| --- | |
| license: other | |
| tags: | |
| - vision | |
| - image-segmentation | |
| datasets: | |
| - cityscapes | |
| widget: | |
| - src: https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png | |
| example_title: Road | |
| --- | |
| # SegFormer (b0-sized) model fine-tuned on CityScapes | |
| SegFormer model fine-tuned on CityScapes at resolution 768x768. 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). | |
| 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. | |
| ## 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.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-768-768") | |
| model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-768-768") | |
| 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#). | |
| ### 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} | |
| } | |
| ``` | |