Swin Transformer V2 Large
| Property | Value |
|---|---|
| Developer | Microsoft |
| Architecture | Swin Transformer V2 |
| Pre-training | ImageNet-21k |
| Resolution | 192x192 |
| Paper | Swin Transformer V2: Scaling Up Capacity and Resolution |
What is swinv2-large-patch4-window12-192-22k?
The Swin Transformer V2 Large is an advanced vision transformer model that implements a hierarchical architecture with local attention windows. It's designed to overcome the limitations of traditional vision transformers by incorporating efficient local processing and linear computational complexity relative to image size.
Implementation Details
This model introduces three major improvements over its predecessor: a residual-post-norm method with cosine attention for better training stability, a log-spaced continuous position bias method for effective resolution adaptation, and the SimMIM self-supervised pre-training approach to reduce dependence on labeled data.
- Hierarchical feature map construction through patch merging
- Local window-based self-attention mechanism
- Pre-trained on ImageNet-21k at 192x192 resolution
- Efficient scaling for both classification and dense recognition tasks
Core Capabilities
- Image classification across 21k ImageNet classes
- Adaptable for high-resolution downstream tasks
- Efficient processing with linear computational complexity
- Suitable for both classification and dense prediction tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive features include its hierarchical architecture, local attention windows, and innovative improvements like cosine attention and log-spaced continuous position bias, making it particularly efficient for processing high-resolution images while maintaining linear computational complexity.
Q: What are the recommended use cases?
This model is well-suited for image classification tasks and can be fine-tuned for various computer vision applications. It's particularly effective when dealing with high-resolution images and when computational efficiency is important.
