Long-Tailed Mineral Identification Based on Multi-Stage Training

Deep learning-based mineral image recognition from macroscopic hand-specimen photographs has emerged as an important approach for mineral identification. However, the inherent imbalance in the natural distribution and reserves of minerals leads to a long-tailed distribution in mineral image datasets, resulting in significantly lower recognition accuracy for minerals with fewer images. To address this issue, we propose a multi-stage training framework that enhances the recognition performance of tail-class minerals through feature extractor adjustment, core feature learning, and classifier re-training. Feature extractor adjustment increases the logit scores of tail-class samples, core feature learning increases the sampling frequency of low-confidence instances, and classifier re-training increases the classifier’s sensitivity to tail-class instances. The multi-stage training framework improves the overall performance of mineral recognition and strengthens the model’s generalization ability. Experimental results on a dataset of 36 mineral categories demonstrate that the proposed method increases the mean accuracy of the one-third mineral species with the least amount of data from 67.47% to 80.06%, with a maximum improvement of 24.1%. Moreover, the overall mean accuracy across all 36 mineral categories increases from 77.27% to 81.27%, confirming the effectiveness of the proposed approach.