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update doc for saving log

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tink2123 2020-09-15 20:20:10 +08:00
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doc/doc_ch/customize.md
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@ -6,7 +6,7 @@
PaddleOCR提供了EAST、DB两种文本检测算法，均支持MobileNetV3、ResNet50_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的DB检测模型（即超轻量模型使用的配置）： PaddleOCR提供了EAST、DB两种文本检测算法，均支持MobileNetV3、ResNet50_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的DB检测模型（即超轻量模型使用的配置）：
``` ```
python3 tools/train.py -c configs/det/det_mv3_db.yml python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
``` ```
更详细的数据准备和训练教程参考文档教程中[文本检测模型训练/评估/预测](./detection.md)。 更详细的数据准备和训练教程参考文档教程中[文本检测模型训练/评估/预测](./detection.md)。
@ -14,7 +14,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml
PaddleOCR提供了CRNN、Rosetta、STAR-Net、RARE四种文本识别算法，均支持MobileNetV3、ResNet34_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的CRNN识别模型（即超轻量模型使用的配置）： PaddleOCR提供了CRNN、Rosetta、STAR-Net、RARE四种文本识别算法，均支持MobileNetV3、ResNet34_vd两种骨干网络，根据需要选择相应的配置文件，启动训练。例如，训练使用MobileNetV3作为骨干网络的CRNN识别模型（即超轻量模型使用的配置）：
``` ```
python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
``` ```
更详细的数据准备和训练教程参考文档教程中[文本识别模型训练/评估/预测](./recognition.md)。 更详细的数据准备和训练教程参考文档教程中[文本识别模型训练/评估/预测](./recognition.md)。

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doc/doc_ch/detection.md
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@ -62,7 +62,10 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
```shell ```shell
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ 训练 mv3_db 模型，并将训练日志保存为 tain_det.log
python3 tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ \
2>&1 | tee train_det.log
``` ```
上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。 上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。

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doc/doc_ch/recognition.md
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@ -128,8 +128,8 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
export PYTHONPATH=$PYTHONPATH:. export PYTHONPATH=$PYTHONPATH:.
# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号 # GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
export CUDA_VISIBLE_DEVICES=0,1,2,3 export CUDA_VISIBLE_DEVICES=0,1,2,3
# 训练icdar15英文数据 # 训练icdar15英文数据 并将训练日志保存为 tain_rec.log
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
``` ```
- 数据增强 - 数据增强

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doc/doc_en/customize_en.md
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@ -6,7 +6,7 @@ The process of making a customized ultra-lightweight OCR models can be divided i
PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model : PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model :
``` ```
python3 tools/train.py -c configs/det/det_mv3_db.yml python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee det_db.log
``` ```
For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md) For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md)
@ -14,7 +14,7 @@ For more details about data preparation and training tutorials, refer to the doc
PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network: PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network:
``` ```
python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml 2>&1 | tee rec_ch_lite.log
``` ```
For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md) For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md)

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doc/doc_en/detection_en.md
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@ -62,7 +62,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
#### START TRAINING #### START TRAINING
*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.* *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
```shell ```shell
python3 tools/train.py -c configs/det/det_mv3_db.yml python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee train_det.log
``` ```
In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file. In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file.

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doc/doc_en/recognition_en.md
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@ -130,8 +130,8 @@ Start training:
export PYTHONPATH=$PYTHONPATH:. export PYTHONPATH=$PYTHONPATH:.
# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES # GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
export CUDA_VISIBLE_DEVICES=0,1,2,3 export CUDA_VISIBLE_DEVICES=0,1,2,3
# Training icdar15 English data # Training icdar15 English data and saving the log as train_rec.log
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
``` ```
- Data Augmentation - Data Augmentation