vit-tiny-patch16-224
| Property | Value |
|---|---|
| Model Type | Vision Transformer (ViT) |
| Source | Converted from timm repository |
| Author | WinKawaks |
| Framework Requirements | PyTorch 2.0+ (for safetensors) |
What is vit-tiny-patch16-224?
vit-tiny-patch16-224 is a lightweight variant of the Vision Transformer architecture designed for efficient image classification. This implementation fills a gap in the available ViT models, as Google hadn't previously published the tiny variant on Hugging Face. The model processes 224x224 pixel images by dividing them into 16x16 patches, making it suitable for various computer vision tasks while maintaining computational efficiency.
Implementation Details
This model represents a converted version of the original timm repository weights, specifically adapted for compatibility with the Hugging Face ecosystem. It maintains the same usage pattern as larger ViT models while offering a more lightweight alternative.
- Input Resolution: 224x224 pixels
- Patch Size: 16x16 pixels
- Architecture: Tiny ViT variant
- Framework: Compatible with PyTorch, requires version 2.0+ for safetensors format
Core Capabilities
- Efficient image classification
- Reduced parameter count compared to larger ViT variants
- Compatible with standard ViT processing pipelines
- Suitable for resource-constrained applications
Frequently Asked Questions
Q: What makes this model unique?
This model fills an important gap in the ViT ecosystem by providing a tiny variant that wasn't previously available on Hugging Face. It offers a more lightweight alternative to larger ViT models while maintaining compatibility with standard ViT workflows.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient image classification where computational resources are limited. It's particularly suitable for deployment in environments where model size needs to be minimized without severely compromising performance.
