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1212.md

@@ -0,0 +1,75 @@
+---
+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 (b5-sized) model fine-tuned on CityScapes
+
+SegFormer model fine-tuned on CityScapes at resolution 640x1280. 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-640-1280")
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
+
+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}
+}
+```

README.md

@@ -1,75 +1,8 @@
----
-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 (b5-sized) model fine-tuned on CityScapes
-
-SegFormer model fine-tuned on CityScapes at resolution 640x1280. 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-640-1280")
-model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
-
-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}
-}
-```
+title: Myseg
+emoji: ☃️
+colorFrom: blue
+colorTo: gray
+sdk: gradio
+sdk_version: 3.44.4
+app_file: app.py
+pinned: false