diff --git a/model_zoo/official/cv/mobilenetv2_quant/Readme.md b/model_zoo/official/cv/mobilenetv2_quant/Readme.md
index 86024a28549..5a22ea8eea5 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/Readme.md
+++ b/model_zoo/official/cv/mobilenetv2_quant/Readme.md
@@ -20,7 +20,6 @@
 
 # [MobileNetV2 Description](#contents)
 
-
 MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
 
 [Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
@@ -38,11 +37,10 @@ The overall network architecture of MobileNetV2 is show below:
 Dataset used: [imagenet](http://www.image-net.org/)
 
 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
-	- Train: 120G, 1.2W images
-	- Test: 5G, 50000 images
+    - Train: 120G, 1.2W images
+    - Test: 5G, 50000 images
 - Data format: RGB images.
-	- Note: Data will be processed in src/dataset.py
-
+  - Note: Data will be processed in src/dataset.py
 
 # [Features](#contents)
 
@@ -54,14 +52,13 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware:Ascend
-  - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below
   - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
   - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
-
 # [Script description](#contents)
 
 ## [Script and sample code](#contents)
@@ -84,7 +81,6 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
   ├── export.py     # export checkpoint files into air/onnx
 ```
 
-
 ## [Script Parameters](#contents)
 
 Parameters for both training and evaluation can be set in config.py
@@ -113,13 +109,11 @@ Parameters for both training and evaluation can be set in config.py
 
 ### Usage
 
-
 You can start training using python or shell scripts. The usage of shell scripts as follows:
 
 - bash run_train.sh [Ascend] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)
 - bash run_train.sh [GPU] [DEVICE_ID_LIST] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)
 
-
 ### Launch
 
 ``` bash
@@ -133,7 +127,7 @@ You can start training using python or shell scripts. The usage of shell scripts
 Training result will be stored in the example path. Checkpoints trained by `Ascend` will be stored at `./train/device$i/checkpoint` by default, and training log  will be redirected to `./train/device$i/train.log`. Checkpoints trained by `GPU` will be stored in `./train/checkpointckpt_$i` by default, and training log will be redirected to `./train/train.log`.  
 `train.log` is as follows:
 
-```
+``` bash
 epoch: [  0/200], step:[  624/  625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
 epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
 epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
@@ -150,7 +144,7 @@ You can start training using python or shell scripts. The usage of shell scripts
 
 ### Launch
 
-```
+``` bash
 # infer example
   shell:
       Ascend: sh run_infer_quant.sh Ascend ~/imagenet/val/ ~/train/mobilenet-60_1601.ckpt
@@ -160,9 +154,9 @@ You can start training using python or shell scripts. The usage of shell scripts
 
 ### Result
 
-Inference result will be stored in the example path, you can find result like the followings in `./val/infer.log`.
+Inference result will be stored in the example path, you can find result like the following in `./val/infer.log`.
 
-```
+``` bash
 result: {'acc': 0.71976314102564111}
 ```
 
diff --git a/model_zoo/official/cv/mobilenetv2_quant/src/launch.py b/model_zoo/official/cv/mobilenetv2_quant/src/launch.py
index 0d05ee9ad78..48b70d77fa7 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/src/launch.py
+++ b/model_zoo/official/cv/mobilenetv2_quant/src/launch.py
@@ -35,7 +35,7 @@ def parse_args():
         >>> parse_args()
     """
     parser = ArgumentParser(description="mindspore distributed training launch "
-                                        "helper utilty that will spawn up "
+                                        "helper utility that will spawn up "
                                         "multiple distributed processes")
     parser.add_argument("--nproc_per_node", type=int, default=1,
                         help="The number of processes to launch on each node, "
diff --git a/model_zoo/official/cv/resnet50_quant/README.md b/model_zoo/official/cv/resnet50_quant/README.md
index e814b1e7848..2c08da8b993 100644
--- a/model_zoo/official/cv/resnet50_quant/README.md
+++ b/model_zoo/official/cv/resnet50_quant/README.md
@@ -37,19 +37,18 @@ The overall network architecture of Resnet50 is show below:
 Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 - Dataset size 224*224 colorful images in 1000 classes
-  - Train：1,281,167 images  
-  - Test： 50,000 images   
+    - Train：1,281,167 images
+    - Test： 50,000 images
 - Data format：jpeg
-  - Note：Data will be processed in dataset.py
+   - Note：Data will be processed in dataset.py
 - Download the dataset, the directory structure is as follows:
 
- ```
+ ```python
 └─dataset
     ├─ilsvrc                # train dataset
     └─validation_preprocess # evaluate dataset
 ```
 
