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b/docs/zh_CN/introduction/ppshitu_application_scenarios.md @@ -8,9 +8,11 @@ - [1. 应用场景介绍](#1-应用场景介绍) - [2. 使用说明](#2-使用说明) - - [2.1 下载、解压场景库数据](#21-下载解压场景库数据) - - [2.2 准备识别模型](#22-准备识别模型) - + - [2.1 环境配置](#21-环境配置) + - [2.2 下载、解压场景库数据](#22-下载解压场景库数据) + - [2.3 准备模型](#23-准备模型) + - [2.4 场景库识别与检索](#24-场景库识别与检索) + - [2.4.1 识别单张图像](#241-识别单张图像) ## 1. 应用场景介绍 @@ -19,28 +21,28 @@ PP-ShiTu应用场景介绍和下载地址如下表所示。 | 场景 |示例图|场景简介|Recall@1|场景库下载地址|原数据集下载地址| |:---:|:---:|:---:|:---:|:---:|:---:| -| 球类 | --- |各种球类识别 | 0.9769 | --- | --- | -| 狗识别 | --- | 狗细分类识别,包括69种狗的图像 | 0.9606 | --- | --- | -| 宝石 | --- | 宝石种类识别 | 0.9653 | --- | --- | -| 动物 | --- |各种动物识别 | 0.9078 | --- | --- | -| 鸟类 | --- |鸟细分类识别,包括400种鸟类各种姿态 | 0.9673 | --- | --- | -| 交通工具 | --- |车、船等交通工具粗分类识别 | 0.9307 | --- | --- | -| 花 | --- |104种花细分类识别 | 0.9788 | --- | --- | -| 运动种类 | --- |100种运动图像识别 | 0.9413 | --- | --- | -| 乐器 | --- |30种不同乐器种类识别 | 0.9467 | --- | --- | -| 宝可梦 | --- |宝可梦神奇宝贝识别 | 0.9236 | --- | --- | -| 船 | --- |船种类识别 |0.9242 | --- | --- | -| 鞋子 | --- |鞋子种类识别,包括靴子、拖鞋等 | 0.9000 | --- | --- | -| 巴黎建筑 | --- |巴黎著名建筑景点识别,如:巴黎铁塔、圣母院等 | 1.000 | --- | --- | -| 蝴蝶 | --- |75种蝴蝶细分类识别 | 0.9360 | --- | --- | -| 野外植物 | --- |野外植物识别 | 0.9758 | --- | --- | -| 天气 | --- |各种天气场景识别,如:雨天、打雷、下雪等 | 0.9924 | --- | --- | -| 坚果 | --- |各种坚果种类识别 | 0.9412 | --- | --- | -| 时装 | --- |首饰、挎包、化妆品等时尚商品识别 | 0.9555 | --- | --- | -| 垃圾 | --- |12种垃圾分类识别 | 0.9845 | --- | --- | -| 航拍场景 | --- |各种航拍场景识别,如机场、火车站等 | 0.9797 | --- | --- | -| 蔬菜 | --- |各种蔬菜识别 | 0.8929 | --- | --- | -| 商标 | --- |两千多种logo识别 | 0.9313 | --- | --- | +| 球类 | --- |各种球类识别 | 0.9769 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/balls-image-classification) | +| 狗识别 | --- | 狗细分类识别,包括69种狗的图像 | 0.9606 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/70-dog-breedsimage-data-set) | +| 宝石 | --- | 宝石种类识别 | 0.9653 | --- | [原数据下载地址](https://www.kaggle.com/datasets/lsind18/gemstones-images) | +| 动物 | --- |各种动物识别 | 0.9078 | --- | [原数据下载地址](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) | +| 鸟类 | --- |鸟细分类识别,包括400种鸟类各种姿态 | 0.9673 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) | +| 交通工具 | --- |车、船等交通工具粗分类识别 | 0.9307 | --- | [原数据下载地址](https://www.kaggle.com/datasets/rishabkoul1/vechicle-dataset) | +| 花 | --- |104种花细分类识别 | 0.9788 | --- | [原数据下载地址](https://www.kaggle.com/datasets/msheriey/104-flowers-garden-of-eden) | +| 运动种类 | --- |100种运动图像识别 | 0.9413 