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Traffic Sign Recognition On Gtsrb
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
| Paper Title | Repository | ||
|---|---|---|---|
| CNN with 3 Spatial Transformers | 99.71% | Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods | - |
| Sill-Net | 99.68% | Sill-Net: Feature Augmentation with Separated Illumination Representation | |
| SeqNet | 99.66% | SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning | - |
| MicronNet (fp16) | 98.9% | MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification | |
| SEER (RegNet10B) | 90.71% | Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision |
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