| Property | Value |
|---|---|
| Parameter Count | 88.1M parameters |
| Model Type | Image Classification / Feature Backbone |
| Architecture | Swin Transformer |
| License | MIT |
| Paper | Swin Transformer Paper |
| Dataset | ImageNet-22k (pretrain), ImageNet-1k (fine-tune) |
What is swin_base_patch4_window7_224.ms_in22k_ft_in1k?
This is a sophisticated vision transformer model that implements the Swin (Shifted Window) architecture, specially designed for computer vision tasks. Pre-trained on the extensive ImageNet-22k dataset and fine-tuned on ImageNet-1k, it offers state-of-the-art performance for image classification and feature extraction tasks.
Implementation Details
The model employs a hierarchical structure with shifted windows, processing images at 224x224 resolution. It features 15.5 GMACs computational complexity and 36.6M activations, making it efficient for production deployment while maintaining high accuracy.
- Patch size: 4x4 pixels
- Window size: 7x7
- Hierarchical feature extraction capabilities
- Supports both classification and backbone functionalities
Core Capabilities
- Image Classification with 1000 classes
- Feature Map Extraction at multiple scales
- Image Embedding Generation
- Support for both training and inference modes
Frequently Asked Questions
Q: What makes this model unique?
The model combines hierarchical feature representation with shifted window-based self-attention, offering an optimal balance between computational efficiency and model performance. Its pre-training on ImageNet-22k followed by ImageNet-1k fine-tuning provides robust feature extraction capabilities.
Q: What are the recommended use cases?
This model excels in image classification tasks, feature extraction for downstream tasks, and as a backbone for complex computer vision applications. It's particularly suitable for applications requiring hierarchical feature understanding and those dealing with high-resolution images.