Traffic Sign Recognition On Tsinghua Tencent
Metrics
MAP
Results
Performance results of various models on this benchmark
Model Name | MAP | Paper Title | Repository |
|---|---|---|---|
| Hierarchical + Background Threshold Model | 0.31 | A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection | - |
| TSR-SA(without RFB-C) | 0.899 | A real-time and high-precision method for small traffic-signs recognition | |
| Sill-Net | - | Sill-Net: Feature Augmentation with Separated Illumination Representation | - |
| Hierarchical Model | 0.30 | A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection | - |
| Background Threshold Model | 0.32 | A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection | - |
| TSR-SA(with RFB-C) | 0.902 | A real-time and high-precision method for small traffic-signs recognition |
0 of 6 row(s) selected.