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2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Bottleneck Transformers for Visual Recognition

   Srinivas, Lin, Parmar, Shlens, Abbeel and Vaswani · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021

   BoTNet swaps some convolutions in a ResNet for global self-attention, and the paper reports gains on instance segmentation and detection, plus strong ImageNet classification accuracy with faster compute.

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   1. UncheckedBoTNet reaches 44.4% Mask AP and 49.7% Box AP on COCO instance segmentation with Mask R-CNN, above ResNeSt's best published single-model, single-scale results.“Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt evaluated on the COCO validation set.”
   2. UncheckedA simple BoTNet adaptation for image classification reaches 84.7% top-1 accuracy on ImageNet, up to 1.64x faster than EfficientNet on TPU-v3 hardware.“Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.”