Swin Transformer Tiny
| Property | Value |
|---|---|
| Parameter Count | 28.5M |
| License | MIT |
| Paper | View Paper |
| Image Size | 224 x 224 |
| GMACs | 4.5 |
What is swin_tiny_patch4_window7_224.ms_in1k?
This is a compact variant of the Swin Transformer architecture, specifically designed for computer vision tasks. It implements a hierarchical vision transformer using shifted windows, trained on the ImageNet-1k dataset. The model represents an efficient balance between computational resources and performance, making it suitable for various image classification tasks.
Implementation Details
The model operates on 224x224 pixel images, utilizing a patch size of 4 and window size of 7. It features 28.3M parameters and requires 4.5 GMACs for inference, with 17.1M activations. The architecture employs a hierarchical structure that processes image patches through shifted window-based self-attention mechanisms.
- Hierarchical feature extraction with shifted windows
- Patch-based image processing (4x4 patches)
- Efficient attention mechanism through windowing
- Optimized for 224x224 resolution images
Core Capabilities
- Image classification on ImageNet-1k dataset
- Feature extraction for downstream tasks
- Embedding generation for image processing
- Multi-scale feature map generation
Frequently Asked Questions
Q: What makes this model unique?
The model's hierarchical structure and shifted window approach make it computationally efficient while maintaining strong performance. Its compact size (28.5M parameters) makes it suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, feature extraction, and as a backbone for more complex computer vision applications. It's particularly well-suited for scenarios requiring a good balance between computational efficiency and accuracy.
