| Property | Value |
|---|---|
| Parameter Count | 87.8M parameters |
| License | Apache 2.0 |
| Paper | View Paper |
| Author | Microsoft |
| Downloads | 29,741 |
What is swin-base-patch4-window7-224?
Swin Transformer is a state-of-the-art vision transformer model that introduces a hierarchical architecture using shifted windows. This base variant processes images at 224x224 resolution and was trained on ImageNet-1k dataset. The model's unique architecture enables efficient processing of visual information through local self-attention computation.
Implementation Details
The model employs a hierarchical feature transformation approach where image patches are progressively merged in deeper layers. It uses shifted windows to enable cross-window connections while maintaining linear computational complexity relative to image size. The patch size is 4x4 pixels with a window size of 7x7.
- Hierarchical feature map construction
- Linear computational complexity
- Shifted window-based self-attention mechanism
- Compatible with both PyTorch and TensorFlow frameworks
Core Capabilities
- Image classification across 1000 ImageNet classes
- Efficient processing of high-resolution images
- Serves as a backbone for dense recognition tasks
- Supports both classification and dense prediction tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's hierarchical architecture and shifted window approach set it apart from traditional vision transformers, enabling efficient processing of high-resolution images while maintaining linear computational complexity.
Q: What are the recommended use cases?
This model is ideal for image classification tasks and can serve as a backbone for various computer vision applications, including dense recognition tasks. It's particularly effective when working with high-resolution images and when computational efficiency is important.