-
 # [Features](#contents)
 
 ## [Mixed Precision](#contents)
@@ -60,14 +59,13 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware:Ascend
-  - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
   - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
-
 # [Script description](#contents)
 
 ## [Script and sample code](#contents)
@@ -124,18 +122,19 @@ Parameters for both training and evaluation can be set in config.py
 ### Usage
 
 - Ascend: sh run_train.sh Ascend [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)
+
 ### Launch
 
-```
+```bash
   # training example
   Ascend: bash run_train.sh Ascend ~/hccl.json ~/imagenet/train/ ~/pretrained_ckeckpoint
 ```
 
 ### Result
 
-Training result will be stored in the example path. Checkpoints will be stored at `./train/device$i/` by default, and training log  will be redirected to `./train/device$i/train.log` like followings. 
+Training result will be stored in the example path. Checkpoints will be stored at `./train/device$i/` by default, and training log  will be redirected to `./train/device$i/train.log` like following.
 
-```
+```bash
 epoch: 1 step: 5004, loss is 4.8995576
 epoch: 2 step: 5004, loss is 3.9235563
 epoch: 3 step: 5004, loss is 3.833077
@@ -153,7 +152,7 @@ You can start training using python or shell scripts. The usage of shell scripts
 
 ### Launch
 
-```
+```bash
 # infer example
   shell:
       Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/Resnet50-30_5004.ckpt
@@ -163,9 +162,9 @@ You can start training using python or shell scripts. The usage of shell scripts
 
 ### Result
 
-Inference result will be stored in the example path, you can find result like the followings in `./eval/infer.log`.
+Inference result will be stored in the example path, you can find result like the following in `./eval/infer.log`.
 
-```
+```bash
 result: {'acc': 0.76576314102564111}
 ```
 
@@ -191,7 +190,7 @@ result: {'acc': 0.76576314102564111}
 | Total time                 | 8pcs: 17 hours(30 epochs with pretrained)                   |
 | Parameters (M)             | 25.5                                                        |
 | Checkpoint for Fine tuning | 197M (.ckpt file)                                           |
-| Scripts                    | [resnet50-quant script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet50_quant) | 
+| Scripts                    | [resnet50-quant script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet50_quant) |
 
 ### Inference Performance
 
diff --git a/model_zoo/official/cv/resnet50_quant/src/launch.py b/model_zoo/official/cv/resnet50_quant/src/launch.py
index c30869504a6..9e7ecb26c4a 100644
--- a/model_zoo/official/cv/resnet50_quant/src/launch.py
+++ b/model_zoo/official/cv/resnet50_quant/src/launch.py
@@ -34,7 +34,7 @@ def parse_args():
         >>> parse_args()
     """
     parser = ArgumentParser(description="mindspore distributed training launch "
-                                        "helper utilty that will spawn up "
+                                        "helper utility that will spawn up "
                                         "multiple distributed processes")
     parser.add_argument("--nproc_per_node", type=int, default=1,
                         help="The number of processes to launch on each node, "
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/eval.py b/model_zoo/official/cv/yolov3_darknet53_quant/eval.py
index 179b7d68e23..27dd899e84a 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/eval.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/eval.py
@@ -93,7 +93,7 @@ class DetectionEngine:
 
     def _nms(self, dets, thresh):
         """Calculate NMS."""
-        # conver xywh -> xmin ymin xmax ymax
+        # convert xywh -> xmin ymin xmax ymax
         x1 = dets[:, 0]
         y1 = dets[:, 1]
         x2 = x1 + dets[:, 2]
@@ -185,7 +185,7 @@ class DetectionEngine:
 
                 x_top_left = x - w / 2.
                 y_top_left = y - h / 2.
-                # creat all False
+                # create all False
                 flag = np.random.random(cls_emb.shape) > sys.maxsize
                 for i in range(flag.shape[0]):
                     c = cls_argmax[i]
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/scripts/run_eval.sh b/model_zoo/official/cv/yolov3_darknet53_quant/scripts/run_eval.sh
index 89debe747cc..9343f0df46e 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/scripts/run_eval.sh
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/scripts/run_eval.sh
@@ -59,7 +59,7 @@ cp ../*.py ./eval$3
 cp -r ../src ./eval$3
 cd ./eval$3 || exit
 env > env.log
-echo "start infering for device $DEVICE_ID"
+echo "start inferring for device $DEVICE_ID"
 python eval.py \
     --data_dir=$DATASET_PATH \
     --pretrained=$CHECKPOINT_PATH \
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py b/model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py
index a240338007b..20197c437b9 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py
@@ -94,7 +94,7 @@ def get_interp_method(interp, sizes=()):
         Neighbors method. (used by default).
         4: Lanczos interpolation over 8x8 pixel neighborhood.
         9: Cubic for enlarge, area for shrink, bilinear for others
-        10: Random select from interpolation method metioned above.
+        10: Random select from interpolation method mentioned above.
         Note:
         When shrinking an image, it will generally look best with AREA-based
         interpolation, whereas, when enlarging an image, it will generally look best
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo.py b/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo.py
index 9f70b12a86d..dad97b3f4fb 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo.py
@@ -58,7 +58,7 @@ class YoloBlock(nn.Cell):
 
