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[PaddleDetection](#PaddleDetection) -- [数据集](#数据集) ## 简介 -这份文档介绍了如何安装PaddleDetection及其依赖项(包括PaddlePaddle),以及COCO和Pascal VOC数据集。 +这份文档介绍了如何安装PaddleDetection及其依赖项(包括PaddlePaddle)。 PaddleDetection的相关信息,请参考[README.md](https://github.com/PaddlePaddle/PaddleDetection/blob/master/README.md). -## PaddlePaddle +## 安装PaddlePaddle +**环境需求:** + +- OS 64位操作系统 +- Python2 >= 2.7.15 or Python 3(3.5.1+/3.6/3.7),64位版本 +- pip/pip3(9.0.1+),64位版本操作系统是 +- CUDA >= 9.0 +- cuDNN >= 7.6 + +如果需要 GPU 多卡训练,请先安装NCCL(Windows暂不支持nccl)。 + +PaddleDetection 依赖 PaddlePaddle 版本关系: -运行PaddleDetection需要PaddlePaddle Fluid v.1.6及更高版本。请按照[安装文档](http://www.paddlepaddle.org.cn/)中的说明进行操作。 +| PaddleDetection版本 | PaddlePaddle版本 | 备注 | +| :----------------: | :---------------: | :-------: | +| v0.3 | >=1.7 | -- | +| v0.4 | >= 1.8.4 | PP-YOLO依赖1.8.4 | + + +``` +# install paddlepaddle +python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple + +# 如果您的机器安装的是CUDA9,请运行以下命令安装 +python -m pip install paddlepaddle-gpu==1.8.4.post97 -i https://mirror.baidu.com/pypi/simple + +如果您的机器安装的是CUDA10,请运行以下命令安装 +python -m pip install paddlepaddle-gpu==1.8.4.post107 -i https://mirror.baidu.com/pypi/simple + +如果您的机器是CPU,请运行以下命令安装 + +python -m pip install paddlepaddle==1.8.4 -i https://mirror.baidu.com/pypi/simple +``` +更多的安装方式如conda, docker安装,请参考[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作 请确保您的PaddlePaddle安装成功并且版本不低于需求版本。使用以下命令进行验证。 @@ -33,13 +63,6 @@ PaddleDetection的相关信息,请参考[README.md](https://github.com/PaddleP python -c "import paddle; print(paddle.__version__)" ``` -**环境需求:** - -- Python2 or Python3 (windows系统仅支持Python3) -- CUDA >= 8.0 -- cuDNN >= 5.0 -- nccl >= 2.1.2 - ## 其他依赖安装 @@ -47,35 +70,19 @@ python -c "import paddle; print(paddle.__version__)" 运行需要COCO-API,安装方式如下: - git clone https://github.com/cocodataset/cocoapi.git - cd cocoapi/PythonAPI - # 若Cython未安装,请安装Cython - pip install Cython - # 安装至全局site-packages - make install - # 若您没有权限或更倾向不安装至全局site-packages - python setup.py install --user - # 或者使用pip安装 - pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI" + # 安装pycocotools + pip install pycocotools **windows用户安装COCO-API方式:** # 若Cython未安装,请安装Cython pip install Cython + # 由于原版cocoapi不支持windows,采用第三方实现版本,该版本仅支持Python3 pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI ## PaddleDetection -**克隆Paddle models模型库:** - -您可以通过以下命令克隆PaddleDetection: - -``` -cd -git clone https://github.com/PaddlePaddle/PaddleDetection.git -``` - **安装Python依赖库:** Python依赖库在[requirements.txt](https://github.com/PaddlePaddle/PaddleDetection/blob/master/requirements.txt)中给出,可通过如下命令安装: @@ -84,113 +91,47 @@ Python依赖库在[requirements.txt](https://github.com/PaddlePaddle/PaddleDetec pip install -r requirements.txt ``` -**确认测试通过:** +**克隆PaddleDetection库:** + +您可以通过以下命令克隆PaddleDetection: ``` -python ppdet/modeling/tests/test_architectures.py +cd +git clone https://github.com/PaddlePaddle/PaddleDetection.git ``` +**提示:** -## 数据集 - - -PaddleDetection默认支持[COCO](http://cocodataset.org)和[Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/), -请按照如下步骤设置数据集。 - -**为本地数据集创建软链接:** - - -配置文件中默认的数据集路径是`dataset/coco`和`dataset/voc`,如果您本地磁盘上已有数据集, -只需创建软链接至数据集目录: - +也可以通过 [https://gitee.com/paddlepaddle/PaddleDetection](https://gitee.com/paddlepaddle/PaddleDetection) 克隆。 ``` -ln -sf /dataset/coco -ln -sf /dataset/voc +cd +git clone https://gitee.com/paddlepaddle/PaddleDetection ``` -对于Pascal VOC数据集,需通过如下命令创建文件列表: +**确认测试通过:** ``` -python dataset/voc/create_list.py +python ppdet/modeling/tests/test_architectures.py ``` -**手动下载数据集:** - -若您本地没有数据集,可通过如下命令下载: - -- COCO - +测试通过后会提示如下信息: ``` -python dataset/coco/download_coco.py +.......... +---------------------------------------------------------------------- +Ran 12 tests in 2.480s +OK (skipped=2) ``` -`COCO` 数据集目录结构如下: +**预训练模型预测** - ``` - dataset/coco/ - ├── annotations - │ ├── instances_train2014.json - │ ├── instances_train2017.json - │ ├── instances_val2014.json - │ ├── instances_val2017.json - │ | ... - ├── train2017 - │ ├── 000000000009.jpg - │ ├── 000000580008.jpg - │ | ... - ├── val2017 - │ ├── 000000000139.jpg - │ ├── 000000000285.jpg - │ | ... - | ... - ``` - -- Pascal VOC +使用预训练模型预测图像,快速体验模型预测效果: ``` -python dataset/voc/download_voc.py +# use_gpu参数设置是否使用GPU +python tools/infer.py -c configs/ppyolo/ppyolo.yml -o use_gpu=true weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439_640x640.jpg ``` -`Pascal VOC` 数据集目录结构如下: - - ``` - dataset/voc/ - ├── trainval.txt - ├── test.txt - ├── label_list.txt (optional) - ├── VOCdevkit/VOC2007 - │ ├── Annotations - │ ├── 001789.xml - │ | ... - │ ├── JPEGImages - │ ├── 001789.jpg - │ | ... - │ ├── ImageSets - │ | ... - ├── VOCdevkit/VOC2012 - │ ├── Annotations - │ ├── 2011_003876.xml - │ | ... - │ ├── JPEGImages - │ ├── 2011_003876.jpg - │ | ... - │ ├── ImageSets - │ | ... - | ... - ``` - -**说明:** 如果你在yaml配置文件中设置`use_default_label=False`, 将从`label_list.txt` -中读取类别列表,反之则可以没有`label_list.txt`文件,检测库会使用Pascal VOC数据集的默 -认类别列表,默认类别列表定义在[voc.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/ppdet/data/source/voc.py) - -**自动下载数据集:** - -若您在数据集未成功设置(例如,在`dataset/coco`或`dataset/voc`中找不到)的情况下开始运行, -PaddleDetection将自动从[COCO-2017](http://images.cocodataset.org)或 -[VOC2012](http://host.robots.ox.ac.uk/pascal/VOC)下载,解压后的数据集将被保存在 -`〜/.cache/paddle/dataset/`目录下,下次运行时,也可自动从该目录发现数据集。 - +会在`output`文件夹下生成一个画有预测结果的同名图像。 -**说明:** +结果如下图: -- 如果要使用自定义数据集,请参考[自定义数据集文档](Custom_DataSet.md) -- 更多有关数据集的介绍,请参考[数据处理文档](../advanced_tutorials/READER.md) +![](../images/000000014439_640x640.jpg) diff --git a/docs/tutorials/PrepareDataSet.md b/docs/tutorials/PrepareDataSet.md new file mode 100644 index 000000000..c27359dd9 --- /dev/null +++ b/docs/tutorials/PrepareDataSet.md @@ -0,0 +1,417 @@ +# 如何准备训练数据 +## 目录 +- [目标检测数据说明](#目标检测数据说明) +- [准备训练数据](#准备训练数据) + - [VOC数据数据](#VOC数据数据) + - [VOC数据集下载](#VOC数据集下载) + - [VOC数据标注文件介绍](#VOC数据标注文件介绍) + - [COCO数据数据](#COCO数据数据) + - [COCO数据集下载](#COCO数据下载) + - [COCO数据标注文件介绍](#COCO数据标注文件介绍) + - [用户数据](#用户数据) + - [用户数据转成VOC数据](#用户数据转成VOC数据) + - [用户数据转成COCO数据](#用户数据转成COCO数据) + - [用户数据自定义reader](#用户数据自定义reader) + - [用户数据数据转换示例](#用户数据数据转换示例) + +### 目标检测数据说明 +目标检测的数据比分类复杂,一张图像中,需要标记出各个目标区域的位置和类别。 + +一般的目标区域位置用一个矩形框来表示,一般用以下3种方式表达: + +| 表达方式 | 说明 | +| :----------------: | :--------------------------------: | +| x1,y1,x2,y2 | (x1,y1)为左上角坐标,(x2,y2)为右下角坐标 | +| x,y,w,h | (x,y)为左上角坐标,w为目标区域宽度,h为目标区域高度 | +| xc,yc,w,h | (xc,yc)为目标区域中心坐标,w为目标区域宽度,h为目标区域高度 | + +常见的目标检测数据集如Pascal VOC和COCO,采用的是第一种 `x1,y1,x2,y2` 表示物体的bounding box. + +### 准备训练数据 +PaddleDetection默认支持[COCO](http://cocodataset.org)和[Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) 和[WIDER-FACE](http://shuoyang1213.me/WIDERFACE/) 数据源。 +同时还支持自定义数据源,包括: + +(1) 自定义数据数据转换成VOC数据; +(2) 自定义数据数据转换成COCO数据; +(3) 自定义新的数据源,增加自定义的reader。 + + +首先进入到`PaddleDetection`根目录下 +``` +cd PaddleDetection/ +ppdet_root=$(pwd) +``` + +#### VOC数据数据 +VOC数据是[Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) 比赛使用的数据。Pascal VOC比赛不仅包含图像分类分类任务,还包含图像目标检测、图像分割等任务,其标注文件中包含多个任务的标注内容。 +VOC数据集指的是Pascal VOC比赛使用的数据。用户自定义的VOC数据,xml文件中的非必须字段,请根据实际情况选择是否标注或是否使用默认值。 +##### VOC数据集下载 + +- 通过代码自动化下载VOC数据集 + + ``` + # 执行代码自动化下载VOC数据集 + python dataset/voc/download_voc.py + ``` + + 代码执行完成后VOC数据集文件组织结构为: + ``` + >>cd dataset/voc/ + >>tree + ├── create_list.py + ├── download_voc.py + ├── generic_det_label_list.txt + ├── generic_det_label_list_zh.txt + ├── label_list.txt + ├── VOCdevkit/VOC2007 + │ ├── annotations + │ ├── 001789.xml + │ | ... + │ ├── JPEGImages + │ ├── 001789.jpg + │ | ... + │ ├── ImageSets + │ | ... + ├── VOCdevkit/VOC2012 + │ ├── Annotations + │ ├── 2011_003876.xml + │ | ... + │ ├── JPEGImages + │ ├── 2011_003876.jpg + │ | ... + │ ├── ImageSets + │ | ... + | ... + ``` + + 各个文件说明 + ``` + # label_list.txt 是类别名称列表,文件名必须是 label_list.txt。若使用VOC数据集,config文件中use_default_label为true时不需要这个文件 + >>cat label_list.txt + aeroplane + bicycle + ... + + # trainval.txt 是训练数据集文件列表 + >>cat trainval.txt + VOCdevkit/VOC2007/JPEGImages/007276.jpg VOCdevkit/VOC2007/Annotations/007276.xml + VOCdevkit/VOC2012/JPEGImages/2011_002612.jpg VOCdevkit/VOC2012/Annotations/2011_002612.xml + ... + + # test.txt 是测试数据集文件列表 + >>cat test.txt + VOCdevkit/VOC2007/JPEGImages/000001.jpg VOCdevkit/VOC2007/Annotations/000001.xml + ... + + # label_list.txt voc 类别名称列表 + >>cat label_list.txt + + aeroplane + bicycle + ... + ``` +- 已下载VOC数据集 + 按照如上数据文件组织结构组织文件即可。 + +##### VOC数据标注文件介绍 +VOC数据是每个图像文件对应一个同名的xml文件,xml文件中标记物体框的坐标和类别等信息。例如图像`2007_002055.jpg`: +![](../images/2007_002055.jpg) + +图片对应的xml文件内包含对应图片的基本信息,比如文件名、来源、图像尺寸以及图像中包含的物体区域信息和类别信息等。 + +xml文件中包含以下字段: +- filename,表示图像名称。 +- size,表示图像尺寸。包括:图像宽度、图像高度、图像深度。 + ``` + + 500 + 375 + 3 + + ``` +- object字段,表示每个物体。包括: + + | 标签 | 说明 | + | :--------: | :-----------: | + | name | 物体类别名称 | + | pose | 关于目标物体姿态描述(非必须字段) | + | truncated | 如果物体的遮挡超过15-20%并且位于边界框之外,请标记为`truncated`(非必须字段) | + | difficult | 难以识别的物体标记为`difficult`(非必须字段) | + | bndbox子标签 | (xmin,ymin) 左上角坐标,(xmax,ymax) 右下角坐标, | + + +#### COCO数据 +COCO数据是[COCO](http://cocodataset.org) 比赛使用的数据。同样的,COCO比赛数也包含多个比赛任务,其标注文件中包含多个任务的标注内容。 +COCO数据集指的是COCO比赛使用的数据。用户自定义的COCO数据,json文件中的一些字段,请根据实际情况选择是否标注或是否使用默认值。 + + +##### COCO数据下载 +- 通过代码自动化下载COCO数据集 + + ``` + # 执行代码自动化下载COCO数据集 + python dataset/voc/download_coco.py + ``` + + 代码执行完成后COCO数据集文件组织结构为: + ``` + >>cd dataset/coco/ + >>tree + ├── annotations + │ ├── instances_train2017.json + │ ├── instances_val2017.json + │ | ... + ├── train2017 + │ ├── 000000000009.jpg + │ ├── 000000580008.jpg + │ | ... + ├── val2017 + │ ├── 000000000139.jpg + │ ├── 000000000285.jpg + │ | ... + | ... + ``` +- 已下载COCO数据集 + 按照如上数据文件组织结构组织文件即可。 + +##### COCO数据标注介绍 +COCO数据标注是将所有训练图像的标注都存放到一个json文件中。数据以字典嵌套的形式存放。 + +json文件中包含以下key: +- info,表示标注文件info。 +- licenses,表示标注文件licenses。 +- images,表示标注文件中图像信息列表,每个元素是一张图像的信息。如下为其中一张图像的信息: + ``` + { + 'license': 3, # license + 'file_name': '000000391895.jpg', # file_name + # coco_url + 'coco_url': 'http://images.cocodataset.org/train2017/000000391895.jpg', + 'height': 360, # image height + 'width': 640, # image width + 'date_captured': '2013-11-14 11:18:45', # date_captured + # flickr_url + 'flickr_url': 'http://farm9.staticflickr.com/8186/8119368305_4e622c8349_z.jpg', + 'id': 391895 # image id + } + ``` +- annotations,表示标注文件中目标物体的标注信息列表,每个元素是一个目标物体的标注信息。如下为其中一个目标物体的标注信息: + ``` + { + + 'segmentation': # 物体的分割标注 + 'area': 2765.1486500000005, # 物体的区域面积 + 'iscrowd': 0, # iscrowd + 'image_id': 558840, # image id + 'bbox': [199.84, 200.46, 77.71, 70.88], # bbox + 'category_id': 58, # category_id + 'id': 156 # image id + } + ``` + + ``` + # 查看COCO标注文件 + import json + coco_anno = json.load(open('./annotations/instances_train2017.json')) + + # coco_anno.keys + print('\nkeys:', coco_anno.keys()) + + # 查看类别信息 + print('\n物体类别:', coco_anno['categories']) + + # 查看一共多少张图 + print('\n图像数量:', len(coco_anno['images'])) + + # 查看一共多少个目标物体 + print('\n标注物体数量:', len(coco_anno['annotations'])) + + # 查看一条目标物体标注信息 + print('\n查看一条目标物体标注信息:', coco_anno['annotations'][0]) + ``` + + COCO数据准备如下。 + `dataset/coco/`最初文件组织结构 + ``` + >>cd dataset/coco/ + >>tree + ├── download_coco.py + ``` + +#### 用户数据 +对于用户数据有3种处理方法: +(1) 将用户数据转成VOC数据(根据需要仅包含物体检测所必须的标签即可) +(2) 将用户数据转成COCO数据(根据需要仅包含物体检测所必须的标签即可) +(3) 自定义一个用户数据的reader(较复杂数据,需要自定义reader) + +##### 用户数据转成VOC数据 +用户数据集转成VOC数据后目录结构如下(注意数据集中路径名、文件名尽量不要使用中文,避免中文编码问题导致出错): + +``` +dataset/xxx/ +├── annotations +│ ├── xxx1.xml +│ ├── xxx2.xml +│ ├── xxx3.xml +│ | ... +├── images +│ ├── xxx1.jpg +│ ├── xxx2.jpg +│ ├── xxx3.jpg +│ | ... +├── label_list.txt (必须提供,且文件名称必须是label_list.txt ) +├── train.txt (训练数据集文件列表, ./images/xxx1.jpg ./annotations/xxx1.xml) +└── valid.txt (测试数据集文件列表) +``` + +各个文件说明 +``` +# label_list.txt 是类别名称列表,改文件名必须是这个 +>>cat label_list.txt +classname1 +classname2 +... + +# train.txt 是训练数据文件列表 +>>cat train.txt +./images/xxx1.jpg ./annotations/xxx1.xml +./images/xxx2.jpg ./annotations/xxx2.xml +... + +# valid.txt 是验证数据文件列表 +>>cat valid.txt +./images/xxx3.jpg ./annotations/xxx3.xml +... +``` + +##### 用户数据转成COCO +在`./tools/`中提供了`x2coco.py`用于将VOC数据集、labelme标注的数据集或cityscape数据集转换为COCO数据,例如: + +(1)labelmes数据转换为COCO数据: +```bash +python tools/x2coco.py \ + --dataset_type labelme \ + --json_input_dir ./labelme_annos/ \ + --image_input_dir ./labelme_imgs/ \ + --output_dir ./cocome/ \ + --train_proportion 0.8 \ + --val_proportion 0.2 \ + --test_proportion 0.0 +``` +(2)voc数据转换为COCO数据: +```bash +python tools/x2coco.py \ + --dataset_type voc \ + --voc_anno_dir path/to/VOCdevkit/VOC2007/Annotations/ \ + --voc_anno_list path/to/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt \ + --voc_label_list dataset/voc/label_list.txt \ + --voc_out_name voc_train.json +``` + +用户数据集转成COCO数据后目录结构如下(注意数据集中路径名、文件名尽量不要使用中文,避免中文编码问题导致出错): +``` +dataset/xxx/ +├── annotations +│ ├── train.json # coco数据的标注文件 +│ ├── valid.json # coco数据的标注文件 +├── images +│ ├── xxx1.jpg +│ ├── xxx2.jpg +│ ├── xxx3.jpg +│ | ... +... +``` + +##### 用户数据自定义reader +如果数据集有新的数据需要添加进PaddleDetection中,您可参考数据处理文档中的[添加新数据源](../advanced_tutorials/READER.md#添加新数据源)文档部分,开发相应代码完成新的数据源支持,同时数据处理具体代码解析等可阅读[数据处理文档](../advanced_tutorials/READER.md) + + +#### 用户数据数据转换示例 + +以[Kaggle数据集](https://www.kaggle.com/andrewmvd/road-sign-detection) 比赛数据为例,说明如何准备自定义数据。 +Kaggle上的 [road-sign-detection](https://www.kaggle.com/andrewmvd/road-sign-detection) 比赛数据包含877张图像,数据类别4类:crosswalk,speedlimit,stop,trafficlight。 +可从Kaggle上下载,也可以从[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/roadsign.zip) 下载。 +路标数据集示例图: +![](../images/road554.png) + +``` +# 下载解压数据 +>>cd $(ppdet_root)/dataset +# 下载kaggle数据集并解压,当前文件组织结构如下 + +├── annotations +│ ├── road0.xml +│ ├── road1.xml +│ ├── road10.xml +│ | ... +├── images +│ ├── road0.jpg +│ ├── road1.jpg +│ ├── road2.jpg +│ | ... +``` + +将数据划分为训练集和测试集 +``` +# 生成 label_list.txt 文件 +>>echo "speedlimit\ncrosswalk\ntrafficlight\nstop" > label_list.txt + +# 生成 train.txt、valid.txt和test.txt列表文件 +>>ls images/*.png | shuf > all_image_list.txt +>>awk -F"/" '{print $2}' all_image_list.txt | awk -F".png" '{print $1}' | awk -F"\t" '{print "images/"$1".png annotations/"$1".xml"}' > all_list.txt + +# 训练集、验证集、测试集比例分别约80%、10%、10%。 +>>head -n 88 all_list.txt > test.txt +>>head -n 176 all_list.txt | tail -n 88 > valid.txt +>>tail -n 701 all_list.txt > train.txt + +# 删除不用文件 +>>rm -rf all_image_list.txt all_list.txt + +最终数据集文件组织结构为: + +├── annotations +│ ├── road0.xml +│ ├── road1.xml +│ ├── road10.xml +│ | ... +├── images +│ ├── road0.jpg +│ ├── road1.jpg +│ ├── road2.jpg +│ | ... +├── label_list.txt +├── test.txt +├── train.txt +└── valid.txt + +# label_list.txt 是类别名称列表,文件名必须是 label_list.txt +>>cat label_list.txt +crosswalk +speedlimit +stop +trafficlight + +# train.txt 是训练数据集文件列表,每一行是一张图像路径和对应标注文件路径,以空格分开。注意这里的路径是数据集文件夹内的相对路径。 +>>cat train.txt +./images/road839.png ./annotations/road839.xml +./images/road363.png ./annotations/road363.xml +... + +# valid.txt 是验证数据集文件列表,每一行是一张图像路径和对应标注文件路径,以空格分开。注意这里的路径是数据集文件夹内的相对路径。 +>>cat valid.txt +./images/road218.png ./annotations/road218.xml +./images/road681.png ./annotations/road681.xml +``` + +也可以下载准备好的数据[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.zip) ,解压到`dataset/roadsign_voc/`文件夹下即可。 +准备好数据后,一般的我们要对数据有所了解,比如图像量,图像尺寸,每一类目标区域个数,目标区域大小等。如有必要,还要对数据进行清洗。 +roadsign数据集统计: + +| 数据 | 图片数量 | +| :--------: | :-----------: | +| train | 701 | +| valid | 176 | + +**说明:**(1)用户数据,建议在训练前仔细检查数据,避免因数据标注格式错误或图像数据不完整造成训练过程中的crash +(2)如果图像尺寸太大的话,在不限制读入数据尺寸情况下,占用内存较多,会造成内存/显存溢出,请合理设置batch_size,可从小到大尝试 diff --git a/docs/tutorials/QUICK_STARTED_cn.md b/docs/tutorials/QUICK_STARTED_cn.md index 34b6106e9..030d0766d 100644 --- a/docs/tutorials/QUICK_STARTED_cn.md +++ b/docs/tutorials/QUICK_STARTED_cn.md @@ -1,77 +1,78 @@ [English](QUICK_STARTED.md) | 简体中文 - # 快速开始 +为了使得用户能够在很短时间内快速产出模型,掌握PaddleDetection的使用方式,这篇教程通过一个预训练检测模型对小数据集进行finetune。在较短时间内即可产出一个效果不错的模型。实际业务中,建议用户根据需要选择合适模型配置文件进行适配。 -为了使得用户能够在很短的时间内快速产出模型,掌握PaddleDetection的使用方式,这篇教程通过一个预训练检测模型对小数据集进行finetune。在P40上单卡大约20min即可产出一个效果不错的模型。 - -- **注:在开始前,如果有GPU设备,指定GPU设备号。** -```bash -export CUDA_VISIBLE_DEVICES=0 +## 一、快速体验 ``` +# 用PP-YOLO算法在COCO数据集上预训练模型预测一张图片 +python tools/infer.py -c configs/ppyolo/ppyolo.yml -o use_gpu=true weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439.jpg +``` +结果如下图: -## 数据准备 - -数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection),其中训练数据集240张图片,测试数据集60张图片,数据类别为3类:苹果,橘子,香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download_fruit.py](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dataset/fruit/download_fruit.py)。