title: How to train deeplabv3 on custom dataset on pytorch?
body: <p>I am trying to do image segmentation and I got to know the Google work of DeepLabv3.</p>
  <p>This is the reference to the paper:
  <a href="https://arxiv.org/abs/1706.05587" rel="nofollow noreferrer">https://arxiv.org/abs/1706.05587</a></p>
  <p>Chen, L.C., Papandreou, G., Schroff, F. and Adam, H., 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.</p>
  <p>This architecture is trained to do segmentation of the 20+1 classes of the Pascal VOC 2012 Dataset (20 foreground and 1 background class).</p>
  <p>Pytorch provides pre-trained deeplabv3 on Pascal dataset, I would like to train the same architecture on cityscapes. Therefore, there are different classes with respect to the Pascal VOC dataset. I would like to know what is the efficient way to do it?</p>
  <p>For now this is the only code I wrote:</p>
  <pre><code>import torch
  model = torch.hub.load('pytorch/vision:v0.6.0', 'deeplabv3_resnet101', pretrained=True)
  model.eval()
  </code></pre>