use pretrained_model for eval
diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md
index a8dee65a22..671fda998d 100644
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -108,9 +108,9 @@ 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=0.5`，`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
-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。
+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.pretrained_model`指向保存的参数文件。
 ```shell
-python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.5 PostProcess.unclip_ratio=1.5
+python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.pretrained_model="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.5 PostProcess.unclip_ratio=1.5
 ```
 
 
diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md
index 028a248fe6..faa015b754 100644
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@@ -420,8 +420,8 @@ Eval:
 评估数据集可以通过 `configs/rec/rec_icdar15_train.yml`  修改Eval中的 `label_file_path` 设置。
 
 ```
-# GPU 评估， Global.checkpoints 为待测权重
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+# GPU 评估， Global.pretrained_model 为待测权重
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
 ```
 
 <a name="预测"></a>
@@ -432,7 +432,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
 
 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
 
-默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重：
+默认预测图片存储在 `infer_img` 里，通过 `-o Global.pretrained_model` 指定权重：
 
 ```
 # 预测英文结果
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index 3ee9092cc6..897f5b3b09 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -101,9 +101,9 @@ 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.
 
-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.
+The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.pretrained_model` to point to the saved parameter file.
 ```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.yml  -o Global.pretrained_model="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index 73157f864c..67eece7e85 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -425,8 +425,8 @@ Eval:
 The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
 
 ```
-# GPU evaluation, Global.checkpoints is the weight to be tested
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+# GPU evaluation, Global.pretrained_model is the weight to be tested
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
 ```
 
 <a name="PREDICTION"></a>
@@ -437,7 +437,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
 
 Using the model trained by paddleocr, you can quickly get prediction through the following script.
 
-The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
+The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.pretrained_model`:
 
 ```
 # Predict English results

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