Update README.md

README.md

@@ -1,3 +1,50 @@
 ---
 license: mit
+tags:
+- vision
+- image-segmentation
+datasets:
+- segments/sidewalk-semantic
+widget:
+- src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg
+  example_title: Brugge
 ---
+
+# SegFormer (b5-sized) model fine-tuned on sidewalk-semantic dataset.
+SegFormer model fine-tuned on SegmentsAI [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic). It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). 
+
+## Model description
+SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
+
+## Notebook and Code 
+You can go through its detailed notebook [here](https://github.com/ZohebAbai/Deep-Learning-Projects/blob/master/09_HF_Image_Segmentation_using_Transformers.ipynb).
+
+For more code examples, refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).
+
+## License
+
+The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
+
+## BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2105-15203,
+  author    = {Enze Xie and
+               Wenhai Wang and
+               Zhiding Yu and
+               Anima Anandkumar and
+               Jose M. Alvarez and
+               Ping Luo},
+  title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
+               Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2105.15203},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2105.15203},
+  eprinttype = {arXiv},
+  eprint    = {2105.15203},
+  timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```