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@@ -43,4 +43,31 @@ upsampled_logits = nn.functional.interpolate(
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  pred_seg = upsampled_logits.argmax(dim=1)[0]
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  plt.imshow(pred_seg)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  pred_seg = upsampled_logits.argmax(dim=1)[0]
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  plt.imshow(pred_seg)
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+ ```
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+ ### License
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+
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+ The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-2105-15203,
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+ author = {Enze Xie and
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+ Wenhai Wang and
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+ Zhiding Yu and
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+ Anima Anandkumar and
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+ Jose M. Alvarez and
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+ Ping Luo},
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+ title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
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+ Transformers},
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+ journal = {CoRR},
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+ volume = {abs/2105.15203},
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+ year = {2021},
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+ url = {https://arxiv.org/abs/2105.15203},
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+ eprinttype = {arXiv},
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+ eprint = {2105.15203},
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+ timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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  ```