Twins-SVT Large Model
| Property | Value |
|---|---|
| Parameter Count | 99.3M |
| GMACs | 15.1 |
| Activations | 35.1M |
| Input Resolution | 224 x 224 |
| Paper | Twins: Revisiting the Design of Spatial Attention in Vision Transformers |
What is twins_svt_large.in1k?
The twins_svt_large.in1k is a sophisticated vision transformer model that reimagines spatial attention mechanisms in vision transformers. Developed by researchers at Meituan AutoML, this model represents a significant advancement in computer vision architecture, trained on the ImageNet-1k dataset.
Implementation Details
This large-scale model features 99.3M parameters and operates at 15.1 GMACs, making it a powerful tool for image processing tasks. It processes images at 224x224 resolution and utilizes advanced spatial attention mechanisms to achieve state-of-the-art performance.
- Optimized spatial attention design
- Efficient feature extraction capabilities
- Flexible architecture supporting both classification and embedding generation
- Pre-trained on ImageNet-1k dataset
Core Capabilities
- Image Classification with high accuracy
- Feature embedding generation
- Support for both inference and feature extraction workflows
- Batch processing capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model's unique strength lies in its reimagined spatial attention mechanism, offering a balance between computational efficiency and model performance. With 99.3M parameters, it provides robust feature extraction while maintaining practical deployment capabilities.
Q: What are the recommended use cases?
The model excels in image classification tasks and can be effectively used for feature extraction in computer vision applications. It's particularly well-suited for applications requiring high-quality image understanding and classification capabilities at scale.
