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
TL;DR
This paper introduces FM-DLM, a method that combines multi-level deep learning models to improve image classification accuracy and adaptability to small devices.
Contribution
The novel contribution is the fusion of multi-level deep learning models to balance wide classification range and device compatibility.
Findings
FM-DLM achieves higher classification accuracy compared to existing methods.
The method supports a wide range of labels while being deployable on small devices.
Label distribution enhances the final classification performance.
Abstract
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…
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Taxonomy
TopicsMachine Learning and Data Classification · Text and Document Classification Technologies · Advanced Neural Network Applications