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- W3120129399 abstract "Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPs count. While FLOPs are often seen as a proxy for network efficiency, when measuring actual GPU training and inference throughput, vanilla ResNet50 is usually significantly faster than its recent competitors, offering better throughput-accuracy trade-off. In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency. We first demonstrate and discuss the bottlenecks induced by FLOPs oriented optimizations. We then suggest alternative designs that better utilize GPU structure and assets. Finally, we introduce a new family of GPU-dedicated models, called TResNet, which achieves better accuracy and efficiency than previous ConvNets <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> . Using a TResNet model, with similar GPU throughput to ResNet50, we reach 80.8% top-1 accuracy on ImageNet. Our TResNet models also transfer well and achieve state-of-the-art accuracy on competitive single-label classification datasets such as Stanford Cars (96.0%), CIFAR-10 (99.0%), CIFAR-100 (91.5%) and Oxford-Flowers (99.1%). TResNet models also achieve state-of-the-art results on a multi-label classification task, and perform well on object detection." @default.
- W3120129399 created "2021-01-18" @default.
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- W3120129399 date "2021-01-01" @default.
- W3120129399 modified "2023-10-16" @default.
- W3120129399 title "TResNet: High Performance GPU-Dedicated Architecture" @default.
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- W3120129399 doi "https://doi.org/10.1109/wacv48630.2021.00144" @default.
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