Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a hybrid vision transformer architecture that obtains the state-of-the-art latency-accuracy trade-off. To this end, we introduce a novel token mixing operator, RepMixer, a building block of FastViT, that uses structural reparameterization to lowe ...
r the memory access cost by removing skip-connections in the network. We further apply traintime overparametrization and large kernel convolutions to boost accuracy and empirically show that these choices have minimal effect on latency. We show that - our model is 3.5× faster than CMT, a recent state-of-the-art hybrid transformer architecture, 4.9× faster than EfficientNet, and 1.9× faster than ConvNeXt on a mobile device for the same accuracy on the ImageNet dataset. At similar latency, our model obtains 4.2% better Top-1 accuracy on ImageNet than MobileOne. Our model consistently outperforms competing architectures across several tasks - image classification, detection, segmentation and 3D mesh regression with significant improvement in latency on both a mobile device and a desktop GPU. Furthermore, our model is highly robust to out-of-distribution samples and corruptions, improving over competing robust models. Code and models are available at https://github.com/apple/ml-fastvit
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The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models.
huggingface/transformers.js is the closest maintained adjacent implementation (Matches contextual method/domain keyword: transformer). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 16108 GitHub stars.
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Evidence graph: 3 refs, 3 links.
Utility signals: depth 100/100, grounding 85/100, status high.
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Strong overlap with paper title keywords · Community adoption signal (2011 stars)
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Modern transformer training baseline.
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Matches contextual method/domain keyword: transformer
Matches contextual method/domain keyword: transformer
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99
Citations
83
References
Tasks
Computer science, FLOPS, Mobile device, Computer engineering, Physical Sciences
Methods
Transformer, Architecture
Domains
Latency (audio), Security token, Artificial intelligence, Computer Vision and Pattern Recognition
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