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/sports-classification) | +| 乐器 | --- |30种不同乐器种类识别 | 0.9467 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/musical-instruments-image-classification) | +| 宝可梦 | --- |宝可梦神奇宝贝识别 | 0.9236 | --- | [原数据下载地址](https://www.kaggle.com/datasets/lantian773030/pokemonclassification) | +| 船 | --- |船种类识别 |0.9242 | --- | [原数据下载地址](https://www.kaggle.com/datasets/imsparsh/dockship-boat-type-classification) | +| 鞋子 | --- |鞋子种类识别,包括靴子、拖鞋等 | 0.9000 | --- | [原数据下载地址](https://www.kaggle.com/datasets/noobyogi0100/shoe-dataset) | +| 巴黎建筑 | --- |巴黎著名建筑景点识别,如:巴黎铁塔、圣母院等 | 1.000 | --- | [原数据下载地址](https://www.kaggle.com/datasets/skylord/oxbuildings) | +| 蝴蝶 | --- |75种蝴蝶细分类识别 | 0.9360 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/butterfly-images40-species) | +| 野外植物 | --- |野外植物识别 | 0.9758 | --- | [原数据下载地址](https://www.kaggle.com/datasets/ryanpartridge01/wild-edible-plants) | +| 天气 | --- |各种天气场景识别,如:雨天、打雷、下雪等 | 0.9924 | --- | [原数据下载地址](https://www.kaggle.com/datasets/jehanbhathena/weather-dataset) | +| 坚果 | --- |各种坚果种类识别 | 0.9412 | --- | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/tree-nuts-image-classification) | +| 时装 | --- |首饰、挎包、化妆品等时尚商品识别 | 0.9555 | --- | [原数据下载地址](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small) | +| 垃圾 | --- |12种垃圾分类识别 | 0.9845 | --- | [原数据下载地址](https://www.kaggle.com/datasets/mostafaabla/garbage-classification) | +| 航拍场景 | --- |各种航拍场景识别,如机场、火车站等 | 0.9797 | --- | [原数据下载地址](https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets) | +| 蔬菜 | --- |各种蔬菜识别 | 0.8929 | --- | [原数据下载地址](https://www.kaggle.com/datasets/zhaoyj688/vegfru) | +| 商标 | --- |两千多种logo识别 | 0.9313 | --- | [原数据下载地址](https://github.com/Wangjing1551/LogoDet-3K-Dataset) | @@ -48,13 +50,22 @@ PP-ShiTu应用场景介绍和下载地址如下表所示。 ## 2. 使用说明 - + + +### 2.1 环境配置 +- 安装:请先参考文档[环境准备](../installation/install_paddleclas.md)配置PaddleClas运行环境 +- 进入`deploy`运行目录,本部分所有内容与命令均需要在`deploy`目录下运行,可以通过下面命令进入`deploy`目录。 +```shell +cd deploy +``` + -### 2.1 下载、解压场景库数据 + + +### 2.2 下载、解压场景库数据 首先创建存放场景库的地址`deploy/datasets`: ```shell -cd deploy mkdir datasets ``` 下载并解压对应场景库到`deploy/datasets`中。 @@ -62,7 +73,7 @@ mkdir datasets cd datasets # 下载并解压场景库数据 -wget {场景库下载链接地址} && tar -xf {压缩包的名称} +wget {场景库下载链接} && tar -xf {压缩包的名称} ``` 以`dataset_name`为例,解压完毕后,`datasets/dataset_name`文件夹下应有如下文件结构: ```shel @@ -70,22 +81,96 @@ wget {场景库下载链接地址} && tar -xf {压缩包的名称} │ ├── gallery/ │ ├── index/ │ ├── query/ +│ ├── gallery_list.txt/ +│ ├── query_list.txt/ +│ ├── image_list.txt/ ├── ... ``` -其中,`gallery`文件夹中存放的是用于构建索引库的原始图像,`index`表示基于原始图像构建得到的索引库信息,`query`文件夹存放的是用于检索的图像列表。 +其中,`gallery`文件夹中存放的是用于构建索引库的原始图像,`index`表示基于原始图像构建得到的索引库信息,`query`文件夹存放的是用于检索的图像列表,`gallery_list.txt`和`query_list.txt`分别为索引库和检索图像的标签文件。 - + -### 2.2 准备识别模型 +### 2.3 准备模型 创建存放模型的文件夹`deploy/models`,并下载轻量级主体检测、识别模型,命令如下: ```shellc cd .. mkdir models cd models -# 下载通用检测 inference 模型并解压 -wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar +# 下载检测模型并解压 +wget {检测模型下载链接} && tar -xf {检测模型压缩包名称} # 下载识别 inference 模型并解压 -wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar && tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar +wget {{识别模型下载链接}} && tar -xf {识别模型压缩包名称} +``` + +解压完成后,`models`文件夹下有如下文件结构: +``` +├── inference_model_name +│ ├── inference.pdiparams +│ ├── inference.pdiparams.info +│ └── inference.pdmodel +├── det_model_name +│ ├── inference.pdiparams +│ ├── inference.pdiparams.info +│ └── inference.pdmodel ``` + + + +### 2.4 场景库识别与检索 + +以`动物识别`场景为例,展示识别和检索过程(如果希望尝试其他场景库的识别与检索效果,在下载解压好对应的场景库数据和模型后,替换对应的配置文件即可完成预测)。 + +注意,此部分使用了`faiss`作为检索库,安装方法如下: +```shell +pip install faiss-cpu==1.7.1post2 +``` + +若使用时,不能正常引用,则`uninstall`之后,重新`install`,尤其是在windows下。 + + + +#### 2.4.1 识别单张图像 + +假设需要测试`./datasets/AnimalImageDataset/Query/antelope/0a37838e99.jpg`这张图像识别和检索效果。 + +首先修改配置文件`./configs/inference_general.yaml`中的`Global.det_inference_model_dir`和`Global.rec_inference_model_dir`为对应的检测和识别模型文件夹,以及修改测试图像地址`Global.infer_imgs`示例如下: + +```shell +Global: + infer_imgs: './datasets/AnimalImageDataset/Query/antelope/0a37838e99.jpg' + det_inference_model_dir: './models/det_model_name' + rec_inference_model_dir: './models/inference_model_name' +``` + +并修改配置文件`./configs/inference_general.yaml`中的index库地址`IndexProcess.index_dir`为对应场景index库: + +```shell +IndexProcess: + index_dir:'./datasets/AnimalImageDataset/Index/SampleAll/index' +``` + + +运行下面的命令,对图像`./datasets/AnimalImageDataset/Query/antelope/0a37838e99.jpg`进行识别与检索 + +```shell +# 使用下面的命令使用 GPU 进行预测 +python3.7 python/predict_system.py -c configs/inference_general.yaml + +# 使用下面的命令使用 CPU 进行预测 +python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.use_gpu=False +``` + +最终输出结果如下: +``` +[{'bbox': [264, 79, 1088, 850], 'rec_docs': 'antelope', 'rec_scores': 0.81452656}] +``` +其中`bbox`表示检测出的主体所在位置,`rec_docs`表示索引库中与检测框最为相似的类别,`rec_scores`表示对应的置信度。 +检测的可视化结果也保存在`output`文件夹下,对于本张图像,识别结果可视化如下所示。 + +![](../../images/ppshitu_application_scenarios/6aa06f252d.jpg) + + + +#### 2.4.2 基于文件夹的批量识别 -- GitLab