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Apache-2.0

dddd_trainer 带带弟弟OCR训练工具

带带弟弟OCR所用的训练工具今天正式开源啦! ddddocr

项目仅支持N卡训练,A卡或其他卡就先别看啦

项目基于Pytorch进行开发,支持cnn与crnn进行训练、断点恢复、自动导出onnx模型,并同时支持无缝使用ddddocrocr_api_server 的无缝部署

训练环境支持

Windows/Linux

Macos仅支持cpu训练

1、深度学习必备环境配置(非仅本项目要求,而是所有深度学习项目要求,cpu训练除外)

开始本教程前请先前往pytorch 官网查看自己系统与硬件支持的pytorch版本,注意30系列之前的N卡,如2080Ti等请选择cuda11以下的版本(例:CUDA 10.2),如果为30系N卡,仅支持CUDA 11版本,请选择CUDA 11以上版本(例:CUDA 11.3),然后根据选择的条件显示的pytorch安装命令完成pytorch安装,由于pytorch的版本更新速度导致很多pypi源仅缓存了cpu版本,CUDA版本需要自己在官网安装。

安装CUDA和CUDNN

根据自己显卡型号与系统选择

cuda

cudnn

注意cudnn支持的cuda版本号要与你安装的cuda版本号对应,不同版本的cuda支持的显卡不一样,20系无脑选择10.2版本cuda,30系无脑选择11.3版本cuda,这里有啥问题就百度吧,算是一个基础问题。

2、训练部分

  • 以下所有变量均以 {param} 格式代替,表示可根据自己需要修改,而使用时并不需要带上{},如步骤创建新的训练项目,使用时可以直接写

python app.py create test_project

  • 1、Clone本项目到本地

git clone https://github.com/sml2h3/dddd_trainer.git

  • 2、进入项目目录并安装本项目所需依赖

pip install -r requirements.txt -i https://pypi.douban.com/simple

  • 3、创建新的训练项目

python app.py create {project_name}

如果想要创建一个CNN的项目,则可以加上--single参数,CNN项目识别比如图片类是什么分类的情况,比如图片上只有一个字,识别这张图是什么字(图上有多个字的不要用CNN模式),又比如分辨图片里是狮子还是兔子用CNN模式比较合适,大多数OCR需求请不要使用--single

python app.py create {project_name} --single

project_name 为项目名称,尽量不要以特殊符号命名

  • 4、准备数据

    项目支持两种形式的数据

    A、从文件名导入

    图片均在同一个文件夹中,且命名为类似,其中/root/images_set为图片所在目录,可以为任意目录地址

    /root/images_set/
    |---- abcde_随机hash值.jpg
    |---- sdae_随机hash值.jpg
    |---- 酱闷肘子_随机hash值.jpg
    

    如下图所示

    image

    那么图片命名可以是

    mkGu_000001d00f140741741ed9916240d8d5.jpg

    为考虑各种情况,dddd_trainer不会自动去处理大小写问题,如果想训练大小写,则在样本标注时就需要自己标注好大小写,如上面例子

    B、从文件中导入

    受限于可能样本组织形式或者特殊字符,本项目支持从txt文档中导入数据,数据集目录必须包含有labels.txt文件和images文件夹, 其中/root/images_set为图片所在目录,可以为任意目录地址

    labels.txt文件中包含了所有在/root/images_set/images目录下基于/root/images_set/images的图片相对路径,/root/images_set/images下可以有目录。

