diff --git a/models/megengine_nlp_bert.md b/models/megengine_nlp_bert.md
index bbdba8bd04db07ace965183790e24425041fee64..4439ff64ba851de990e2322ad5169903e893b143 100644
--- a/models/megengine_nlp_bert.md
+++ b/models/megengine_nlp_bert.md
@@ -6,7 +6,7 @@ summary:
     zh_CN: BERT
 author: MegEngine Team
 tags: [nlp]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/nlp/bert
 ---
 
 ```python
@@ -23,7 +23,7 @@ model = megengine.hub.load("megengine/models", "wwm_cased_L-24_H-1024_A-16", pre
 # model = megengine.hub.load("megengine/models", "multi_cased_L-12_H-768_A-12", pretrained=True)
 ```
 
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 
 这个项目中, 我们用MegEngine重新实现了Google开源的BERT模型.
 
@@ -72,21 +72,21 @@ def create_hub_bert(model_name, pretrained):
 
     vocab_url = '{}/{}/{}'.format(DATA_URL, model_name, VOCAB_NAME)
     config_url = '{}/{}/{}'.format(DATA_URL, model_name, CONFIG_NAME)
-    
+
     vocab_file = './{}/{}'.format(model_name, VOCAB_NAME)
     config_file = './{}/{}'.format(model_name, CONFIG_NAME)
-    
+
     download_file(vocab_url, vocab_file)
     download_file(config_url, config_file)
 
     config = BertConfig(config_file)
 
     model = hub.load(
-        "megengine/models", 
-        MODEL_NAME[model_name], 
+        "megengine/models",
+        MODEL_NAME[model_name],
         pretrained=pretrained,
     )
-    
+
     return model, config, vocab_file
 ```
 
@@ -102,14 +102,14 @@ class BertForSequenceClassification(Module):
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
         _, pooled_output = self.bert(
-            input_ids, token_type_ids, 
+            input_ids, token_type_ids,
             attention_mask, output_all_encoded_layers=False)
         pooled_output = self.dropout(pooled_output)
         logits = self.classifier(pooled_output)
 
         if labels is not None:
             loss = cross_entropy_with_softmax(
-                logits.reshape(-1, self.num_labels), 
+                logits.reshape(-1, self.num_labels),
                 labels.reshape(-1))
             return logits, loss
         else:
@@ -123,7 +123,7 @@ model = BertForSequenceClassification(config, num_labels=2, bert=bert)
 
 ### 模型描述
 
-我们在[models](https://github.com/megengine/models/official/nlp/bert)中提供了简单的示例代码. 
+我们在[models](https://github.com/megengine/models/official/nlp/bert)中提供了简单的示例代码.
 此示例代码在Microsoft Research Paraphrase（MRPC）数据集上对预训练的`uncased_L-12_H-768_A-12`模型进行微调.
 
 我们的样例代码中使用了原始的超参进行微调, 在测试集中可以得到84％到88％的正确率.
@@ -131,9 +131,9 @@ model = BertForSequenceClassification(config, num_labels=2, bert=bert)
 ### 参考文献
 
  - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805), Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova;
- 
 
-<!-- section: en_US --> 
+
+<!-- section: en_US -->
 This repository contains reimplemented Google's BERT by MegEngine.
 
 We provide the following pre-trained models for users to finetune in different tasks.
@@ -181,21 +181,21 @@ def create_hub_bert(model_name, pretrained):
 
     vocab_url = '{}/{}/{}'.format(DATA_URL, model_name, VOCAB_NAME)
     config_url = '{}/{}/{}'.format(DATA_URL, model_name, CONFIG_NAME)
-    
+
     vocab_file = './{}/{}'.format(model_name, VOCAB_NAME)
     config_file = './{}/{}'.format(model_name, CONFIG_NAME)
-    
+
     download_file(vocab_url, vocab_file)
     download_file(config_url, config_file)
 
     config = BertConfig(config_file)
 
     model = hub.load(
-        "megengine/models", 
-        MODEL_NAME[model_name], 
+        "megengine/models",
+        MODEL_NAME[model_name],
         pretrained=pretrained,
     )
-    
+
     return model, config, vocab_file
 ```
 