     Args:
         in_channels: Integer. Input channel.
-        out_chls: Interger. Middle channel.
+        out_chls: Integer. Middle channel.
         out_channels: Integer. Output channel.
 
     Returns:
@@ -108,7 +108,7 @@ class YOLOv3(nn.Cell):
      Args:
          backbone_shape: List. Darknet output channels shape.
          backbone: Cell. Backbone Network.
-         out_channel: Interger. Output channel.
+         out_channel: Integer. Output channel.
 
      Returns:
          Tensor, output tensor.
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo_dataset.py b/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo_dataset.py
index 28d799640ca..591e3f1c7db 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo_dataset.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo_dataset.py
@@ -44,7 +44,7 @@ def has_valid_annotation(anno):
     # if all boxes have close to zero area, there is no annotation
     if _has_only_empty_bbox(anno):
         return False
-    # keypoints task have a slight different critera for considering
+    # keypoints task have a slight different criteria for considering
     # if an annotation is valid
     if "keypoints" not in anno[0]:
         return True
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/train.py b/model_zoo/official/cv/yolov3_darknet53_quant/train.py
index 1967aeac28f..e18acb60a24 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/train.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/train.py
@@ -121,7 +121,7 @@ def parse_args():
         args.rank = get_rank()
         args.group_size = get_group_size()
 
-    # select for master rank save ckpt or all rank save, compatiable for model parallel
+    # select for master rank save ckpt or all rank save, compatible for model parallel
     args.rank_save_ckpt_flag = 0
     if args.is_save_on_master:
         if args.rank == 0:
@@ -140,6 +140,14 @@ def conver_training_shape(args):
     training_shape = [int(args.training_shape), int(args.training_shape)]
     return training_shape
 
+def build_quant_network(network):
+    quantizer = QuantizationAwareTraining(bn_fold=True,
+                                          per_channel=[True, False],
+                                          symmetric=[True, False],
+                                          one_conv_fold=False)
+    network = quantizer.quantize(network)
+    return network
+
 
 def train():
     """Train function."""
@@ -168,11 +176,7 @@ def train():
     config = ConfigYOLOV3DarkNet53()
     # convert fusion network to quantization aware network
     if config.quantization_aware:
-        quantizer = QuantizationAwareTraining(bn_fold=True,
-                                              per_channel=[True, False],
-                                              symmetric=[True, False],
-                                              one_conv_fold=False)
-        network = quantizer.quantize(network)
+        network = build_quant_network(network)
 
     network = YoloWithLossCell(network)
     args.logger.info('finish get network')
@@ -198,11 +202,8 @@ def train():
 
     lr = get_lr(args)
 
-    opt = Momentum(params=get_param_groups(network),
-                   learning_rate=Tensor(lr),
-                   momentum=args.momentum,
-                   weight_decay=args.weight_decay,
-                   loss_scale=args.loss_scale)
+    opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr), momentum=args.momentum,
+                   weight_decay=args.weight_decay, loss_scale=args.loss_scale)
 
     network = TrainingWrapper(network, opt)
     network.set_train()
@@ -213,9 +214,7 @@ def train():
         ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
                                        keep_checkpoint_max=ckpt_max_num)
         save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
-        ckpt_cb = ModelCheckpoint(config=ckpt_config,
-                                  directory=save_ckpt_path,
-                                  prefix='{}'.format(args.rank))
+        ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=save_ckpt_path, prefix='{}'.format(args.rank))
         cb_params = _InternalCallbackParam()
         cb_params.train_network = network
         cb_params.epoch_num = ckpt_max_num
diff --git a/model_zoo/official/cv/yolov3_resnet18/README.md b/model_zoo/official/cv/yolov3_resnet18/README.md
index 78646862ea7..92b540c3003 100644
--- a/model_zoo/official/cv/yolov3_resnet18/README.md
+++ b/model_zoo/official/cv/yolov3_resnet18/README.md
@@ -4,7 +4,7 @@
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
 - [Environment Requirements](#environment-requirements)
-- [Quick Start](#quick-start)    
+- [Quick Start](#quick-start)
 - [Script Description](#script-description)
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
@@ -19,7 +19,6 @@
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 
-
 # [YOLOv3_ResNet18 Description](#contents)
 
 YOLOv3 network based on ResNet-18, with support for training and evaluation.
@@ -30,24 +29,25 @@ YOLOv3 network based on ResNet-18, with support for training and evaluation.
 
