adding DUC model server link (pytorch#88)

* adding DUC model server link
* update readme

models/semantic_segmentation/DUC/README.md

@@ -28,9 +28,7 @@ The output of the network is a tensor of shape (1 X `label_num` X `H` * `W`) whe
 ### Postprocessing
 The output tensor is reshaped and resized to give the softmax map of shape (`H` X `W` X `label_num`). The raw label map is computed by doing an argmax on the softmax map. The script [cityscapes_labels.py](cityscapes_labels.py) contains the segmentation category labels and their corresponding color map. Using this the colorized segmented images are generated. Check [duc-postprocess.py](duc-postprocess.py) for code.  
 
-<!--
-To do quick inference with the model, check out [Model Server](https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md/#arcface-resnet100_onnx).
--->
+To do quick inference with the model, check out [Model Server](https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md/#duc-resnet101_onnx).
 
 ## <a name="dset"></a>Dataset
 Cityscapes dataset is used for training and validation. It is a large dataset that focuses on semantic understanding of urban street scenes. It contains 5000 images with fine annotations across 50 cities, different seasons, varying scene layout and background. There are a total of 30 categories in the dataset of which 19 are included for training and evaluation. The training, validation and test set contains 2975, 500 and 1525 fine images, respectively.
@@ -52,9 +50,6 @@ Please note that the dataset is under copyright. Refer to the [citation](https:/
 ## Validation accuracy
 The [mIOU](#metric) score obtained by the models on the validation set are mentioned above and they match with those mentioned in the paper.
 
-## Training
-Coming soon.
-
 ## <a name="metric"></a>Validation
 **mean Intersection Over Union (mIOU)** is the metric used for validation. For each class the intersection over union (IOU) of pixel labels between the output and the target segmentation maps is computed and then averaged over all classes to give us the mean intersection over union (mIOU).