Multi-label rock ore slice classification approach based on self-training
Rock ore slice identification is a task that requires a high level of expertise.Manual identification often results in unavoidable subjective errors and is highly inefficient.Deep learning image recognition technology can effi-ciently perform rock ore slice identification,but training deep learning models requires a large amount of annotated data.Therefore,it is important to find efficient ways to utilize limited annotated data.By adopting a multi-label classifi-cation approach,a classifier can be trained on a labeled dataset,and then this classifier is used to generate pseudo-labels for a large number of unlabeled rock ore slice images.Finally,the model is retrained using the labeled training data and all the unlabeled data.The results show that the use of multi-label classification approach for identification of rock ore slice structures and minerals is feasible.Additionally,this paper employs a semi-supervised learning approach to train the model and improve the model's generalization ability without requiring a large amount of manual annota-tion.
rock ore sliceimage recognitionmulti-label classificationsemi-supervised learningclassifierdeep learn-ing model
万方数据
维普