@@ -212,14 +212,14 @@ class BertForSequenceClassification(Module):
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
         _, pooled_output = self.bert(
-            input_ids, token_type_ids, 
+            input_ids, token_type_ids,
             attention_mask, output_all_encoded_layers=False)
         pooled_output = self.dropout(pooled_output)
         logits = self.classifier(pooled_output)
 
         if labels is not None:
             loss = cross_entropy_with_softmax(
-                logits.reshape(-1, self.num_labels), 
+                logits.reshape(-1, self.num_labels),
                 labels.reshape(-1))
             return logits, loss
         else:
@@ -234,11 +234,11 @@ All pre-trained models expect the data to be pre-processed correctly. The requir
 
 ### Model Description
 We provide example code in [models](https://github.com/megengine/models/official/nlp/bert).
-This example code fine-tunes the pre-trained `uncased_L-12_H-768_A-12` model on the Microsoft Research Paraphrase (MRPC) dataset. 
+This example code fine-tunes the pre-trained `uncased_L-12_H-768_A-12` model on the Microsoft Research Paraphrase (MRPC) dataset.
 
 Our test ran on the original implementation hyper-parameters gave evaluation results between 84% and 88%.
 
 
 ### References
  - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805), Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova;
- 
+
diff --git a/models/megengine_vision_deeplabv3plus.md b/models/megengine_vision_deeplabv3plus.md
index 29d31c7d2dba75cf889a7ae028deb32f846263a3..6d7cc507c546380ea68f591b3f1990d9096ec070 100644
--- a/models/megengine_vision_deeplabv3plus.md
+++ b/models/megengine_vision_deeplabv3plus.md
@@ -6,7 +6,7 @@ summary:
     zh_CN: DeepLabV3plus (VOC预训练权重）
 author: MegEngine Team
 tags: [vision]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/segmentation
 ---
 
 ```python
@@ -18,15 +18,15 @@ model = hub.load(
 )
 model.eval()
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 
 所有预训练模型希望数据被正确预处理。模型要求输入BGR的图片, 建议缩放到512x512，最后做归一化处理 (均值为: `[103.530, 116.280, 123.675]`, 标准差为: `[57.375, 57.120, 58.395]`)。
 
 
 下面是一段处理一张图片的样例代码。
 
-```python 
-# Download an example image from the megengine data website 
+```python
+# Download an example image from the megengine data website
 import urllib
 url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
 try: urllib.URLopener().retrieve(url, filename)
@@ -72,14 +72,14 @@ pred = cv2.resize(pred.astype("uint8"), (oriw, orih), interpolation=cv2.INTER_LI
 - [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611.pdf), Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and
 Hartwig Adam; ECCV, 2018
 
-<!-- section: en_US --> 
+<!-- section: en_US -->
 
 All pre-trained models expect input images normalized in the same way. Input images must be 3-channel BGR images of shape (H x W x 3), reszied to (512 x 512), then normalized using mean = [103.530, 116.280, 123.675] and std = [57.375, 57.120, 58.395]).
 
 Here's a sample execution.
 
-```python 
-# Download an example image from the megengine data website 
+```python
+# Download an example image from the megengine data website
 import urllib
 url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
 try: urllib.URLopener().retrieve(url, filename)
diff --git a/models/megengine_vision_faster_rcnn.md b/models/megengine_vision_faster_rcnn.md
index 80125cb17b7362af3fad78a324c79ff0ed6767e1..8ed5b31629045c57cddc50347a88371a89cea021 100644
--- a/models/megengine_vision_faster_rcnn.md
+++ b/models/megengine_vision_faster_rcnn.md
@@ -6,14 +6,14 @@ summary:
     zh_CN: Faster-RCNN (COCO预训练权重）
 author: MegEngine Team
 tags: [vision, detection]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/detection
 ---
 
