  We investigate whether ResNet architectures can outperform more traditional
Convolutional Neural Networks on the task of fine-grained vehicle
classification. We train and test ResNet-18, ResNet-34 and ResNet-50 on the
Comprehensive Cars dataset without pre-training on other datasets. We then
modify the networks to use Spatially Weighted Pooling. Finally, we add a
localisation step before the classification process, using a network based on
ResNet-50. We find that using Spatially Weighted Pooling and localisation both
improve classification accuracy of ResNet50. Spatially Weighted Pooling
increases accuracy by 1.5 percent points and localisation increases accuracy by
3.4 percent points. Using both increases accuracy by 3.7 percent points giving
a top-1 accuracy of 96.351\% on the Comprehensive Cars dataset. Our method
achieves higher accuracy than a range of methods including those that use
traditional CNNs. However, our method does not perform quite as well as
pre-trained networks that use Spatially Weighted Pooling.
