update docs

doc/doc_ch/detection.md

@@ -61,7 +61,7 @@ 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*
 
-```python
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
 ```
 
@@ -69,14 +69,14 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=
 有关配置文件的详细解释，请参考[链接](./config.md)。
 
 您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
-```python
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```
 
 #### 断点训练
 
 如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
-```python
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
 ```
 
@@ -89,13 +89,13 @@ PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall
 运行如下代码，根据配置文件`det_db_mv3.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
 
 评估时设置后处理参数`box_thresh=0.6`，`unclip_ratio=1.5`，使用不同数据集、不同模型训练，可调整这两个参数进行优化
-```python
+```shell
 python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。
 
 比如：
-```python
+```shell
 python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
@@ -104,17 +104,17 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./ou
 ## 测试检测效果
 
 测试单张图像的检测效果
-```python
+```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
 ```
 
 测试DB模型时，调整后处理阈值，
-```python
+```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 
 测试文件夹下所有图像的检测效果
-```python
+```shell
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
 ```

doc/doc_en/detection_en.md

@@ -6,7 +6,7 @@ This section uses the icdar2015 dataset as an example to introduce the training,
 The icdar2015 dataset can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading.
 
 Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:
-```
+```shell
 # Under the PaddleOCR path
 cd PaddleOCR/
 wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt
@@ -39,7 +39,7 @@ If you want to train PaddleOCR on other datasets, please build the annotation fi
 ## TRAINING
 
 First download the pretrained model. The detection model of PaddleOCR currently supports two backbones, namely MobileNetV3 and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
-```
+```shell
 cd PaddleOCR/
 # Download the pre-trained model of MobileNetV3
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
@@ -47,7 +47,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 
 # decompressing the pre-training model file, take MobileNetV3 as an example
-tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
+tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
 
 # Note: After decompressing the backbone pre-training weight file correctly, the file list in the folder is as follows:
 ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
@@ -61,7 +61,7 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models
 
 #### START TRAINING
 *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml
 ```
 
@@ -69,7 +69,7 @@ In the above instruction, use `-c` to select the training to use the `configs/de
 For a detailed explanation of the configuration file, please refer to [config](./config_en.md).
 
 You can also use `-o` to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```
 
@@ -77,11 +77,11 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
 
 For example:
-```
+```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
 ```
 
-**Note**:The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by Global.checkpoints will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
+**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
 
 
 ## EVALUATION
@@ -92,7 +92,7 @@ Run the following code to calculate the evaluation indicators. The result will b
 
 When evaluating, set post-processing parameters `box_thresh=0.6`, `unclip_ratio=1.5`. If you use different datasets, different models for training, these two parameters should be adjusted for better result.
 
-```
+```shell
 python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file.