 ```python
 from megengine import hub
 model = hub.load(
     "megengine/models",
-    "faster_rcnn_fpn_res50_coco_1x_800size",
+    "faster_rcnn_res50_coco_1x_800size",
     pretrained=True,
     use_cache=False,
 )
@@ -24,7 +24,7 @@ models_api = hub.import_module(
     git_host="github.com",
 )
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 
 所有预训练模型希望数据被正确预处理。
 模型要求输入BGR的图片, 同时需要等比例缩放到：短边和长边分别不超过800/1333
@@ -52,7 +52,7 @@ from megengine import jit
 def infer():
     predictions = model(model.inputs)
     return predictions
-    
+
 print(infer())
 ```
 
@@ -60,9 +60,10 @@ print(infer())
 
 目前我们提供了retinanet的预训练模型, 在coco验证集上的结果如下：
 
-| model                        | mAP<br>@5-95     |  
-| ---                          |     ---          |
-| faster-rcnn-res50-1x-800size |    37.3          |
+| model                               | mAP<br>@5-95 |
+| ---                                 | :---:        |
+| faster-rcnn-res50-1x-800size        | 38.8         |
+| faster-rcnn-res50-1x-800size-syncbn | 39.3         |
 
 ### 参考文献
 
@@ -70,8 +71,8 @@ print(infer())
 - [Feature Pyramid Networks for Object Detection](https://arxiv.org/pdf/1612.03144.pdf) T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.
 - [Microsoft COCO: Common Objects in Context](https://arxiv.org/pdf/1405.0312.pdf)  Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C Lawrence, Lin T Y, Maire M, Belongie S, et al. European conference on computer vision. Springer, Cham, 2014: 740-755.
 
- 
-<!-- section: en_US --> 
+
+<!-- section: en_US -->
 
 All pre-trained models expect input images normalized in the same way,
 i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge/longedge to no more than `800/1333`.
@@ -99,7 +100,7 @@ from megengine import jit
 def infer():
     predictions = model(model.inputs)
     return predictions
-    
+
 print(infer())
 ```
 
@@ -107,9 +108,10 @@ print(infer())
 
 Currently we provide a `retinanet` model which is pretrained on `COCO2017` training set. The mAP on `COCO2017` val set can be found in following table.
 
-| model                        | mAP<br>@5-95     |  
-| ---                          |     ---          |
-| faster-rcnn-res50-1x-800size |    37.3          |
+| model                               | mAP<br>@5-95 |
+| ---                                 | :---:        |
+| faster-rcnn-res50-1x-800size        | 38.8         |
+| faster-rcnn-res50-1x-800size-syncbn | 39.3         |
 
 ### References
 
diff --git a/models/megengine_vision_mspn.md b/models/megengine_vision_mspn.md
index 29d7f73f385e310cc3b7b791952230b86b393516..37373458c44152c0df43cf1cf65b99fec6a0eab7 100644
--- a/models/megengine_vision_mspn.md
+++ b/models/megengine_vision_mspn.md
@@ -6,7 +6,7 @@ summary:
     zh_CN: MSPN（COCO 预训练权重）
 author: MegEngine Team
 tags: [vision, keypoints]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/keypoints
 ---
 
 ```python3
@@ -14,7 +14,7 @@ import megengine.hub
 model = megengine.hub.load('megengine/models', 'mspn_4stage', pretrained=True)
 model.eval()
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 MSPN是单人关节点检测模型，在多人场景下需要配合人体检测器使用。详细的多人检测代码示例可以参考[inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py)。
 
 针对单张图片，这里提供使用retinanet做人体检测，然后用MSPN检测关节点的示例:
@@ -75,7 +75,7 @@ cv2.imwrite("vis_skeleton.jpg", canvas)
 ### 参考文献
 - [Rethinking on Multi-Stage Networks for Human Pose Estimation](https://arxiv.org/pdf/1901.00148.pdf) Wenbo Li1, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei and Jian Sun
 
-<!-- section: en_US --> 
+<!-- section: en_US -->
 SimpleBaseline is classical network for single person pose estimation. It can also be applied to multi-person cases when combined with a human detector. The details of this pipline can be referred to [inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py).
 
 For single image, here is a sample execution when SimpleBaseline is combined with retinanet
@@ -128,11 +128,11 @@ cv2.imwrite("vis_skeleton.jpg", canvas)
 ```
 ### Model Desription
 
-With the AP human detectoin results being 56.4 on COCO val2017 dataset, the performances of simplebline on COCO val2017 dataset is 
+With the AP human detectoin results being 56.4 on COCO val2017 dataset, the performances of simplebline on COCO val2017 dataset is
 
 |Methods|Backbone|Input Size| AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
 |---|:---:|---|---|---|---|---|---|---|---|---|---|---|
 | MSPN_4stage |MSPN|256x192| 0.752 | 0.900 | 0.819 | 0.716 | 0.825 | 0.819 | 0.943 | 0.875 | 0.770 | 0.887 |
 
