We have used 4-fold validation to confirm that the model performs equally well on the total data, thereby minimizing the risk of overfitting. Moreover, at each fold, the auxetic materials are kept in the testing set only. Since it has been already discussed that the model will be trained on the normal class only. The model can separate the auxetic materials from the rest with an average F1 score of 0.56 and an average Precision of 0.64 over the full set. The model labels materials above a certain threshold of reconstruction error as anomalies. This threshold is decided by maximizing the F1 score.