diff --git a/benchmark/run_det.sh b/benchmark/run_det.sh
index 4631f6ff0a60296cebf03821ecd17cd01d33664a..c94af85c365d66b2e0f0a143f14f0340d2f56a73 100644
--- a/benchmark/run_det.sh
+++ b/benchmark/run_det.sh
@@ -1,10 +1,11 @@
 # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7  paddle=2.1.2  py=37
 # 执行目录：需说明
-# cd PaddleOCR
+cd PaddleOCR
 # 1 安装该模型需要的依赖 (如需开启优化策略请注明)
-# python3.7 -m pip install -r requirements.txt
+python3.7 -m pip install -r requirements.txt
 # 2 拷贝该模型需要数据、预训练模型
-# wget -p ./tain_data/ xxxxx
+wget -p ./tain_data/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data  && tar xf icdar2015.tar && cd ../
+wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
 # 3 批量运行（如不方便批量，1，2需放到单个模型中）
 
 model_mode_list=(det_mv3_db det_r50_vd_east)
@@ -15,11 +16,11 @@ for model_mode in ${model_mode_list[@]}; do
           for bs_item in ${bs_list[@]}; do
             echo "index is speed, 1gpus, begin, ${model_name}"
             run_mode=sp
-            CUDA_VISIBLE_DEVICES=7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}     #  (5min)
+            CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}     #  (5min)
             sleep 60
             echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
             run_mode=mp
-            CUDA_VISIBLE_DEVICES=6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} 
+            CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} 
             sleep 60
             done
       done
diff --git a/configs/det/det_r50_vd_east.yml b/configs/det/det_r50_vd_east.yml
index 0253c5bd9940fa6c0ec7da2c6639c1bc060842ca..e84a5fa7a7af34bde5e0abc6fed2e01f6ce42e6b 100644
--- a/configs/det/det_r50_vd_east.yml
+++ b/configs/det/det_r50_vd_east.yml
@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
   cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/
+  pretrained_model: ./pretrain_models/ResNet50_vd_pretrained
   checkpoints: 
   save_inference_dir:
   use_visualdl: False