Swin Transformer Base (Swin-B)
| Property | Value |
|---|---|
| Parameters | 88M |
| FLOPs | 15.4G |
| License | MIT |
| ImageNet-1K Accuracy | 83.5% |
What is swin_b?
Swin-B is a hierarchical vision transformer that represents a significant advancement in computer vision architectures. It introduces the innovative concept of shifted windows, which enables efficient processing of high-resolution images while maintaining computational efficiency. The model achieves strong performance across various vision tasks, including image classification, object detection, and semantic segmentation.
Implementation Details
The model implements a hierarchical structure using shifted window-based self-attention. This architecture processes images by computing self-attention within non-overlapping windows while also allowing for cross-window connections through the shifting operation. Key technical specifications include:
- Base architecture with 88M parameters
- 15.4G FLOPs for inference
- 224x224 input resolution
- Achieves 83.5% top-1 accuracy on ImageNet-1K
Core Capabilities
- Image Classification on ImageNet
- Object Detection and Instance Segmentation on COCO
- Semantic Segmentation on ADE20K
- Adaptable for various downstream vision tasks
Frequently Asked Questions
Q: What makes this model unique?
The Swin Transformer introduces a hierarchical architecture with shifted windows, allowing it to process images more efficiently than traditional vision transformers while maintaining strong performance. Its design enables better handling of varying scales of visual elements.
Q: What are the recommended use cases?
The model is well-suited for various computer vision tasks, particularly image classification, object detection, and semantic segmentation. It's especially effective for applications requiring high accuracy and the ability to process multiple scales of visual information.
