摘要
研究如何利用TensorFlow对垃圾图像进行分类。采用卷积神经网络(CNN)作为主要方法,通过在大型数据集上进行训练和微调,实现了对不同类型垃圾图像的准确分类。研究结果表明,提出的模型在测试集上表现卓越,整体分类准确率超过90%。此外,通过对模型进行可视化分析,揭示了其对图像特征的学习方式,进一步深化了对分类过程的理解。总而言之,基于TensorFlow的深度学习方法在垃圾图像分类领域具有广泛的应用前景。
This paper explores how to use TensorFlow to classify garbage images.Using Convolutional Neural Networks(CNN)as the main method,accurate classification of different types of garbage images is achieved through training and finetuning on large datasets.The research results indicate that the proposed model performs excellently on the test set,with an overall classification accuracy of over 90%.In addition,through visual analysis of the model,its learning method for image features is revealed,further deepening the understanding of the classification process.In summary,deep learning methods based on TensorFlow have broad application prospects in the field of garbage image classification.
作者
曲明阳
张岳
QU Mingyang;ZHANG Yue(Shandong Youth University of Political Science,Ji'nan 250103,China)
出处
《现代信息科技》
2024年第5期115-119,共5页
Modern Information Technology
