Traffic Sign Recognition On Gtsrb
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | 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 | |
| MicronNet (fp16) | 98.9% | MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification | - |
| SeqNet | 99.66% | SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning | |
| Sill-Net | 99.68% | Sill-Net: Feature Augmentation with Separated Illumination Representation | - |
| SEER (RegNet10B) | 90.71% | Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision | - |
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