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README.md

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-# SegFormer (b0-sized) model fine-tuned on Segments.ai [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic).
-SegFormer model fine-tuned on [Segments.ai](https://segments.ai) 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). 
+# SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic.
+SegFormer model fine-tuned on [Segments.ai](https://segments.ai) [`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.
 ### How to use