 The overall network architecture of YOLOv3 is show below:
 
-And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResNet18 has 4 stages. The ResNet architecture performs the initial convolution and max-pooling using 7×7 and 3×3 kernel sizes respectively. Afterward,  every stage of the network has different Residual blocks (2, 2, 2, 2) containing two 3×3 conv layers. Finally, the network has an Average Pooling layer followed by a fully connected layer. 
-
+And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResNet18 has 4 stages. The ResNet architecture performs the initial convolution and max-pooling using 7×7 and 3×3 kernel sizes respectively. Afterward,  every stage of the network has different Residual blocks (2, 2, 2, 2) containing two 3×3 conv layers. Finally, the network has an Average Pooling layer followed by a fully connected layer.
 
 # [Dataset](#contents)
+
 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/>)
 
 - Dataset size：19G
-  - Train：18G，118000 images  
-  - Val：1G，5000 images 
-  - Annotations：241M，instances，captions，person_keypoints etc
+    - Train：18G，118000 images  
+    - Val：1G，5000 images
+    - Annotations：241M，instances，captions，person_keypoints etc
 - Data format：image and json files
   - Note：Data will be processed in dataset.py
 
 - Dataset
 
     1. The directory structure is as follows:
+
         ```
         .
         ├── annotations  # annotation jsons
@@ -55,7 +55,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
         └── val2017      # infer dataset
         ```
 
-    2. Organize the dataset infomation into a TXT file, each row in the file is as follows:
+    2. Organize the dataset information into a TXT file, each row in the file is as follows:
 
         ```
         train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
@@ -63,63 +63,61 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 
         Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. `dataset.py` is the parsing script, we read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are external inputs.
 
-
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-  - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
+    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
   - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
   - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
-
-
 # [Quick Start](#contents)
 
-After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows: 
+After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows:
 
-- runing on Ascend
+- running on Ascend
 
     ```shell script
     #run standalone training example
     sh run_standalone_train.sh [DEVICE_ID] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH]
-    
+
     #run distributed training example
     sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH] [RANK_TABLE_FILE]
-   
+
     #run evaluation example
     sh run_eval.sh [DEVICE_ID] [CKPT_PATH] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH]
     ```
+
 # [Script Description](#contents)
 
 ## [Script and Sample Code](#contents)
 
-```
+```python
 └── cv
     ├── README.md                           // descriptions about all the models
     ├── mindspore_hub_conf.md               // config for mindspore hub
-    └── yolov3_resnet18        
+    └── yolov3_resnet18
         ├── README.md                       // descriptions about yolov3_resnet18
-        ├── scripts 
+        ├── scripts
             ├── run_distribute_train.sh     // shell script for distributed on Ascend
             ├── run_standalone_train.sh     // shell script for distributed on Ascend
             └── run_eval.sh                 // shell script for evaluation on Ascend
-        ├── src 
+        ├── src
             ├── dataset.py                  // creating dataset
             ├── yolov3.py                   // yolov3 architecture
-            ├── config.py                   // parameter configuration 
+            ├── config.py                   // parameter configuration
             └── utils.py                    // util function
-        ├── train.py                        // training script 
+        ├── train.py                        // training script
         └── eval.py                         // evaluation script  
 ```
 
 ## [Script Parameters](#contents)
 
-  ```
   Major parameters in train.py and config.py as follows:
 
+  ```python
     device_num: Use device nums, default is 1.
     lr: Learning rate, default is 0.001.
     epoch_size: Epoch size, default is 50.
@@ -133,24 +131,23 @@ After installing MindSpore via the official website, you can start training and
     img_shape: Image height and width used as input to the model.
   ```
 
-
 ## [Training Process](#contents)
 
 ### Training on Ascend
+
 To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.**
 
 - Stand alone mode
 
-    ```
+    ```bash
     sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt
     ```
 
     The input variables are device id, epoch size, mindrecord directory path, dataset directory path and train TXT file path.
 
-
 - Distributed mode
 
