Papers › TResNet: High Performance GPU-Dedicated Architecture
TResNet: High Performance GPU-Dedicated Architecture
Tal Ridnik, Hussam Lawen, Asaf Noy, Emanuel Ben Baruch, Gilad Sharir, Itamar Friedman
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-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 achieve better accuracy and efficiency than previous ConvNets. 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%). They also perform well on multi-label classification and object detection tasks. Implementation is available at: https://github.com/mrT23/TResNet.
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Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | Oxford 102 Flowers | TResNet-L | Accuracy | 99.1% | #6 of 25 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | TResNet-XL | Percentage correct | 99 | #22 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | TResNet-L-V2 | Percentage correct | 92.6 | #12 of 211 | Archive leaderboard | report |
| Image Classification | Flowers-102 | TResNet-L | Accuracy | 99.1% | #14 of 52 | Archive leaderboard | report |
| Image Classification | ImageNet | TResNet-XL | Number of params | 77M | #329 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | TResNet-XL | Top 1 Accuracy | 84.3% | #329 of 1060 | Archive leaderboard | report |
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Methods
Introduced by this paper: TResNet
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