As a part of the Data-Centric AI Competition, we propose data-centric approaches to improve the diversity of the training samples by iterative sampling. The method itself relies strongly on the fidelity of augmented samples and the diversity of the augmentation methods. Moreover, we improve the performance further by introducing more samples for the problematic classes providing closer samples to edge cases, potentially those the model at hand misclassifies.