diff --git a/official/cv/resnet/README.md b/official/cv/resnet/README.md
index 7570284e04807999f8f8f238b84a37a8ac872f5f..2e606ac978a45f10e046bc83d959fa0ac66541c4 100644
--- a/official/cv/resnet/README.md
+++ b/official/cv/resnet/README.md
@@ -616,12 +616,12 @@ epoch: [0/1] step: [100/5004], loss is 6.814013Epoch time: 3437.154 ms, fps: 148
 
 ```bash
 # evaluation
-Usage: bash run_eval.sh [DATASET_PATH] [CONFIG_PATH] [CHECKPOINT_PATH]
+Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
 ```bash
 # evaluation example
-bash run_eval.sh resnet50 cifar10 ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt --config_path /.yaml
+bash run_eval.sh ~/cifar10-10-verify-bin /resnet50_cifar10/train_parallel0/resnet-90_195.ckpt config/resnet50_cifar10_config.yaml
 ```
 
 > checkpoint can be produced in training process.
@@ -726,7 +726,7 @@ Export on ModelArts (If you want to run in modelarts, please check the official
 ### Infer on Ascend310
 
 Before performing inference, the mindir file must bu exported by `export.py` script. We only provide an example of inference using MINDIR model.
-Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits.
+Current batch_Size can only be set to 1.
 
 ```shell
 # Ascend310 inference
@@ -813,7 +813,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 4 mins                          | 11 minds    |
 | Parameters (M)             | 11.2                                                        | 11.2          |
 | Checkpoint for Fine tuning | 86M (.ckpt file)                                         | 85.4 (.ckpt file)     |
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ResNet18 on ImageNet2012
 
@@ -833,7 +833,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 110 mins                        | 130 mins            |
 | Parameters (M)             | 11.7                                                       | 11.7 |
 | Checkpoint for Fine tuning | 90M (.ckpt file)                                         |  90M (.ckpt file)                                         |
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ResNet50 on CIFAR-10
 
@@ -853,7 +853,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 6 mins                          | 20.2 mins|
 | Parameters (M)             | 25.5                                                         | 25.5 |
 | Checkpoint for Fine tuning | 179.7M (.ckpt file)                                         |179.7M (.ckpt file)|
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ResNet50 on ImageNet2012
 
@@ -873,12 +873,12 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 114 mins                          | 260 mins|
 | Parameters (M)             | 25.5                                                         | 25.5 |
 | Checkpoint for Fine tuning | 197M (.ckpt file)                                         |197M (.ckpt file)     |
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ResNet34 on ImageNet2012
 
 | Parameters                 | Ascend 910                                                   |
-| -------------------------- | -------------------------------------- |---------------------------------- |
+| -------------------------- | -------------------------------------- |
 | Model Version              | ResNet50-v1.5                                                |
 | Resource                   | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8  |
 | uploaded Date              | 07/05/2020 (month/day/year)  锛�                        |
@@ -893,7 +893,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 112 mins                          |
 | Parameters (M)             | 20.79                                                         |
 | Checkpoint for Fine tuning | 166M (.ckpt file)                                         |
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ResNet101 on ImageNet2012
 
@@ -913,7 +913,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 301 mins                          | 1100 mins|
 | Parameters (M)             | 44.6                                                        | 44.6 |
 | Checkpoint for Fine tuning | 343M (.ckpt file)                                         |343M (.ckpt file)     |
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ResNet152 on ImageNet2012
 
@@ -933,7 +933,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time   |  577 mins |
 | Parameters(M)   | 60.19 |
 | Checkpoint for Fine tuning | 462M锛�.ckpt file锛�  |
-| Scripts  | [Link](https://gitee.com/panpanrui/mindspore/tree/master/model_zoo/official/cv/resnet152)  |
+| config  | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config)  |
 
