基于TensorFlow的垃圾图像分类研究
Research on Garbage Image Classification Based on TensorFlow
曲明阳 1张岳1
作者信息
- 1. 山东青年政治学院,山东 济南 250103
- 折叠
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.
Abstract
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 fine-tuning 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.
关键词
TensorFlow/垃圾分类/Python/卷积神经网络/注意力机制Key words
TensorFlow/garbage classification/Python/Convolutional Neural Networks/Attention Mechanism引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据