 ### References
-- [Rethinking on Multi-Stage Networks for Human Pose Estimation](https://arxiv.org/pdf/1901.00148.pdf) Wenbo Li1, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei and Jian Sun
\ No newline at end of file
+- [Rethinking on Multi-Stage Networks for Human Pose Estimation](https://arxiv.org/pdf/1901.00148.pdf) Wenbo Li1, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei and Jian Sun
diff --git a/models/megengine_vision_resnet.md b/models/megengine_vision_resnet.md
index eb1ba56e99bb909a4b61b36bc6b7a2ed935a177b..4f389c4aab974798954a9e03bb09329f0a88e5e3 100644
--- a/models/megengine_vision_resnet.md
+++ b/models/megengine_vision_resnet.md
@@ -6,7 +6,7 @@ summary:
     zh_CN: 深度残差网络（ImageNet 预训练权重）
 author: MegEngine Team
 tags: [vision, classification]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/classification
 ---
 
 ```python
@@ -20,7 +20,7 @@ model = megengine.hub.load('megengine/models', 'resnet18', pretrained=True)
 # model = megengine.hub.load('megengine/models', 'resnext50_32x4d', pretrained=True)
 model.eval()
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 
 所有预训练模型希望数据被正确预处理。
 模型要求输入BGR的图片, 短边缩放到`256`, 并中心裁剪至`(224 x 224)`的大小，最后做归一化处理 (均值为: `[103.530, 116.280, 123.675]`, 标准差为: `[57.375, 57.120, 58.395]`)。
@@ -59,10 +59,10 @@ print(probs)
 
 | 模型 | Top1 acc | Top5 acc |
 | --- | --- | --- |
-| ResNet18 |  70.312  |  89.430  | 
-| ResNet34 |  73.960  |  91.630  | 
-| ResNet50 |  76.254  |  93.056  | 
-| ResNet101|  77.944  |  93.844  | 
+| ResNet18 |  70.312  |  89.430  |
+| ResNet34 |  73.960  |  91.630  |
+| ResNet50 |  76.254  |  93.056  |
+| ResNet101|  77.944  |  93.844  |
 | ResNet152|  78.582  |  94.130  |
 | ResNeXt50 32x4d | 77.592 | 93.644 |
 
@@ -71,7 +71,7 @@ print(probs)
  - [Deep Residual Learning for Image Recognition](http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf), Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778
  - [Aggregated Residual Transformation for Deep Neural Networks](http://openaccess.thecvf.com/content_cvpr_2017/papers/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.pdf), Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1492-1500
 
-<!-- section: en_US --> 
+<!-- section: en_US -->
 
 All pre-trained models expect input images normalized in the same way,
 i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge to `256`, center-cropped to `(224 x 224)`.
@@ -111,10 +111,10 @@ Currently we provide these pretrained models: `resnet18`, `resnet34`, `resnet50`
 
 | model | Top1 acc | Top5 acc |
 | --- | --- | --- |
-| ResNet18 |  70.312  |  89.430  | 
-| ResNet34 |  73.960  |  91.630  | 
-| ResNet50 |  76.254  |  93.056  | 
-| ResNet101|  77.944  |  93.844  | 
+| ResNet18 |  70.312  |  89.430  |
+| ResNet34 |  73.960  |  91.630  |
+| ResNet50 |  76.254  |  93.056  |
+| ResNet101|  77.944  |  93.844  |
 | ResNet152|  78.582  |  94.130  |
 | ResNeXt50 32x4d | 77.592 | 93.644 |
 
diff --git a/models/megengine_vision_retinanet.md b/models/megengine_vision_retinanet.md
index 221d0ef4f4a47c3f8e8d1b856201c1f29dbcc800..283df28debcdad121922679ca974fbb1b1a708a3 100644
--- a/models/megengine_vision_retinanet.md
+++ b/models/megengine_vision_retinanet.md
@@ -6,15 +6,16 @@ summary:
     zh_CN: RetinaNet (COCO预训练权重）
 author: MegEngine Team
 tags: [vision, detection]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/detection
 ---
 
