Skip to content

P
PaddleClas

PaddlePaddle / PaddleClas
1 年多 前同步成功

通知 116
Star 4999
Fork 1114
P
PaddleClas
提交 b0775e6e 编写于 作者: 悟、's avatar 悟、
浏览文件
操作

add links

上级 fd85021e
隐藏空白更改
内联 并排
Showing with 46 addition and 46 deletion
docs/zh_CN/introduction/ppshitu_application_scenarios.md
浏览文件 @ b0775e6e
......@@ -14,40 +14,38 @@
- [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)
- [2.4.2 基于文件夹的批量识别](#242-基于文件夹的批量识别)
- [3 新增场景库图像识别体验](#3-新增场景库图像识别体验)
- [3.1 根据相似度划分Gallery和Query](#31-根据相似度划分gallery和query)
- [3.2 根据划分好的Gallery构建index索引](#32-根据划分好的gallery构建index索引)
<a name="1. 应用场景介绍"></a>
## 1. 应用场景介绍
PP-ShiTu应用场景介绍和下载地址如下表所示。
PP-ShiTu对原数据集进行了`Gallery`库和`Query`库划分，并生成了对应的`Index`索引库，具体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 | <img src="../../images/ppshitu_application_scenarios/Ball.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/DogBreeds.jpg" height = "100"> | 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 | <img src="../../images/ppshitu_application_scenarios/Gemstones.jpg" height = "100"> | 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 | <img src="../../images/ppshitu_application_scenarios/AnimalImageDataset.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Bird400.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Vechicles.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/104flowers.jpeg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/100sports.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/MusicInstruments.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Pokemon.png" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Boat.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Shoes.jpeg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Paris.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Butterfly.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/WildEdiblePlants.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/WeatherImageRecognition.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/TreeNuts.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/FashionProductsImage.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Garbage12.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/AID.jpg" height = "100"> |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 | <img src="../../images/ppshitu_application_scenarios/Veg200.jpg" height = "100"> |各种蔬菜识别 | 0.8929 | --- | [原数据下载地址](https://www.kaggle.com/datasets/zhaoyj688/vegfru) |
| 商标 | <img src="../../images/ppshitu_application_scenarios/Logo3K.jpg" height = "100"> |两千多种logo识别 | 0.9313 | --- | [原数据下载地址](https://github.com/Wangjing1551/LogoDet-3K-Dataset) |
| 球类 | <img src="../../images/ppshitu_application_scenarios/Ball.jpg" height = "100"> |各种球类识别 | 0.9769 | [Balls](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Balls.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/balls-image-classification) |
| 狗识别 | <img src="../../images/ppshitu_application_scenarios/DogBreeds.jpg" height = "100"> | 狗细分类识别，包括69种狗的图像 | 0.9606 | [DogBreeds](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/DogBreeds.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/70-dog-breedsimage-data-set) |
| 宝石 | <img src="../../images/ppshitu_application_scenarios/Gemstones.jpg" height = "100"> | 宝石种类识别 | 0.9653 | [Gemstones](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Gemstones.tar) | [原数据下载地址](https://www.kaggle.com/datasets/lsind18/gemstones-images) |
| 动物 | <img src="../../images/ppshitu_application_scenarios/AnimalImageDataset.jpg" height = "100"> |各种动物识别 | 0.9078 | [AnimalImageDataset](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/AnimalImageDataset.tar) | [原数据下载地址](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) |
| 鸟类 | <img src="../../images/ppshitu_application_scenarios/Bird400.jpg" height = "100"> |鸟细分类识别，包括400种各种姿态的鸟类图像 | 0.9673 | [Bird400](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Bird400.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) |
| 交通工具 | <img src="../../images/ppshitu_application_scenarios/Vechicles.jpg" height = "100"> |车、船等交通工具粗分类识别 | 0.9307 | [Vechicles](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Vechicles.tar) | [原数据下载地址](https://www.kaggle.com/datasets/rishabkoul1/vechicle-dataset) |
