| Property | Value |
|---|---|
| Parameters | 28.3M |
| GMACs | 4.6 |
| Activations | 19.1M |
| Input Resolution | 224x224 |
| Training Dataset | ImageNet-1k |
| Paper | AutoFormerV2 |
What is swin_s3_tiny_224.ms_in1k?
This model is a specialized variant of the Swin Transformer architecture, optimized through the S3 (Searching the Search Space) methodology. It represents a careful balance between computational efficiency and performance, designed specifically for computer vision tasks with 224x224 pixel inputs.
Implementation Details
The model implements a hierarchical vision transformer architecture using shifted windows, combining the benefits of the original Swin Transformer with automated architecture search principles from AutoFormerV2. With 28.3M parameters and 4.6 GMACs, it offers an efficient solution for image classification and feature extraction tasks.
- Hierarchical feature representation
- Shifted window-based self-attention
- Optimized architecture through S3 search strategy
- Balanced efficiency-performance trade-off
Core Capabilities
- Image classification on ImageNet-1k dataset
- Feature map extraction with multiple resolution levels
- Image embedding generation
- Support for both NCHW and NHWC output formats
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines the Swin Transformer architecture with S3 optimization, resulting in a compact yet powerful model that maintains high performance while requiring only 4.6 GMACs of computation.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, feature extraction, and as a backbone for downstream computer vision tasks. It's particularly effective when working with standard 224x224 resolution images and when computational efficiency is a priority.