-base while maintaining compatibility with the Hugging Face ecosystem. It's particularly notable as Google didn't officially publish the ViT-tiny and ViT-small checkpoints.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient image processing where computational resources are limited but the benefits of transformer-based architectures are desired. It's particularly suitable for mobile applications, edge devices, or scenarios requiring quick inference times.
