Swin Large Transformer Model
| Property | Value |
|---|---|
| Parameter Count | 196.5M |
| License | MIT |
| Paper | Swin Transformer Paper |
| Image Size | 224 x 224 |
| GMACs | 34.5 |
What is swin_large_patch4_window7_224.ms_in22k_ft_in1k?
This is an advanced implementation of the Swin Transformer architecture, specifically designed for computer vision tasks. It represents a significant advancement in vision transformers, utilizing a hierarchical design with shifted windows for efficient processing. Pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k, this model achieves exceptional performance in image classification tasks.
Implementation Details
The model employs a sophisticated architecture with patch size 4 and window size 7, operating on 224x224 pixel images. It features a hierarchical structure that processes visual information at different scales, making it particularly effective at capturing both fine-grained and global image features.
- Hierarchical Feature Transformation with 196.5M parameters
- Shifted Window (Swin) attention mechanism
- Multi-scale feature processing capability
- Efficient activation size of 54.9M
Core Capabilities
- High-accuracy image classification
- Feature map extraction at multiple scales
- Image embedding generation
- Transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its hierarchical structure and shifted window approach, which enables efficient processing of high-resolution images while maintaining computational efficiency. The large parameter count (196.5M) and pre-training on ImageNet-22k provide exceptional feature extraction capabilities.
Q: What are the recommended use cases?
The model excels in image classification tasks, feature extraction, and as a backbone for various computer vision applications. It's particularly suitable for applications requiring high accuracy and detailed feature understanding, such as fine-grained classification or transfer learning tasks.
