基于深度学习FasterRCNN模型Restnet50 的生活垃圾智能分类(准确率达84%)-含python工程全源码

上传者: 31136513 | 上传时间: 2023-10-12 23:23:00 | 文件大小: 926KB | 文件类型: ZIP
1.项目基于FasterRCNN 模型,通过RPN 网络获取图片候选区域,以Restnet50 提取特征,实现生活垃圾的智慧分拣。 2.项目运行环境:硬件环境和Python 环境。其中FasterRCNN 对计算要求较高,有一部分是Restnet50 的卷积层。必须使用较大内存的GPU 才可以完成训练。在本项目中,用华为云提供的模型训练服务(GPU tesla P100)实现,链接:https://www.hwtelcloud.com/products/mts。 3.项目包括2个模块:5 个模块:数据预处理、数据加载、模型构建、模型保存及训练、模型加载及调用。数据下载地址: https://pan.baidu.com/s/1ZAbzYMLv0fcLFJsu64u0iw,提取码 yba3 4.准确率评估:本部分包括模型准确率和分类别准确率。数据总体准确率为 0.840 识别效果比较理想。其中面包、菜根、瓜子壳的类别准确率较低。

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