FM-DLM: A new method for image classification based on the fusion of multi-level deep learning models - Guanghao Jin; Hengguang Li; Hui Du; Qingzeng Song (PLoS ONE, 2026)
Currently, deep learning models are widely used in many classification applications, but their utilization is limited by some factors. The large models can ensure classification of wide range, but they cannot be deployed to some small devices. The small models can be deployed to the small devices, but the number of labels is limited. To solve these problems, this paper proposes a classification method based on the Fusion of Multi-level Deep Learning Models (FM-DLM). We apply the Baidu-AI platform as a Level 0 model for classification of wide range samples. Then, we use the difference between Level 1 models to perform dataset prediction. Then, we can use the Level 2 models that were trained on the predicted dataset, which is to perform label classification. Finally, we use label distribution to achieve higher accuracy. The experimental results show that our method can achieve higher accuracy than the existing methods while ensuring a wide range of classification.