  Rock Classification is an essential geological problem since it provides
important formation information. However, exploration on this problem using
convolutional neural networks is not sufficient. To tackle this problem, we
propose two approaches using residual neural networks. We first adopt data
augmentation methods to enlarge our dataset. By modifying kernel sizes,
normalization methods and composition based on ResNet34, we achieve an accuracy
of 70.1% on the test dataset, with an increase of 3.5% compared to regular
Resnet34. Furthermore, using a similar backbone like BoTNet that incorporates
multihead self attention, we additionally use internal residual connections in
our model. This boosts the model's performance, achieving an accuracy of 73.7%
on the test dataset. We also explore how the number of bottleneck transformer
blocks may influence model performance. We discover that models with more than
one bottleneck transformer block may not further improve performance. Finally,
we believe that our approach can inspire future work related to this problem
and our model design can facilitate the development of new residual model
architectures.
