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)