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--- |
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tags: |
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- vision |
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- image-segmentation |
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datasets: |
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- segments/sidewalk-semantic |
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widget: |
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- src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg |
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example_title: Brugge |
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--- |
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# SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic. |
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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). |
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## Model description |
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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. |
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### How to use |
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Here is how to use this model to classify an image of the sidewalk dataset: |
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```python |
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from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation |
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from PIL import Image |
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import requests |
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feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") |
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model = SegformerForSemanticSegmentation.from_pretrained("segments-tobias/segformer-b0-finetuned-segments-sidewalk") |
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url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) |
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``` |