    当然,在这种模式下,图片的文件名随意,可以有具体label也可以没有,因为咱们不从这里获取图片的label

    如下所示

  • a.images下无目录的形式

    /root/images_set/
    |---- labels.txt
    |---- images
          |---- 随机hash值.jpg
          |---- 随机hash值.jpg
          |---- 酱闷肘子_随机hash值.jpg
    
    labels.txt文件内容为(其中\t制表符为每行文件名与label的分隔符)
    随机hash值.jpg\tabcd
    随机hash值.jpg\tsdae
    酱闷肘子_随机hash值.jpg\t酱闷肘子

    b.images下有目录的形式

    /root/images_set/
    |---- labels.txt
    |---- images
          |---- aaaa
                |---- 随机hash值.jpg
          |---- 酱闷肘子_随机hash值.jpg
    
    labels.txt文件内容为(其中\t制表符为每行文件名与label的分隔符)
    aaaa/随机hash值.jpg\tabcd
    aaaa/随机hash值.jpg\tsdae
    酱闷肘子_随机hash值.jpg\t酱闷肘子
    

    为了新手更好的理解本部分的内容,本项目也提供了两套基础数据集提供测试

    数据集一 数据集二

  • 5、修改配置文件

Model:
    CharSet: []     # 字符集,不要动,会自动生成
    ImageChannel: 1 # 图片通道数,如果你想以灰度图进行训练,则设置为1,彩图,则设置为3。如果设置为1,数据集是彩图,项目会在训练的过程中自动在内存中将读取到的彩图转为灰度图,并不需要提前自己修改并且该设置不会修改本地图片
    ImageHeight: 64 # 图片自动缩放后的高度,单位为px,高度必须为16的倍数,会自动缩放图像
    ImageWidth: -1  # 图片自动缩放后的宽度,单位为px,本项若设置为-1,将自动根据情况调整
    Word: false     # 是否为CNN模型,这里在创建项目的时候通过参数控制,不要自己修改
System:
    Allow_Ext: [jpg, jpeg, png, bmp]  # 支持的图片后缀,不满足的图片将会被自动忽略
    GPU: true                         # 是否启用GPU去训练,使用GPU训练需要参考步骤一安装好环境
    GPU_ID: 0                         # GPU设备号,0为第一张显卡
    Path: ''                          # 数据集根目录,在缓存图片步骤会自动生成,不需要自己改,除非数据集地址改了
    Project: test                     # 项目名称 也就是{project_name}
    Val: 0.03                         # 验证集的数据量比例,0.03就是3%,在缓存数据时,会自动选则3%的图片用作训练过程中的数据验证,修改本值之后需要重新缓存数据
Train:
    BATCH_SIZE: 32                                    # 训练时每一个batch_size的大小,主要取决于你的显存或内存大小,可以根据自己的情况,多测试,一般为16的倍数,如16,32,64,128
    CNN: {NAME: ddddocr}                              # 特征提取的模型,目前支持的值为ddddocr,effnetv2_l,effnetv2_m,effnetv2_xl,effnetv2_s,mobilenetv2,mobilenetv3_s,mobilenetv3_l
    DROPOUT: 0.3                                      # 非专业人员不要动
    LR: 0.01                                          # 初始学习率
    OPTIMIZER: SGD                                    # 优化器,不要动
    SAVE_CHECKPOINTS_STEP: 2000                       # 每多少step保存一次模型
    TARGET: {Accuracy: 0.97, Cost: 0.05, Epoch: 20}   # 训练结束的目标,同时满足时自动结束训练并保存onnx模型,Accuracy为需要满足的最小准确率,Cost为需要满足的最小损失,Epoch为需要满足的最小训练轮数
    TEST_BATCH_SIZE: 32                               # 测试时每一个batch_size的大小,主要取决于你的显存或内存大小,可以根据自己的情况,多测试,一般为16的倍数,如16,32,64,128
    TEST_STEP: 1000                                   # 每多少step进行一次测试

配置文件位于本项目根目录下projects/{project_name}/config.yaml

  • 6、缓存数据

python app.py cache {project_name} /root/images_set/

如果是从labels.txt里面读取数据

python app.py cache {project_name} /root/images_set/ file

  • 7、开始训练或恢复训练

python app.py train {project_name}

  • 8、部署

你们先训练着,我去适配ddddocr和ocr_api_server了,适配完我再继续更新文档

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