SwiftFormer-XS
| Property | Value |
|---|---|
| Developer | MBZUAI |
| Training Data | ImageNet-1K |
| Model Type | Vision Transformer |
| Performance | 78.5% Top-1 Accuracy |
What is swiftformer-xs?
SwiftFormer-XS is an innovative vision transformer model that introduces a novel efficient additive attention mechanism, specifically designed for mobile vision applications. Developed by researchers at MBZUAI, it represents a significant advancement in balancing model accuracy with mobile inference speed.
Implementation Details
The model's architecture replaces traditional quadratic matrix multiplication operations in self-attention computation with linear element-wise multiplications, resulting in significantly improved efficiency. This makes it particularly suitable for mobile deployments, achieving impressive performance metrics on devices like the iPhone 14.
- Efficient additive attention mechanism
- Optimized for mobile deployment
- 2x faster than MobileViT-v2
- Achieves 78.5% top-1 accuracy on ImageNet-1K
Core Capabilities
- Image classification tasks
- Real-time mobile vision applications
- Efficient processing with minimal latency (0.8ms on iPhone 14)
- High accuracy while maintaining speed
Frequently Asked Questions
Q: What makes this model unique?
SwiftFormer-XS stands out due to its novel additive attention mechanism that significantly reduces computational complexity while maintaining high accuracy. It achieves better performance than existing mobile-oriented models while being twice as fast.
Q: What are the recommended use cases?
The model is ideal for mobile vision applications requiring real-time processing, such as on-device image classification, mobile AI applications, and scenarios where both speed and accuracy are crucial.
