Swin Transformer (Tiny)
| Property | Value |
|---|---|
| Parameter Count | 28.3M parameters |
| License | Apache 2.0 |
| Paper | View Paper |
| Training Data | ImageNet-1k |
| Author | Microsoft |
What is swin-tiny-patch4-window7-224?
The Swin Transformer tiny model is a hierarchical vision transformer designed for efficient image classification. This variant represents a compact implementation with 28.3M parameters, trained on ImageNet-1k at 224x224 resolution. It introduces an innovative approach to vision transformers by utilizing shifted windows for attention computation.
Implementation Details
The model employs a hierarchical structure that processes images through progressively merged patches, computing self-attention within local windows rather than globally. This approach maintains linear computational complexity relative to image size, making it more efficient than traditional vision transformers.
- Utilizes patch-based image processing with 4x4 patch size
- Features shifted window attention mechanism (window size 7)
- Supports both PyTorch and TensorFlow frameworks
- Optimized for 224x224 image resolution
Core Capabilities
- Image classification across 1000 ImageNet classes
- Efficient feature extraction with hierarchical representation
- Balanced performance and computational efficiency
- Suitable for both classification and dense prediction tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its shifted window approach, which enables efficient attention computation while maintaining hierarchical feature representation. This makes it more computationally efficient than traditional vision transformers while preserving strong performance.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, particularly when working with standard resolution images. It can serve as a backbone for various computer vision tasks, including both classification and dense prediction applications.