下载数据方式如下: +![](../images/000000014439.jpg) -```bash -python dataset/fruit/download_fruit.py -``` -## 开始训练 +## 二、准备数据 +数据集参考[Kaggle数据集](https://www.kaggle.com/andrewmvd/road-sign-detection) ,包含877张图像,数据类别4类:crosswalk,speedlimit,stop,trafficlight。 +将数据划分为训练集701张图和测试集176张图,[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar). -训练命令如下: +``` +# 注意:可跳过这步下载,后面训练会自动下载 +python dataset/roadsign_voc/download_roadsign_voc.py +``` -```bash -python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml --eval +## 三、训练、评估、预测 +### 1、训练 ``` +# 边训练边测试 CPU需要约1小时(use_gpu=false),1080Ti GPU需要约5分钟。 +# -c 参数表示指定使用哪个配置文件 +# -o 参数表示指定配置文件中的全局变量(覆盖配置文件中的设置),这里设置使用gpu, +# --eval 参数表示边训练边评估,会自动保存一个评估结果最好的名为best_model.pdmodel的模型 -训练使用`yolov3_mobilenet_v1`基于COCO数据集训练好的模型进行finetune。 +python tools/train.py -c configs/yolov3_mobilenet_v1_roadsign.yml --eval -o use_gpu=true +``` +如果想通过VisualDL实时观察loss变化去去曲线,在训练命令种添加--use_vdl=true,以及通过--vdl_log_dir设置日志保存路径。 -如果想通过VisualDL实时观察loss和精度值,启动命令添加`--use_vdl=True`,以及通过`--vdl_log_dir`设置日志保存路径,但注意**VisualDL需Python>=3.5**: +**但注意VisualDL需Python>=3.5** +首先安装[VisualDL](https://github.com/PaddlePaddle/VisualDL) +``` +python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple +``` -```bash -python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml \ - --use_vdl=True \ - --vdl_log_dir=vdl_fruit_dir/scalar \ +``` +python -u tools/train.py -c configs/yolov3_mobilenet_v1_roadsign.yml \ + --use_vdl=true \ + --vdl_log_dir=vdl_dir/scalar \ --eval ``` - -通过`visualdl`命令实时查看变化曲线: - -```bash -visualdl --logdir vdl_fruit_dir/scalar/ --host --port +通过visualdl命令实时查看变化曲线: ``` - -VisualDL结果显示如下: - -![](../images/visualdl_fruit.jpg) - -训练模型[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_fruit.tar) - -## 评估预测 - -评估命令如下: - -```bash -python -u tools/eval.py -c configs/yolov3_mobilenet_v1_fruit.yml +visualdl --logdir vdl_dir/scalar/ --host --port ``` -预测命令如下 +### 2、评估 +``` +# 评估 默认使用训练过程中保存的best_model +# -c 参数表示指定使用哪个配置文件 +# -o 参数表示指定配置文件中的全局变量(覆盖配置文件中的设置) -```bash -python -u tools/infer.py -c configs/yolov3_mobilenet_v1_fruit.yml \ - -o weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_fruit.tar \ - --infer_img=demo/orange_71.jpg +python tools/eval.py -c configs/yolov3_mobilenet_v1_roadsign.yml-o use_gpu=true ``` -预测图片如下: -![](../../demo/orange_71.jpg) +### 3、预测 +``` +# -c 参数表示指定使用哪个配置文件 +# -o 参数表示指定配置文件中的全局变量(覆盖配置文件中的设置) +# --infer_img 参数指定预测图像路径 +# 预测结束后会在output文件夹中生成一张画有预测结果的同名图像 -![](../images/orange_71_detection.jpg) +python tools/infer.py -c configs/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true --infer_img=demo/road554.png +``` +结果如下图: -更多训练及评估流程,请参考[入门使用文档](GETTING_STARTED_cn.md)。 +![](../images/road554.png) -- GitLab