diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md
index c2b62edbee7ae855cd32b03cc0019027fb05f669..3945b7f0b996a18cfdb943c3ec68ce43fa53e6f3 100644
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -44,13 +44,15 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 
 ## 快速启动训练
 
-首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
+首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列，
 您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
 ```shell
 cd PaddleOCR/
 # 下载MobileNetV3的预训练模型
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
-# 下载ResNet50的预训练模型
+# 或，下载ResNet18_vd的预训练模型
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+# 或，下载ResNet50_vd的预训练模型
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
 
 # 解压预训练模型文件，以MobileNetV3为例
@@ -72,24 +74,24 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
 
 ```shell
 # 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 \ 
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.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_v1.1.yml配置文件。
 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
 ```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.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：
 ```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./your/trained/model
 ```
 
 **注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
@@ -98,17 +100,17 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
 
 PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
 
-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。
+运行如下代码，根据配置文件`det_db_mv3_v1.1.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
 ```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
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.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：
 ```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
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml  -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 * 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置
@@ -117,16 +119,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./ou
 
 测试单张图像的检测效果
 ```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"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.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，
 ```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
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.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
 ```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"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
 ```
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index 401d7a9ad479716a6d6694ca1f432a2c934def88..96928364486c3aa122806ad5144681dfde785556 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -38,12 +38,14 @@ 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.
+First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd 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
-# Download the pre-trained model of ResNet50
+# or, download the pre-trained model of ResNet18_vd
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+# or, download the pre-trained model of ResNet50_vd
 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
@@ -62,7 +64,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
 #### 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 2>&1 | tee train_det.log
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.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.
@@ -70,7 +72,7 @@ For a detailed explanation of the configuration file, please refer to [config](.
 
 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
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Optimizer.base_lr=0.0001
 ```
 
 #### load trained model and continue training
@@ -78,7 +80,7 @@ If you expect to load trained model and continue the training again, you can spe
 
 For example:
 ```shell
-python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
+python3 tools/train.py -c configs/det/det_mv3_db_v1.1.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.
@@ -88,18 +90,18 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
 
 PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.
 
-Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by `save_res_path` in the configuration file `det_db_mv3.yml`
+Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by `save_res_path` in the configuration file `det_db_mv3_v1.1.yml`
 
 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
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.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.
 
 Such as:
 ```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
+python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml  -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 * Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.
@@ -108,16 +110,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./ou
 
 Test the detection result on a single image:
 ```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"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
 ```
 
 When testing the DB model, adjust the post-processing threshold:
 ```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
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.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
 ```
 
 
 Test the detection result on all images in the folder:
 ```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"
+python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
 ```