 #### SE-ResNet50 on ImageNet2012
 
@@ -953,7 +953,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Total time                 | 49.3 mins                                                  |
 | Parameters (M)             | 25.5                                                         |
 | Checkpoint for Fine tuning | 215.9M (.ckpt file)                                         |
-| Scripts                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| config                    | [Link](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 ### Inference Performance
 
@@ -969,7 +969,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 32                          |
 | outputs             | probability                 |
 | Accuracy            | 94.02%                      |
-| Model for inference | 43M (.air file)             |
+| Model for inference | 43M (.mindir file)             |
 
 #### ResNet18 on ImageNet2012
 
@@ -983,7 +983,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 256                         |
 | outputs             | probability                 |
 | Accuracy            | 70.53%                      |
-| Model for inference | 45M (.air file)             |
+| Model for inference | 45M (.mindir file)             |
 
 #### ResNet34 on ImageNet2012
 
@@ -997,7 +997,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 256                         |
 | outputs             | probability                 |
 | Accuracy            | 73.67%                      |
-| Model for inference | 70M (.air file)             |
+| Model for inference | 70M (.mindir file)             |
 
 #### ResNet50 on CIFAR-10
 
@@ -1011,7 +1011,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 32                          | 32                          |
 | outputs             | probability                 | probability                 |
 | Accuracy            | 91.44%                      | 91.37%                      |
-| Model for inference | 91M (.air file)         |  |
+| Model for inference | 91M (.mindir file)         |  |
 
 #### ResNet50 on ImageNet2012
 
@@ -1025,7 +1025,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 256                         | 256                          |
 | outputs             | probability                 | probability                 |
 | Accuracy            | 76.70%                      | 76.74%                      |
-| Model for inference | 98M (.air file)         |  |
+| Model for inference | 98M (.mindir file)         |  |
 
 #### ResNet101 on ImageNet2012
 
@@ -1039,7 +1039,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 32                          | 32                          |
 | outputs             | probability                 | probability                 |
 | Accuracy            | 78.53%                      | 78.64%                      |
-| Model for inference | 171M (.air file)         |  |
+| Model for inference | 171M (.mindir file)         |  |
 
 #### ResNet152 on ImageNet2012
 
@@ -1053,7 +1053,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 32                          |
 | outputs             | probability                 |
 | Accuracy            | 78.60%                      |
-| Model for inference | 236M (.air file)            |
+| Model for inference | 236M (.mindir file)            |
 
 #### SE-ResNet50 on ImageNet2012
 
@@ -1067,7 +1067,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | batch_size          | 32                          |
 | outputs             | probability                 |
 | Accuracy            | 76.80%                      |
-| Model for inference | 109M (.air file)            |
+| Model for inference | 109M (.mindir file)            |
 
 # [Description of Random Situation](#contents)
 
diff --git a/official/cv/resnet/README_CN.md b/official/cv/resnet/README_CN.md
index 64d70484d17397de889819c379d5a7194330e683..c31941796ff7884d6f4b379f239da06b40a84f4a 100644
--- a/official/cv/resnet/README_CN.md
+++ b/official/cv/resnet/README_CN.md
@@ -587,7 +587,7 @@ Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
 
 ```bash
 # 璇勪及绀轰緥
-bash run_eval.sh resnet50 cifar10 ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt --config_path /*.yaml
+bash run_eval.sh ~/cifar10-10-verify-bin  /resnet50_cifar10/train_parallel0/resnet-90_195.ckpt config/resnet50_cifar10_config.yaml
 ```
 
