Update README.md

README.md

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+### 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
 ---