| 花 | <img src="../../images/ppshitu_application_scenarios/104flowers.jpeg" height = "100"> |104种花细分类识别 | 0.9788 | [104flowers](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/104flowrs.tar) | [原数据下载地址](https://www.kaggle.com/datasets/msheriey/104-flowers-garden-of-eden) |
| 运动种类 | <img src="../../images/ppshitu_application_scenarios/100sports.jpg" height = "100"> |100种运动图像识别 | 0.9413 | [100sports](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/100sports.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/sports-classification) |
| 乐器 | <img src="../../images/ppshitu_application_scenarios/MusicInstruments.jpg" height = "100"> |30种不同乐器种类识别 | 0.9467 | [MusicInstruments](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/MusicInstruments.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/musical-instruments-image-classification) |
| 宝可梦 | <img src="../../images/ppshitu_application_scenarios/Pokemon.png" height = "100"> |宝可梦神奇宝贝识别 | 0.9236 | [Pokemon](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Pokemon.tar) | [原数据下载地址](https://www.kaggle.com/datasets/lantian773030/pokemonclassification) |
| 船 | <img src="../../images/ppshitu_application_scenarios/Boat.jpg" height = "100"> |船种类识别 |0.9242 | [Boat](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Boat.tar) | [原数据下载地址](https://www.kaggle.com/datasets/imsparsh/dockship-boat-type-classification) |
| 鞋子 | <img src="../../images/ppshitu_application_scenarios/Shoes.jpeg" height = "100"> |鞋子种类识别，包括靴子、拖鞋等 | 0.9000 | [Shoes](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Shoes.tar) | [原数据下载地址](https://www.kaggle.com/datasets/noobyogi0100/shoe-dataset) |
| 巴黎建筑 | <img src="../../images/ppshitu_application_scenarios/Paris.jpg" height = "100"> |巴黎著名建筑景点识别，如：巴黎铁塔、圣母院等 | 1.000 | [Paris](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Paris.tar) | [原数据下载地址](https://www.kaggle.com/datasets/skylord/oxbuildings) |
| 蝴蝶 | <img src="../../images/ppshitu_application_scenarios/Butterfly.jpg" height = "100"> |75种蝴蝶细分类识别 | 0.9360 | [Butterfly](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Butterfly.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/butterfly-images40-species) |
| 野外植物 | <img src="../../images/ppshitu_application_scenarios/WildEdiblePlants.jpg" height = "100"> |野外植物识别 | 0.9758 | [WildEdiblePlants](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/WildEdiblePlants.tar) | [原数据下载地址](https://www.kaggle.com/datasets/ryanpartridge01/wild-edible-plants) |
| 天气 | <img src="../../images/ppshitu_application_scenarios/WeatherImageRecognition.jpg" height = "100"> |各种天气场景识别，如：雨天、打雷、下雪等 | 0.9924 | [WeatherImageRecognition](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/WeatherImageRecognition.tar) | [原数据下载地址](https://www.kaggle.com/datasets/jehanbhathena/weather-dataset) |
| 坚果 | <img src="../../images/ppshitu_application_scenarios/TreeNuts.jpg" height = "100"> |各种坚果种类识别 | 0.9412 | [TreeNuts](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/TreeNuts.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/tree-nuts-image-classification) |
| 时装 | <img src="../../images/ppshitu_application_scenarios/FashionProductsImage.jpg" height = "100"> |首饰、挎包、化妆品等时尚商品识别 | 0.9555 | [FashionProductImageSmall](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/FashionProductImageSmall.tar) | [原数据下载地址](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small) |
| 垃圾 | <img src="../../images/ppshitu_application_scenarios/Garbage12.jpg" height = "100"> |12种垃圾分类识别 | 0.9845 | [Garbage12](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Garbage12.tar) | [原数据下载地址](https://www.kaggle.com/datasets/mostafaabla/garbage-classification) |
| 航拍场景 | <img src="../../images/ppshitu_application_scenarios/AID.jpg" height = "100"> |各种航拍场景识别，如机场、火车站等 | 0.9797 | [AID](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/AID.tar) | [原数据下载地址](https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets) |
| 蔬菜 | <img src="../../images/ppshitu_application_scenarios/Veg200.jpg" height = "100"> |各种蔬菜识别 | 0.8929 | [Veg200](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Veg200.tar) | [原数据下载地址](https://www.kaggle.com/datasets/zhaoyj688/vegfru) |
