text: """1. Getting Started with FCN Pre-trained Models
  ==============================================
  
  This is a quick demo of using GluonCV FCN model.
  Please follow the `installation guide <../index.html>`_ to install MXNet and GluonCV if not yet.
  """
  import mxnet as mx
  from mxnet import image
  from mxnet.gluon.data.vision import transforms
  import gluoncv
  # using cpu
  ctx = mx.cpu(0)
  
  
  ##############################################################################
  # Prepare the image
  # -----------------
  #
  # download the example image
  url = 'https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/1.jpg'
  filename = 'example.jpg'
  gluoncv.utils.download(url, filename)
  
  ##############################################################################
  # load the image
  img = image.imread(filename)
  
  from matplotlib import pyplot as plt
  plt.imshow(img.asnumpy())
  plt.show()
  
  ##############################################################################
  # normalize the image using dataset mean
  transform_fn = transforms.Compose([
      transforms.ToTensor(),
      transforms.Normalize([.485, .456, .406], [.229, .224, .225])
  ])
  img = transform_fn(img)
  img = img.expand_dims(0).as_in_context(ctx)
  
  ##############################################################################
  # Load the pre-trained model and make prediction
  # ----------------------------------------------
  #
  # get pre-trained model
  model = gluoncv.model_zoo.get_model('fcn_resnet50_voc', pretrained=True)
  
  ##############################################################################
  # make prediction using single scale
  output = model.evaluate(img)
  predict = mx.nd.squeeze(mx.nd.argmax(output, 1)).asnumpy()
  
  ##############################################################################
  # Add color pallete for visualization
  from gluoncv.utils.viz import get_color_pallete
  import matplotlib.image as mpimg
  mask = get_color_pallete(predict, 'pascal_voc')
  mask.save('output.png')
  
  ##############################################################################
  # show the predicted mask
  mmask = mpimg.imread('output.png')
  plt.imshow(mmask)
  plt.show()
  
  ##############################################################################
  # More Examples
  # -------------
  #
  #.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/4.jpg
  #    :width: 45%
  #
  #.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/4.png
  #    :width: 45%
  #
  #.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/5.jpg
  #    :width: 45%
  #
  #.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/5.png
  #    :width: 45%
  #
  #.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/6.jpg
  #    :width: 45%
  #
  #.. image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/voc_examples/6.png
  #    :width: 45%
  