Swin-base-patch4-window7-224-in22k
| Property | Value |
|---|---|
| Parameter Count | 109M |
| License | Apache 2.0 |
| Training Data | ImageNet-21k (14M images, 21,841 classes) |
| Paper | Swin Transformer Paper |
What is swin-base-patch4-window7-224-in22k?
The Swin Transformer is an advanced vision transformer model developed by Microsoft that introduces a hierarchical architecture using shifted windows. This base variant features 109M parameters and was pre-trained on the massive ImageNet-21k dataset, containing 14 million images across 21,841 classes.
Implementation Details
This model implements a unique hierarchical feature extraction approach, processing images at 224x224 resolution using 4x4 patch sizes and 7x7 window sizes. Unlike traditional vision transformers, it maintains linear computational complexity through localized self-attention within shifted windows.
- Hierarchical feature map construction through patch merging
- Shifted window-based self-attention mechanism
- Linear complexity relative to input image size
- Supports both PyTorch and TensorFlow frameworks
Core Capabilities
- High-performance image classification
- Efficient processing of high-resolution images
- Adaptable for dense prediction tasks
- Seamless integration with modern deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its shifted window approach, which enables efficient processing of high-resolution images while maintaining linear computational complexity. Unlike traditional vision transformers, it creates hierarchical feature maps, making it suitable for both classification and dense prediction tasks.
Q: What are the recommended use cases?
The model excels in image classification tasks and can be fine-tuned for various computer vision applications. It's particularly effective for scenarios requiring high-resolution image processing or where computational efficiency is crucial.