| 商标 | <img src="../../images/ppshitu_application_scenarios/Logo3K.jpg" height = "100"> |两千多种logo识别 | 0.9313 | [Logo3k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Logo3k.tar) | [原数据下载地址](https://github.com/Wangjing1551/LogoDet-3K-Dataset) |
......@@ -88,10 +86,10 @@ wget {场景库下载链接} && tar -xf {压缩包的名称}
│ ├── Query/
│ ├── gallery_list.txt/
│ ├── query_list.txt/
│ ├── image_list.txt/
│ ├── label_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，`gallery_list.txt`和`query_list.txt`分别为索引库和检索图像的标签文件。
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，`label_list.txt`是标签的中英文对照文件（注意：商标场景库文件不包含中英文对照文件）。
<a name="2.3 准备识别模型"></a>
......@@ -103,10 +101,12 @@ mkdir models
cd models
# 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}
# wget {检测模型下载链接} && tar -xf {检测模型压缩包名称}
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.0_infer.tar
# 下载识别 inference 模型并解压
wget {识别模型下载链接} && tar -xf {识别模型压缩包名称}
#wget {识别模型下载链接} && tar -xf {识别模型压缩包名称}
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
```
解压完成后，`models`文件夹下有如下文件结构：
......@@ -138,26 +138,26 @@ pip install faiss-cpu==1.7.1post2
#### 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。
假设需要测试`./datasets/AnimalImageDataset/Query/羚羊/0a37838e99.jpg`这张图像识别和检索效果。
首先分别修改配置文件`./configs/inference_general.yaml`中的`Global.det_inference_model_dir`和`Global.rec_inference_model_dir`字段为对应的检测和识别模型文件夹，以及修改测试图像地址字段`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'
infer_imgs: './datasets/AnimalImageDataset/Query/羚羊/0a37838e99.jpg'
det_inference_model_dir: './models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar'
rec_inference_model_dir: './models/general_PPLCNetV2_base_pretrained_v1.0_infer.tar'
```
并修改配置文件`./configs/inference_general.yaml`中的`IndexProcess.index_dir`字段为对应场景index库地址：
```shell
IndexProcess:
index_dir:'./datasets/AnimalImageDataset/Index/SampleAll/index'
index_dir:'./datasets/AnimalImageDataset/Index/'
```
运行下面的命令，对图像`./datasets/AnimalImageDataset/Query/antelope/0a37838e99.jpg`进行识别与检索
运行下面的命令，对图像`./datasets/AnimalImageDataset/Query/羚羊/0a37838e99.jpg`进行识别与检索
```shell
# 使用下面的命令使用 GPU 进行预测
......@@ -169,12 +169,12 @@ python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.u
最终输出结果如下：
```
[{'bbox': [264, 79, 1088, 850], 'rec_docs': 'antelope', 'rec_scores': 0.81452656}]
[{'bbox': [609, 70, 1079, 629], 'rec_docs': '羚羊', 'rec_scores': 0.6571544}]
```
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/rec_result.jpg)
![](../../images/ppshitu_application_scenarios/systerm_result.jpg)
<a name="2.4.2 基于文件夹的批量识别"></a>
......@@ -184,18 +184,18 @@ python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.u
```shell
# 使用下面的命令使用 GPU 进行预测，如果希望使用 CPU 预测，可以在命令后面添加 -o Global.use_gpu=False
python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.infer_imgs="./datasets/AnimalImageDataset/Query/antelope"
python3.7 python/predict_system.py -c configs/inference_general.yaml -o Global.infer_imgs="./datasets/AnimalImageDataset/Query/羚羊"
```
终端中会输出该文件夹内所有图像的识别结果，如下所示。
```
...
[{'bbox': [0, 0, 1200, 675], 'rec_docs': 'antelope', 'rec_scores': 0.6153812}]
[{'bbox': [0, 0, 275, 183], 'rec_docs': 'antelope', 'rec_scores': 0.77218026}]
[{'bbox': [264, 79, 1088, 850], 'rec_docs': 'antelope', 'rec_scores': 0.81452656}]
[{'bbox': [0, 0, 188, 268], 'rec_docs': 'antelope', 'rec_scores': 0.637074}]
[{'bbox': [118, 41, 235, 161], 'rec_docs': 'antelope', 'rec_scores': 0.67315465}]
[{'bbox': [0, 0, 175, 287], 'rec_docs': 'antelope', 'rec_scores': 0.68271667}]
[{'bbox': [0, 0, 310, 163], 'rec_docs': 'antelope', 'rec_scores': 0.6706451}]
[{'bbox': [0, 0, 1200, 675], 'rec_docs': '羚羊', 'rec_scores': 0.6153812}]
[{'bbox': [0, 0, 275, 183], 'rec_docs': '羚羊', 'rec_scores': 0.77218026}]
[{'bbox': [264, 79, 1088, 850], 'rec_docs': '羚羊', 'rec_scores': 0.81452656}]
[{'bbox': [0, 0, 188, 268], 'rec_docs': '羚羊', 'rec_scores': 0.637074}]
[{'bbox': [118, 41, 235, 161], 'rec_docs': '羚羊', 'rec_scores': 0.67315465}]
[{'bbox': [0, 0, 175, 287], 'rec_docs': '羚羊', 'rec_scores': 0.68271667}]
[{'bbox': [0, 0, 310, 163], 'rec_docs': '羚羊', 'rec_scores': 0.6706451}]
...
```
所有图像的识别结果可视化图像也保存在`output`文件夹内。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或