Vehicle Recognition Benchmarks
Top-1 car classification accuracy on Stanford car dataset
| Methods | Accuracy (top-1) |
| Sighthound | 93.6% |
| Krause et al. [1] | 92.8% |
| Lin et al. [2] | 91.3% |
| Zhang et al. [3] | 88.4% |
| Xie et al. [4] | 86.3% |
| Gosselin et al. [5] | 82.7% |
Top-1 & top-5 car classification accuracy of compCar dataset
We compared our results with popular deep networks of GoogLeNet, Overfeat and AlexNet reported in [6].
| Methods | Accuracy (top1) | Accuracy (top5) |
| Sighthound Cloud | 95.88% | 99.53% |
| GoogLeNet [6] | 91.2% | 98.1% |
| Overfeat [6] | 87.9% | 96.9% |
| AlexNet [6] | 81.9% | 94.0% |
References
1. Krause, Jonathan, e.a.: Fine-grained recognition without part annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015)
2. Lin, T.Y., RoyChowdhury, A., Maji., S.: Bilinear cnn models for fine-grained visual recognition. In: Proceedings of the IEEE International Conference on Computer Vision. (2015)
3. et al., X.Z.: Embedding label structures for fine-grained feature representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2016)
4. Xie, Saining, e.a.: Hyper-class augmented and regularized deep learning for finegrained image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015)
5. Gosselin, P.H., Murray, N., Jegou, H., Perronnin., F.: Re-visiting the fisher vector for fine-grained classification. In: Pattern Recognition Letters. (2014)
6. Yang, Linjie, e.a.: A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015)
