Swin Transformer V2 Large
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | View Paper |
| Architecture | Vision Transformer |
| Task | Image Classification |
What is swinv2-large-patch4-window12to24-192to384-22kto1k-ft?
The Swin Transformer V2 Large is an advanced vision transformer model that represents a significant evolution in computer vision architecture. Pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k, this model operates at a resolution of 384x384 pixels and incorporates several innovative improvements over its predecessors.
Implementation Details
This model implements three major technical innovations: a residual-post-norm method with cosine attention for improved training stability, a log-spaced continuous position bias method for effective resolution adaptation, and the SimMIM self-supervised pre-training approach.
- Hierarchical feature map construction through patch merging
- Linear computational complexity due to local window-based self-attention
- Improved scaling capability for high-resolution images
Core Capabilities
- High-performance image classification across 1000 ImageNet classes
- Efficient processing of high-resolution images
- Adaptable feature extraction for various vision tasks
- Stable training characteristics for large-scale deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its innovative architecture that combines the benefits of transformer-based processing with efficient local window attention, making it particularly suitable for high-resolution image processing while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks but can serve as a backbone for various computer vision applications, including dense recognition tasks. It's particularly well-suited for scenarios requiring high-resolution image processing with computational efficiency.
