A Mixup-based Margin Aware and Calibration Model for Imbalanced in Soil Classification

In the process of soil image classification, the issue of class imbalance occurs, which leads to a decline in the generalization performance of the classifier due to the lack of data from minority classes. We investigated the effectiveness of Mixup through margin statistical analysis and successfully improved the deep imbalanced classification with uneven margins. Additionally, we investigated the relationship between margins and logits, and empirically discovered that uncalibrated margins exhibit a positive correlation with logits. Based on this revelation, we propose a Mixup-based margin-aware and calibration model to address the challenge of handling imbalanced soil image classification data. We conducted experiments using the Soil dataset and additionally tested the generalization capabilities of our method on the CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. The experimental results indicate that our approach achieved impressive results.