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README.md
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README.md
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---
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license: other
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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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- cityscapes
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widget:
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- src: https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png
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example_title: Road
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---
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# SegFormer (b1-sized) model fine-tuned on CityScapes
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SegFormer model fine-tuned on CityScapes at resolution 1024x1024. 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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Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
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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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## Intended uses & limitations
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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.
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### How to use
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Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
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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-b1-finetuned-cityscapes-1024-1024")
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model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
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url = "http://images.cocodataset.org/val2017/000000039769.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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```
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For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).
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### License
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The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-2105-15203,
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author = {Enze Xie and
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Wenhai Wang and
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Zhiding Yu and
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Anima Anandkumar and
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Jose M. Alvarez and
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Ping Luo},
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title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
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Transformers},
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journal = {CoRR},
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volume = {abs/2105.15203},
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year = {2021},
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url = {https://arxiv.org/abs/2105.15203},
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eprinttype = {arXiv},
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eprint = {2105.15203},
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timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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{
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{
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"architectures": [
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"architectures": [
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"SegformerForSemanticSegmentation"
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"SegformerForImageSegmentation"
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],
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],
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"attention_probs_dropout_prob": 0.0,
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"attention_probs_dropout_prob": 0.0,
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"classifier_dropout_prob": 0.1,
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"classifier_dropout_prob": 0.1,
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{
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{
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"align": false,
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"crop_size": [
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512,
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512
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],
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"do_normalize": true,
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"do_normalize": true,
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"do_pad": true,
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"do_random_crop": false,
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"do_resize": true,
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"do_resize": true,
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"feature_extractor_type": "SegformerFeatureExtractor",
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"feature_extractor_type": "SegformerFeatureExtractor",
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"image_mean": [
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"image_mean": [
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0.456,
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0.456,
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0.406
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0.406
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],
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],
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"image_scale": [
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512,
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512
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],
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"image_std": [
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"image_std": [
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0.229,
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0.229,
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0.224,
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0.224,
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0.225
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0.225
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],
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],
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"reduce_labels": false,
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"keep_ratio": false,
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"padding_value": 0,
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"reduce_zero_label": false,
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"resample": 2,
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"resample": 2,
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"size": 1024
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"segmentation_padding_value": -100,
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"size_divisor": 32
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}
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}
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