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--- |
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license: other |
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tags: |
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- vision |
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datasets: |
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- imagenet_1k |
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widget: |
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- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg |
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example_title: House |
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- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg |
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example_title: Castle |
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--- |
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# SegFormer (b0-sized) encoder pre-trained-only |
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SegFormer encoder fine-tuned on Imagenet-1k. 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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This repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes. |
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## Intended uses & limitations |
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You can use the model for fine-tuning of 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 SegformerImageProcessor, SegformerForImageClassification |
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from PIL import Image |
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import requests |
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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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image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0") |
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model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0") |
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inputs = image_processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# model predicts one of the 1000 ImageNet classes |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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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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