readme: ---
  tags:
  - vision
  - image-segmentation
  datasets:
  - segments/sidewalk-semantic
  finetuned_from:
  - nvidia/mit-b5
  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.
  
  ## Code and Notebook 
  Here is how to use this model to classify an image of the sidewalk dataset:
  
  ```python
  from transformers import SegformerFeatureExtractor, SegformerForImageClassification
  from PIL import Image
  import requests
  
  url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = SegformerFeatureExtractor.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
  model = SegformerForImageClassification.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  
  # model predicts one of the 35 Sidewalk classes
  predicted_class_idx = logits.argmax(-1).item()
  print("Predicted class:", model.config.id2label[predicted_class_idx])
  ```
  
  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}
  }
  ```
  