Tobias Cornille commited on
Commit
4b17d85
1 Parent(s): 661d9c6

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +1 -1
README.md CHANGED
@@ -9,7 +9,7 @@ widget:
9
  example_title: Brugge
10
  ---
11
  # SegFormer (b0-sized) model fine-tuned on Segments.ai Sidewalk Semantic
12
- SegFormer model fine-tuned on 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).
13
  ## Model description
14
  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.
15
  ## Intended uses & limitations
 
9
  example_title: Brugge
10
  ---
11
  # SegFormer (b0-sized) model fine-tuned on Segments.ai Sidewalk Semantic
12
+ 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).
13
  ## Model description
14
  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.
15
  ## Intended uses & limitations