### SegFormer Finetuned for Seal Segmentation

#### BaseOn https://huggingface.co/nvidia/mit-b0


#### 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:

```
from transformers import AutoImageProcessor, SegformerForSemanticSegmentation
from PIL import Image
import requests

image_processor = AutoImageProcessor.from_pretrained("Fantast/segformer-mit-b0-finetuned-for-seal")
model = SegformerForSemanticSegmentation.from_pretrained("Fantast/segformer-mit-b0-finetuned-for-seal")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
list(logits.shape)
```

For more code examples, we refer to the documentation.

License
The license for this model can be found here.

BibTeX entry and citation info
@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}
}

---
license: mit
---