readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-segmentation
  datasets:
  - segments/sidewalk-semantic
  ---
  
  # SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset
  
  SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. 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.
  
  ## Intended uses & limitations
  
  You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
  
  ```python
  from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
  from PIL import Image
  import requests
  
  feature_extractor = SegformerFeatureExtractor(reduce_labels=True)
  model = SegformerForSemanticSegmentation.from_pretrained("ChainYo/segformer-sidewalk")
  
  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
  ```
  
  For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).