eb849f6604
* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
3.4 KiB
SwiftFormer
Overview
The SwiftFormer model was proposed in SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.
The abstract from the paper is the following:
Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2.
Tips:
- One can use the [ViTImageProcessor] API to prepare images for the model.
This model was contributed by shehan97. The original code can be found here.
SwiftFormerConfig
autodoc SwiftFormerConfig
SwiftFormerModel
autodoc SwiftFormerModel - forward
SwiftFormerForImageClassification
autodoc SwiftFormerForImageClassification - forward