One of the most promising avenues to study dark matter is from its interactions with gravity. In particular, it is well known that dark matter can be studied from the effect of its substructure in strong galaxy-galaxy lensing images. However, in practice, this is a very challenging problem to solve as the lensing signature is a sub-dominant effect, relative to that from the main halo, and there are also many systematics which are hard to account for. To circumvent these issues, machine learning has been studied extensively in the context of lensing to circumvent exactly these problems. Indeed, deep learning methods have the potential to accurately identify images containing substructure accurately. Most applications of machine learning to strong lensing rely on using convolution neural networks (CNN). In this work, we study the performance of equivariant neural networks (ENN) using simulated strong galaxy-galaxy lensing images as a means to study dark matter. We find that equivariant neural networks outperform state-of-the-art CNNs in both classification and regression tasks. This suggests that ENNs may be better suited for future lensing studies.