update config yml file
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@ -56,7 +56,7 @@
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### Architecture ([ppocr/modeling](../../ppocr/modeling))
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在ppocr中，网络被划分为Transform,Backbone,Neck和Head四个阶段
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在PaddleOCR中，网络被划分为Transform,Backbone,Neck和Head四个阶段
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| 字段 | 用途 | 默认值 | 备注 |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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@ -128,4 +128,99 @@
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## 3. 多语言配置文件生成
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【参考识别模型训练补充内容】
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PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
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您有两种方式创建所需的配置文件：
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1. 通过脚本自动生成
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[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) 可以帮助您生成多语言模型的配置文件
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- 以意大利语为例，如果您的数据是按如下格式准备的：
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```
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|-train_data
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|- it_train.txt # 训练集标签
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|- it_val.txt # 验证集标签
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|- data
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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可以使用默认参数，生成配置文件：
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```bash
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# 该代码需要在指定目录运行
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cd PaddleOCR/configs/rec/multi_language/
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# 通过-l或者--language参数设置需要生成的语种的配置文件，该命令会将默认参数写入配置文件
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python3 generate_multi_language_configs.py -l it
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```
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- 如果您的数据放置在其他位置，或希望使用自己的字典，可以通过指定相关参数来生成配置文件:
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```bash
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# -l或者--language字段是必须的
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# --train修改训练集，--val修改验证集，--data_dir修改数据集目录，--dict修改字典路径， -o修改对应默认参数
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cd PaddleOCR/configs/rec/multi_language/
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python3 generate_multi_language_configs.py -l it \ # 语种
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--train {path/of/train_label.txt} \ # 训练标签文件的路径
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--val {path/of/val_label.txt} \ # 验证集标签文件的路径
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--data_dir {train_data/path} \ # 训练数据的根目录
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--dict {path/of/dict} \ # 字典文件路径
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-o Global.use_gpu=False # 是否使用gpu
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...
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```
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意大利文由拉丁字母组成，因此执行完命令后会得到名为 rec_latin_lite_train.yml 的配置文件。
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2. 手动修改配置文件
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您也可以手动修改模版中的以下几个字段:
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```
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Global:
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use_gpu: True
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epoch_num: 500
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...
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character_type: it # 需要识别的语种
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character_dict_path: {path/of/dict} # 字典文件所在路径
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ # 数据存放根目录
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label_file_list: ["./train_data/train_list.txt"] # 训练集label路径
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...
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ # 数据存放根目录
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label_file_list: ["./train_data/val_list.txt"] # 验证集label路径
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...
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```
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目前PaddleOCR支持的多语言算法有：
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| 配置文件 | 算法名称 | backbone | trans | seq | pred | language | character_type |
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| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: |
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| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 中文繁体 | chinese_cht|
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| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语（区分大小写） | EN |
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| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 | french |
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| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 | german |
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| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 | japan |
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| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 | korean |
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| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 拉丁字母 | latin |
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| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 阿拉伯字母 | ar |
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| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 斯拉夫字母 | cyrillic |
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| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 梵文字母 | devanagari |
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更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)
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多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以通过下面两种方式下载。
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* [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA)。提取码：frgi。
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* [google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
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@ -51,7 +51,7 @@ Take rec_chinese_lite_train_v2.0.yml as an example
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### Architecture ([ppocr/modeling](../../ppocr/modeling))
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In ppocr, the network is divided into four stages: Transform, Backbone, Neck and Head
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In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck and Head
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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@ -120,3 +120,107 @@ In ppocr, the network is divided into four stages: Transform, Backbone, Neck and
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| batch_size_per_card | Single card batch size during training | 256 | \ |
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| drop_last | Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size | True | \ |
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| num_workers | The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process | 8 | \ |
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## 3. Multi-language config yml file generation
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PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
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provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
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There are two ways to create the required configuration file:：
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1. Automatically generated by script
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[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models
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- Take Italian as an example, if your data is prepared in the following format:
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```
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|-train_data
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|- it_train.txt # train_set label
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|- it_val.txt # val_set label
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|- data
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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You can use the default parameters to generate a configuration file:
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```bash
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# The code needs to be run in the specified directory
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cd PaddleOCR/configs/rec/multi_language/
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# Set the configuration file of the language to be generated through the -l or --language parameter.
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# This command will write the default parameters into the configuration file
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python3 generate_multi_language_configs.py -l it
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```
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- If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters:
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```bash
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# -l or --language field is required
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# --train to modify the training set
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# --val to modify the validation set
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# --data_dir to modify the data set directory
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# --dict to modify the dict path
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# -o to modify the corresponding default parameters
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cd PaddleOCR/configs/rec/multi_language/
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python3 generate_multi_language_configs.py -l it \ # language
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--train {path/of/train_label.txt} \ # path of train_label
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--val {path/of/val_label.txt} \ # path of val_label
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--data_dir {train_data/path} \ # root directory of training data
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--dict {path/of/dict} \ # path of dict
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-o Global.use_gpu=False # whether to use gpu
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...
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```
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Italian is made up of Latin letters, so after executing the command, you will get the rec_latin_lite_train.yml.
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2. Manually modify the configuration file
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You can also manually modify the following fields in the template:
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```
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Global:
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use_gpu: True
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epoch_num: 500
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...
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character_type: it # language
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character_dict_path: {path/of/dict} # path of dict
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ # root directory of training data
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label_file_list: ["./train_data/train_list.txt"] # train label path
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...
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ # root directory of val data
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label_file_list: ["./train_data/val_list.txt"] # val label path
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...
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```
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Currently, the multi-language algorithms supported by PaddleOCR are:
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| Configuration file | Algorithm name | backbone | trans | seq | pred | language | character_type |
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| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: |
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| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | chinese traditional | chinese_cht|
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| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English(Case sensitive) | EN |
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| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French | french |
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| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German | german |
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| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese | japan |
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| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean | korean |
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| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Latin | latin |
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| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | arabic | ar |
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| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | cyrillic | cyrillic |
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| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | devanagari | devanagari |
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For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
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The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
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* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
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* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
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