 > 璁粌杩囩▼涓彲浠ョ敓鎴愭鏌ョ偣銆�
@@ -689,7 +689,7 @@ ModelArts瀵煎嚭mindir
 ### 鍦ˋscend310鎵ц鎺ㄧ悊
 
 鍦ㄦ墽琛屾帹鐞嗗墠锛宮indir鏂囦欢蹇呴』閫氳繃`export.py`鑴氭湰瀵煎嚭銆備互涓嬪睍绀轰簡浣跨敤minir妯″瀷鎵ц鎺ㄧ悊鐨勭ず渚嬨€�
-鐩墠浠呮敮鎸乥atch_Size涓�1鐨勬帹鐞嗐€傜簿搴﹁绠楄繃绋嬮渶瑕�70G+鐨勫唴瀛橈紝鍚﹀垯杩涚▼灏嗕細鍥犱负瓒呭嚭鍐呭瓨琚郴缁熺粓姝€€�
+鐩墠浠呮敮鎸乥atch_Size涓�1鐨勬帹鐞嗐€�
 
 ```shell
 # Ascend310 inference
@@ -776,7 +776,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 4鍒嗛挓                          | 11鍒嗛挓       |
 | 鍙傛暟(M)             | 11.2                                                         | 11.2                         |
 | 寰皟妫€鏌ョ偣 | 86锛�.ckpt鏂囦欢锛�                                         |
-| 鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| 閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ImageNet2012涓婄殑ResNet18
 
@@ -796,7 +796,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 110鍒嗛挓                          | 130鍒嗛挓       |
 | 鍙傛暟(M)             | 11.7                                                         | 11.7 |
 | 寰皟妫€鏌ョ偣| 90M锛�.ckpt鏂囦欢锛�                                         |  90M锛�.ckpt鏂囦欢锛� |
-| 鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| 閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### CIFAR-10涓婄殑ResNet50
 
@@ -816,7 +816,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 6鍒嗛挓                          | 20.2鍒嗛挓|
 | 鍙傛暟(M)             | 25.5                                                         | 25.5 |
 | 寰皟妫€鏌ョ偣 | 179.7M锛�.ckpt鏂囦欢锛�                                         | 179.7M锛�.ckpt鏂囦欢锛� |
-| 鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| 閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ImageNet2012涓婄殑ResNet50
 
@@ -836,7 +836,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 114鍒嗛挓                          | 500鍒嗛挓|
 | 鍙傛暟(M)             | 25.5                                                         | 25.5 |
 | 寰皟妫€鏌ョ偣| 197M锛�.ckpt鏂囦欢锛�                                         | 197M锛�.ckpt鏂囦欢锛�     |
-| 鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| 閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ImageNet2012涓婄殑ResNet34
 
@@ -856,7 +856,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 112鍒嗛挓                          |
 | 鍙傛暟(M)             | 20.79                                                         |
 | 寰皟妫€鏌ョ偣| 166M锛�.ckpt鏂囦欢锛�                                         |
-| 鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+| 閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config)|
 
 #### ImageNet2012涓婄殑ResNet101
 
@@ -876,7 +876,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 301鍒嗛挓                          | 1100鍒嗛挓|
 | 鍙傛暟(M)             | 44.6                                                        | 44.6 |
 | 寰皟妫€鏌ョ偣| 343M锛�.ckpt鏂囦欢锛�                                         | 343M锛�.ckpt鏂囦欢锛�     |
-|鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+|閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 #### ImageNet2012涓婄殑ResNet152
 
@@ -896,7 +896,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 |鎬绘椂闀�   |  577鍒嗛挓 |
 |鍙傛暟(M)   | 60.19 |
 |  寰皟妫€鏌ョ偣 | 462M锛�.ckpt鏂囦欢锛�  |
-| 鑴氭湰  | [閾炬帴](https://gitee.com/panpanrui/mindspore/tree/master/model_zoo/official/cv/resnet152)  |
+| 閰嶇疆鏂囦欢  | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config)  |
 
 #### ImageNet2012涓婄殑SE-ResNet50
 
@@ -916,7 +916,7 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 鎬绘椂闀�                 | 49.3鍒嗛挓                                                  |
 | 鍙傛暟(M)             | 25.5                                                         |
 | 寰皟妫€鏌ョ偣 | 215.9M 锛�.ckpt鏂囦欢锛�                                         |
-|鑴氭湰                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet) |
+|閰嶇疆鏂囦欢                    | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
 
 # 闅忔満鎯呭喌璇存槑