!567 fix resnet url in reamme

Merge pull request !567 from qujianwei/master

official/cv/faster_rcnn/README.md

@@ -101,8 +101,8 @@ After installing MindSpore via the official website, you can start training and
 Note:
 
 1. the first run will generate the mindeocrd file, which will take a long time.
-2. pretrained model is a resnet50 checkpoint that trained over ImageNet2012.you can train it with [resnet50](https://gitee.com/qujianwei/mindspore/tree/master/model_zoo/official/cv/resnet) scripts in modelzoo, and use src/convert_checkpoint.py to get the pretrain model.
-3. BACKBONE_MODEL is a checkpoint file trained with [resnet50](https://gitee.com/qujianwei/mindspore/tree/master/model_zoo/official/cv/resnet) scripts in modelzoo.PRETRAINED_MODEL is a checkpoint file after convert.VALIDATION_JSON_FILE is label file. CHECKPOINT_PATH is a checkpoint file after trained.
+2. pretrained model is a resnet50 checkpoint that trained over ImageNet2012.you can train it with [resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) scripts in modelzoo, and use src/convert_checkpoint.py to get the pretrain model.
+3. BACKBONE_MODEL is a checkpoint file trained with [resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) scripts in modelzoo.PRETRAINED_MODEL is a checkpoint file after convert.VALIDATION_JSON_FILE is label file. CHECKPOINT_PATH is a checkpoint file after trained.
 
 ## Run on Ascend
 

official/cv/faster_rcnn/README_CN.md

@@ -102,8 +102,8 @@ Faster R-CNN是一个两阶段目标检测网络，该网络采用RPN，可以
 注意：
 
 1. 第一次运行生成MindRecord文件，耗时较长。
-2. 预训练模型是在ImageNet2012上训练的ResNet-50检查点。你可以使用ModelZoo中 [resnet50](https://gitee.com/qujianwei/mindspore/tree/master/model_zoo/official/cv/resnet) 脚本来训练, 然后使用src/convert_checkpoint.py把训练好的resnet50的权重文件转换为可加载的权重文件。
-3. BACKBONE_MODEL是通过modelzoo中的[resnet50](https://gitee.com/qujianwei/mindspore/tree/master/model_zoo/official/cv/resnet)脚本训练的。PRETRAINED_MODEL是经过转换后的权重文件。VALIDATION_JSON_FILE为标签文件。CHECKPOINT_PATH是训练后的检查点文件。
+2. 预训练模型是在ImageNet2012上训练的ResNet-50检查点。你可以使用ModelZoo中 [resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) 脚本来训练, 然后使用src/convert_checkpoint.py把训练好的resnet50的权重文件转换为可加载的权重文件。
+3. BACKBONE_MODEL是通过modelzoo中的[resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/resnet)脚本训练的。PRETRAINED_MODEL是经过转换后的权重文件。VALIDATION_JSON_FILE为标签文件。CHECKPOINT_PATH是训练后的检查点文件。
 
 ## 在Ascend上运行
 

official/cv/maskrcnn/README.md

@@ -115,7 +115,7 @@ pip install mmcv=0.2.14
     Note:
     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/models/tree/master/utils/hccl_tools) with JSON format needs to be created in advance.
-    3. PRETRAINED_CKPT is a resnet50 checkpoint that trained over ImageNet2012.you can train it with [resnet50](https://gitee.com/qujianwei/mindspore/tree/master/model_zoo/official/cv/resnet) scripts in modelzoo, and use src/convert_checkpoint.py to get the pretrain checkpoint file.
+    3. PRETRAINED_CKPT is a resnet50 checkpoint that trained over ImageNet2012.you can train it with [resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) scripts in modelzoo, and use src/convert_checkpoint.py to get the pretrain checkpoint file.
     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.

official/cv/maskrcnn/README_CN.md

@@ -117,7 +117,7 @@ pip install mmcv=0.2.14
     注：
     1. 为加快数据预处理速度，MindSpore提供了MindRecord数据格式。因此，训练前首先需要生成基于COCO2017数据集的MindRecord文件。COCO2017原始数据集转换为MindRecord格式大概需要4小时。
     2. 进行分布式训练前，需要提前创建JSON格式的[hccl配置文件](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools)。
-    3. PRETRAINED_CKPT是一个ResNet50检查点，通过ImageNet2012训练。你可以使用ModelZoo中 [resnet50](https://gitee.com/qujianwei/mindspore/tree/master/model_zoo/official/cv/resnet) 脚本来训练, 然后使用src/convert_checkpoint.py把训练好的resnet50的权重文件转换为可加载的权重文件。
+    3. PRETRAINED_CKPT是一个ResNet50检查点，通过ImageNet2012训练。你可以使用ModelZoo中 [resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) 脚本来训练, 然后使用src/convert_checkpoint.py把训练好的resnet50的权重文件转换为可加载的权重文件。
 
 4. 执行评估脚本。
    训练结束后，按照如下步骤启动评估：