fix(hub): fix detection models and links (#7)

98939299 · Jianfeng Wang · GitHub · 73b34503 · 98939299 · 98939299
8 changed file
--- 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

--- 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

--- 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,
 )
@@ -61,8 +61,9 @@ 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          |
+| ---                                 | :---:        |
+| faster-rcnn-res50-1x-800size        | 38.8         |
+| faster-rcnn-res50-1x-800size-syncbn | 39.3         |

 ### 参考文献

@@ -108,8 +109,9 @@ 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          |
+| ---                                 | :---:        |
+| faster-rcnn-res50-1x-800size        | 38.8         |
+| faster-rcnn-res50-1x-800size-syncbn | 39.3         |

 ### References


--- 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

--- 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

--- 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()

@@ -60,8 +61,9 @@ 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          |
+| ---                                   | :---:        |
+| retinanet-res50-coco1x-800size        | 36.4         |
+| retinanet-res50-coco1x-800size-syncbn | 37.1         |

 ### 参考文献

@@ -107,8 +109,9 @@ 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          |
+| ---                                   | :---:        |
+| retinanet-res50-coco1x-800size        | 36.4         |
+| retinanet-res50-coco1x-800size-syncbn | 37.1         |

 ### References


--- 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

--- 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