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imageretrieval-lsh's Introduction

图像检索模型

1.模型介绍

--技术：深度卷积神经网络技术、LSH局部敏感哈希算法、flask web端部署、nts细粒度分类技术

2.预训练模型

--图像分类预训练模型：https://drive.google.com/file/d/1F-eKqPRjlya5GH2HwTlLKNSPEUaxCu9H/view?usp=sharing
--图像检索预训练模型：http://cmp.felk.cvut.cz/cnnimageretrieval/data/whiten/retrieval-SfM-120k/retrieval-SfM-120k-vgg16-gem-r-rwhiten-19b204e.pth
    resnet50 http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/gl18/gl18-tl-resnet50-gem-w-83fdc30.pth

3.数据集

--生产上数据集进行迁移学习并查重

4.数据预处理

数据大小处理为（224*224）
--筛选出tiff、tif等格式文件，并解决pillow的底层问题（opencv解决conver（“RGB”）问题）
--数据分类筛选：采用nts网络进行细粒度分类

5.模型业务使用

--分类：python utils/classify.py
--特征提取：python utils/retrieval_feature.py
--图像离线检索：python utils/retrieval_index.py
--在线部署：python interface.py (采用flask框架部署，同时离线更新数据库)
          python app_test.py (测试接口)

6.指标

--map：0.93

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