-    ```
+    ```bash
     sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json
     ```
 
@@ -158,7 +155,7 @@ To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and
 
 You will get the loss value and time of each step as following:
 
-  ```
+  ```bash
   epoch: 145 step: 156, loss is 12.202981
   epoch time: 25599.22742843628, per step time: 164.0976117207454
   epoch: 146 step: 156, loss is 16.91706
@@ -168,19 +165,20 @@ You will get the loss value and time of each step as following:
   epoch: 148 step: 156, loss is 10.431475
   epoch time: 23634.241580963135, per step time: 151.50154859591754
   epoch: 149 step: 156, loss is 14.665991
-  epoch time: 24118.8325881958, per step time: 154.60790120638333 
+  epoch time: 24118.8325881958, per step time: 154.60790120638333
   epoch: 150 step: 156, loss is 10.779521
   epoch time: 25319.57221031189, per step time: 162.30495006610187
   ```
 
-Note the results is two-classification(person and face) used our own annotations with coco2017, you can change `num_classes` in `config.py` to train your dataset. And we will suport 80 classifications in coco2017 the near future.
+Note the results is two-classification(person and face) used our own annotations with coco2017, you can change `num_classes` in `config.py` to train your dataset. And we will support 80 classifications in coco2017 the near future.
 
 ## [Evaluation Process](#contents)
+
 ### Evaluation on Ascend
 
 To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/training/en/master/use/save_model.html) file.
 
-  ```
+  ```bash
   sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt
   ```
 
@@ -188,18 +186,18 @@ The input variables are device id, checkpoint path, mindrecord directory path, d
 
 You will get the precision and recall value of each class:
 
-  ```
+  ```bash
   class 0 precision is 88.18%, recall is 66.00%
   class 1 precision is 85.34%, recall is 79.13%
   ```
 
 Note the precision and recall values are results of two-classification(person and face) used our own annotations with coco2017.
 
-
 # [Model Description](#contents)
+
 ## [Performance](#contents)
 
-### Evaluation Performance 
+### Evaluation Performance
 
 | Parameters                 | Ascend                                                      |
 | -------------------------- | ----------------------------------------------------------- |
@@ -217,7 +215,6 @@ Note the precision and recall values are results of two-classification(person an
 | Parameters (M)             | 189                                                         |
 | Scripts                    | [yolov3_resnet18 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18) | [yolov3_resnet18 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18) |
 
-
 ### Inference Performance
 
 | Parameters          | Ascend                                          |
@@ -233,9 +230,9 @@ Note the precision and recall values are results of two-classification(person an
 
 # [Description of Random Situation](#contents)
 
-In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py. 
+In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
 
+# [ModelZoo Homepage](#contents)
 
-# [ModelZoo Homepage](#contents)  
  Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  
 
diff --git a/model_zoo/official/cv/yolov3_resnet18/scripts/run_distribute_train.sh b/model_zoo/official/cv/yolov3_resnet18/scripts/run_distribute_train.sh
index 0e7c48effbb..e273cf74039 100644
--- a/model_zoo/official/cv/yolov3_resnet18/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_distribute_train.sh
@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "======================================================================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH RANK_TABLE_FILE PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
 echo "For example: sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json /opt/yolov3-150.ckpt(optional) 100(optional)"
 echo "It is better to use absolute path."
@@ -47,7 +47,7 @@ then
     exit 1
 fi
 
-echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+echo "After running the script, the network runs in the background. The log will be generated in LOGx/log.txt"
 
 export RANK_TABLE_FILE=$6
 export RANK_SIZE=$1
diff --git a/model_zoo/official/cv/yolov3_resnet18/scripts/run_eval.sh b/model_zoo/official/cv/yolov3_resnet18/scripts/run_eval.sh
index ccdb6198542..8bb054289c0 100644
--- a/model_zoo/official/cv/yolov3_resnet18/scripts/run_eval.sh
+++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_eval.sh
@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_eval.sh DEVICE_ID CKPT_PATH MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
 echo "for example: sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt"
 echo "=============================================================================================================="
diff --git a/model_zoo/official/cv/yolov3_resnet18/scripts/run_standalone_train.sh b/model_zoo/official/cv/yolov3_resnet18/scripts/run_standalone_train.sh
index 71634a89cbf..5e3a234e257 100644
--- a/model_zoo/official/cv/yolov3_resnet18/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_standalone_train.sh
@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "========================================================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
 echo "for example: sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt /opt/yolov3-50.ckpt(optional) 30(optional)"
 echo "========================================================================================================================================="