yolov3 resnet18 add dataset note

official/cv/yolov3_resnet18/README.md

@@ -41,7 +41,7 @@ And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResN
 
 Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
 
-Dataset used: [COCO2017](<http://images.cocodataset.org/>)
+Dataset used: [COCO2017](<http://images.cocodataset.org/>), labeling uses unpublished face, human body labeling data, temporarily does not provide public reproduction, only provides pre-training [checkpoint](https://download.mindspore.cn/model_zoo/r1.2/yolov3resnet18_ascend_v120_coco2017_official_cv_bs32_acc86/yolov3resnet18_ascend_v120_coco2017_official_cv_bs32_acc86.ckpt).
 
 - Dataset size：19G
     - Train：18G，118000 images  

official/cv/yolov3_resnet18/README_CN.md

@@ -43,7 +43,7 @@ YOLOv3整体网络架构如下：
 
 # 数据集
 
-使用的数据集：[COCO 2017](<http://images.cocodataset.org/>)
+使用的数据集：[COCO 2017](<http://images.cocodataset.org/>)，标注使用未公开的人脸/人体标注数据，暂时不提供公开复现，仅提供预训练[checkpoint](https://download.mindspore.cn/model_zoo/r1.2/yolov3resnet18_ascend_v120_coco2017_official_cv_bs32_acc86/yolov3resnet18_ascend_v120_coco2017_official_cv_bs32_acc86.ckpt).
 
 - 数据集大小：19 GB
     - 训练集：18 GB，118000张图片