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.

翻译：岩石分类是地质学中的一个关键问题，因其能够提供重要的地层信息。然而，利用卷积神经网络对该问题的探索尚不充分。为解决这一问题，我们提出了两种基于残差神经网络的方法。首先采用数据增强方法扩大数据集。通过调整核尺寸、归一化方法以及基于ResNet34的结构组合，我们在测试集上达到了70.1%的准确率，较常规ResNet34提升了3.5%。此外，借鉴融合多头自注意力机制的BoTNet骨干网络，我们在模型中额外引入了内部残差连接，使模型性能进一步提升，在测试集上取得了73.7%的准确率。我们还探究了瓶颈变换器模块数量对模型性能的影响，发现使用超过一个瓶颈变换器模块并不能进一步提升性能。最后，我们相信本研究方法可为该问题的后续研究提供启发，而我们的模型设计也有助于推动新型残差模型架构的发展。