Swin Transformer Large
| Property | Value |
|---|---|
| Author | Microsoft |
| Training Data | ImageNet-21k (14M images, 21,841 classes) |
| Input Resolution | 384x384 |
| Paper | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows |
What is swin-large-patch4-window12-384-in22k?
The Swin Transformer Large is a state-of-the-art vision transformer model that introduces a hierarchical architecture using shifted windows for efficient attention computation. It's designed to overcome the limitations of traditional vision transformers by implementing a more scalable approach to image processing.
Implementation Details
This model processes images by first dividing them into 4x4 patches and employs 12x12 local windows for self-attention computation. It builds hierarchical feature maps through progressive patch merging, enabling multi-scale feature representation. The model's unique characteristic is its linear computational complexity relative to image size, achieved through local window-based self-attention.
- Hierarchical feature map construction
- Shifted window-based self-attention mechanism
- Linear computational complexity
- Pre-trained on ImageNet-21k dataset
Core Capabilities
- Image classification across 21,841 classes
- General-purpose backbone for vision tasks
- Efficient processing of high-resolution images
- Adaptable for dense recognition tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its hierarchical architecture with shifted windows, allowing it to process images more efficiently than traditional vision transformers while maintaining high accuracy. It achieves linear computational complexity through localized attention computation.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks and can serve as a backbone for various computer vision applications. It's especially effective for high-resolution image processing and can be fine-tuned for specific downstream tasks.