 ```python
 from megengine import hub
 model = hub.load(
     "megengine/models",
-    "retinanet_res50_1x_800size",
+    "retinanet_res50_coco_1x_800size",
     pretrained=True,
+    use_cache=False,
 )
 model.eval()
 
@@ -23,7 +24,7 @@ models_api = hub.import_module(
     git_host="github.com",
 )
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 
 所有预训练模型希望数据被正确预处理。
 模型要求输入BGR的图片, 同时需要等比例缩放到：短边和长边分别不超过800/1333
@@ -51,7 +52,7 @@ from megengine import jit
 def infer():
     predictions = model(model.inputs)
     return predictions
-    
+
 print(infer())
 ```
 
@@ -59,9 +60,10 @@ print(infer())
 
 目前我们提供了retinanet的预训练模型, 在coco验证集上的结果如下：
 
-| model                        | mAP<br>@5-95     |  
-| ---                        |     ---          |
-| retinanet-res50-1x-800size |    36.0          |
+| model                                 | mAP<br>@5-95 |
+| ---                                   | :---:        |
+| retinanet-res50-coco1x-800size        | 36.4         |
+| retinanet-res50-coco1x-800size-syncbn | 37.1         |
 
 ### 参考文献
 
@@ -69,8 +71,8 @@ print(infer())
 - [Microsoft COCO: Common Objects in Context](https://arxiv.org/pdf/1405.0312.pdf)  Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C Lawrence
 Lin T Y, Maire M, Belongie S, et al. European conference on computer vision. Springer, Cham, 2014: 740-755.
 
- 
-<!-- section: en_US --> 
+
+<!-- section: en_US -->
 
 All pre-trained models expect input images normalized in the same way,
 i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge/longedge to no more than `800/1333`.
@@ -98,7 +100,7 @@ from megengine import jit
 def infer():
     predictions = model(model.inputs)
     return predictions
-    
+
 print(infer())
 ```
 
@@ -106,9 +108,10 @@ print(infer())
 
 Currently we provide a `retinanet` model which is pretrained on `COCO2017` training set. The mAP on `COCO2017` val set can be found in following table.
 
-| model                        | mAP<br>@5-95     |  
-| ---                        |     ---          |
-| retinanet-res50-1x-800size |    36.0          |
+| model                                 | mAP<br>@5-95 |
+| ---                                   | :---:        |
+| retinanet-res50-coco1x-800size        | 36.4         |
+| retinanet-res50-coco1x-800size-syncbn | 37.1         |
 
