Neural Machine Translation (NMT) for low-resource languages remains a challenge for many NLP researchers. In this work, we deploy a standard data augmentation methodology by back-translation to a new language translation direction, i.e., Cantonese-to-English. We present the models we fine-tuned using the limited amount of real data and the synthetic data we generated using back-translation by three models: OpusMT, NLLB, and mBART.We carried out automatic evaluation using a range of different metrics including those that are lexical-based and embedding-based.Furthermore, we create a user-friendly interface for the models we included in this project, CantonMT, and make it available to facilitate Cantonese-to-English MT research. Researchers can add more models to this platform via our open-source CantonMT toolkit, available at https://github.com/kenrickkung/CantoneseTranslation.