  The training datasets used in long-tailed recognition are extremely
unbalanced, resulting in significant variation in per-class accuracy across
categories. Prior works mostly used average accuracy to evaluate their
algorithms, which easily ignores those worst-performing categories. In this
paper, we aim to enhance the accuracy of the worst-performing categories and
utilize the harmonic mean and geometric mean to assess the model's performance.
We revive the balanced undersampling idea to achieve this goal. In few-shot
learning, balanced subsets are few-shot and will surely under-fit, hence it is
not used in modern long-tailed learning. But, we find that it produces a more
equitable distribution of accuracy across categories with much higher harmonic
and geometric mean accuracy, and, but lower average accuracy. Moreover, we
devise a straightforward model ensemble strategy, which does not result in any
additional overhead and achieves improved harmonic and geometric mean while
keeping the average accuracy almost intact when compared to state-of-the-art
long-tailed learning methods. We validate the effectiveness of our approach on
widely utilized benchmark datasets for long-tailed learning. Our code is at
\href{https://github.com/yuhao318/BTM/}{https://github.com/yuhao318/BTM/}.