 ### References
 
diff --git a/models/megengine_vision_shufflenet_v2.md b/models/megengine_vision_shufflenet_v2.md
index aea733ba7e441d5fb5ba15b610ec1adab8a0f922..69ba3a3d7ee9397d80476cb63e7d01820e1c61e8 100644
--- a/models/megengine_vision_shufflenet_v2.md
+++ b/models/megengine_vision_shufflenet_v2.md
@@ -6,7 +6,7 @@ summary:
     zh_CN: ShuffleNet V2（ImageNet 预训练权重）
 author: MegEngine Team
 tags: [vision, classification]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/classification
 ---
 
 ```python
@@ -17,7 +17,7 @@ model = megengine.hub.load('megengine/models', 'shufflenet_v2_x1_0', pretrained=
 # model = megengine.hub.load('megengine/models', 'shufflenet_v2_x2_0', pretrained=True)
 model.eval()
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 
 所有预训练模型希望数据被正确预处理。
 模型要求输入BGR的图片, 短边缩放到`256`, 并中心裁剪至`(224 x 224)`的大小，最后做归一化处理 (均值为: `[103.530, 116.280, 123.675]`, 标准差为: `[57.375, 57.120, 58.395]`)。
@@ -56,16 +56,16 @@ print(probs)
 
 | 模型 | top1 acc | top5 acc |
 | --- | --- | --- |
-| ShuffleNetV2 x0.5 |  60.696  |  82.190  | 
-| ShuffleNetV2 x1.0 |  69.372  |  88.764  | 
-| ShuffleNetV2 x1.5 |  72.806  |  90.792  | 
-| ShuffleNetV2 x2.0 |  75.074  |  92.278  | 
+| ShuffleNetV2 x0.5 |  60.696  |  82.190  |
+| ShuffleNetV2 x1.0 |  69.372  |  88.764  |
+| ShuffleNetV2 x1.5 |  72.806  |  90.792  |
+| ShuffleNetV2 x2.0 |  75.074  |  92.278  |
 
 ### 参考文献
 
 - [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164), Ma, Ningning, et al. "Shufflenet v2: Practical guidelines for efficient cnn architecture design." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
 
-<!-- section: en_US --> 
+<!-- section: en_US -->
 
 All pre-trained models expect input images normalized in the same way,
 i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge to `256`, center-cropped to `(224 x 224)`.
@@ -105,11 +105,11 @@ Currently we provide several pretrained models(see the table below), Their 1-cro
 
 | model | top1 acc | top5 acc |
 | --- | --- | --- |
-| ShuffleNetV2 x0.5 |  60.696  |  82.190  | 
-| ShuffleNetV2 x1.0 |  69.372  |  88.764  | 
-| ShuffleNetV2 x1.5 |  72.806  |  90.792  | 
-| ShuffleNetV2 x2.0 |  75.074  |  92.278  | 
+| ShuffleNetV2 x0.5 |  60.696  |  82.190  |
+| ShuffleNetV2 x1.0 |  69.372  |  88.764  |
+| ShuffleNetV2 x1.5 |  72.806  |  90.792  |
+| ShuffleNetV2 x2.0 |  75.074  |  92.278  |
 
 ### References
 
- - [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164), Ma, Ningning, et al. "Shufflenet v2: Practical guidelines for efficient cnn architecture design." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
\ No newline at end of file
+ - [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164), Ma, Ningning, et al. "Shufflenet v2: Practical guidelines for efficient cnn architecture design." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
diff --git a/models/megengine_vision_simplebaseline.md b/models/megengine_vision_simplebaseline.md
index 24baac1745d6ce0c956509ddc101813530ddc787..c4f2e3cab6c79739baa29c8603f82748d0ec587d 100644
--- a/models/megengine_vision_simplebaseline.md
+++ b/models/megengine_vision_simplebaseline.md
@@ -6,7 +6,7 @@ summary:
     zh_CN: SimpleBaeline（COCO 预训练权重）
 author: MegEngine Team
 tags: [vision, keypoints]
-github-link: https://github.com/megengine/models
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/keypoints
 ---
 
 ```python3
@@ -17,7 +17,7 @@ model = megengine.hub.load('megengine/models', 'simplebaseline_res50', pretraine
 # model = megengine.hub.load('megengine/models', 'simplebaseline_res152', pretrained=True)
 model.eval()
 ```
-<!-- section: zh_CN --> 
+<!-- section: zh_CN -->
 SimpleBaseline是单人关节点检测模型，在多人场景下需要配合人体检测器使用。详细的多人检测代码示例可以参考[inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py)。
 
 针对单张图片，这里提供使用retinanet做人体检测，然后用SimpleBaseline检测关节点的示例:
@@ -79,7 +79,7 @@ cv2.imwrite("vis_skeleton.jpg", canvas)
 ### 参考文献
 - [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/pdf/1804.06208.pdf), Bin Xiao, Haiping Wu, and Yichen Wei
 
-<!-- section: en_US --> 
+<!-- section: en_US -->
 SimpleBaseline is classical network for single person pose estimation. It can also be applied to multi-person cases when combined with a human detector. The details of this pipline can be referred to [inference.py](https://github.com/MegEngine/Models/blob/master/official/vision/keypoints/inference.py).
 
 For single image, here is a sample execution when SimpleBaseline is combined with retinanet
@@ -141,4 +141,4 @@ With the AP human detectoin results being 56.4 on COCO val2017 dataset, the perf
 | SimpleBaseline |Res152|256x192| 0.724 | 0.888 | 0.794 | 0.688 | 0.795 | 0.795 | 0.934 | 0.856 | 0.746 | 0.863 |
 
 ### References
-- [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/pdf/1804.06208.pdf), Bin Xiao, Haiping Wu, and Yichen Wei
\ No newline at end of file
+- [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/pdf/1804.06208.pdf), Bin Xiao, Haiping Wu, and Yichen Wei