Swin Transformer V2 (Tiny)
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | View Paper |
| Training Data | ImageNet-1K |
| Input Resolution | 256x256 |
What is swinv2-tiny-patch4-window16-256?
The Swin Transformer V2 Tiny is a compact vision transformer model designed for efficient image classification tasks. It represents Microsoft's evolution of the original Swin architecture, incorporating significant improvements in training stability and transfer learning capabilities. The model processes 256x256 pixel images using a hierarchical feature extraction approach with local self-attention mechanisms.
Implementation Details
This implementation features a sophisticated architecture that divides images into 4x4 patches and utilizes 16x16 local attention windows. The model incorporates three major improvements over its predecessor:
- Residual-post-norm method with cosine attention for enhanced training stability
- Log-spaced continuous position bias for effective resolution adaptation
- SimMIM self-supervised pre-training methodology
Core Capabilities
- Image classification across 1000 ImageNet classes
- Efficient processing with linear computational complexity
- Hierarchical feature map generation
- Effective handling of both low and high-resolution inputs
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that combines the benefits of transformers with local attention mechanisms, making it computationally efficient while maintaining strong performance. The tiny variant is particularly suitable for applications where computational resources are limited.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks and can serve as a backbone for various computer vision applications. It's particularly well-suited for scenarios requiring efficient processing of standard resolution images (256x256) while maintaining good accuracy.
