feat(classification): add resnext101 pretrained model (#7)

README.md

@@ -62,6 +62,7 @@ export PYTHONPATH=/path/to/models:$PYTHONPATH
 | ResNet101 | 77.944 | 93.844 |
 | ResNet152 | 78.582 | 94.130 | 
 | ResNeXt50 32x4d | 77.592 | 93.644 |
+| ResNeXt101 32x8d| 79.520 | 94.586 |
 | ShuffleNetV2 x0.5 |  60.696  |  82.190  | 
 | ShuffleNetV2 x1.0 |  69.372  |  88.764  | 
 | ShuffleNetV2 x1.5 |  72.806  |  90.792  | 

official/vision/classification/resnet/README.md

@@ -14,6 +14,7 @@
 | ResNet101 | 77.944 | 93.844 | 
 | ResNet152 | 78.582 | 94.130 | 
 | ResNeXt50 32x4d | 77.592 | 93.644 | 
+| ResNeXt101 32x8d| 79.520 | 94.586 |
 
 用户可以通过`megengine.hub`直接加载本目录下定义好的模型，例如：
 

official/vision/classification/resnet/model.py

@@ -367,6 +367,9 @@ def resnext50_32x4d(**kwargs):
     return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
 
 
+@hub.pretrained(
+    "https://data.megengine.org.cn/models/weights/resnext101_32x8d_fbaug_79520_80efb344.pkl"
+)
 def resnext101_32x8d(**kwargs):
     r"""ResNeXt-101 32x8d model from
     `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_