|
--- |
|
license: other |
|
tags: |
|
- vision |
|
- image-segmentation |
|
datasets: |
|
- scene_parse_150 |
|
widget: |
|
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg |
|
example_title: House |
|
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg |
|
example_title: Castle |
|
--- |
|
|
|
# SegFormer (b4-sized) model fine-tuned on ADE20k |
|
|
|
SegFormer model fine-tuned on ADE20k at resolution 512x512. 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-b4-finetuned-ade-512-512") |
|
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b4-finetuned-ade-512-512") |
|
|
|
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} |
|
} |
|
``` |
|
|