SwiftFormer (swiftformer xs) Model description 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. 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. Intended uses & limitations How to use import requests from PIL import Image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) from transformers import ViTImageProcessor processor = ViTImageProcessor.from pretrained('shehan97/swiftformer xs') inputs = processor(images=image, return ten…
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