diff --git a/model_zoo/official/cv/maskrcnn/README.md b/model_zoo/official/cv/maskrcnn/README.md
index d876e9cdce4..7f16c5ead21 100644
--- a/model_zoo/official/cv/maskrcnn/README.md
+++ b/model_zoo/official/cv/maskrcnn/README.md
@@ -101,6 +101,8 @@ pip install mmcv=0.2.14
     1. To speed up data preprocessing, MindSpore provide a data format named MindRecord, hence the first step is to generate MindRecord files based on COCO2017 dataset before training. The process of converting raw COCO2017 dataset to MindRecord format may take about 4 hours.
     2. For distributed training, a [hccl configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools) with JSON format needs to be created in advance.
     3. PRETRAINED_CKPT is a resnet50 checkpoint that trained over ImageNet2012.
+    4. For large models like MaskRCNN, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+
 
 4. Execute eval script.
    After training, you can start evaluation as follows: