Create Readme.md

Readme.md

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+---
+license: other
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+widget:
+- src: >-
+    https://media.istockphoto.com/id/515788534/photo/cheerful-and-confidant.jpg?s=612x612&w=0&k=20&c=T0Z4DfameRpyGhzevPomrm-wjZp7wmGjpAyjGcTzpkA=
+  example_title: Person
+- src: >-
+    https://storage.googleapis.com/pai-images/1484fd9ea9d746eb9f1de0d6778dbea2.jpeg
+  example_title: Person
+datasets:
+- sayeed99/fashion_segmentation
+model-index:
+- name: segformer-b3-fashion
+  results: []
+pipeline_tag: image-segmentation
+---
+
+
+# segformer-b3-fashion
+
+This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the sayeed99/fashion_segmentation dataset using original image sizes without resizing.
+
+
+```python
+from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+import torch.nn as nn
+
+processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer-b3-fashion")
+model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer-b3-fashion")
+
+url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
+
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits.cpu()
+
+upsampled_logits = nn.functional.interpolate(
+    logits,
+    size=image.size[::-1],
+    mode="bilinear",
+    align_corners=False,
+)
+
+pred_seg = upsampled_logits.argmax(dim=1)[0]
+plt.imshow(pred_seg)
+```
+
+Labels : {"0":"Unlabelled", "1": "shirt, blouse", "2": "top, t-shirt, sweatshirt", "3": "sweater", "4": "cardigan", "5": "jacket", "6": "vest", "7": "pants", "8": "shorts", "9": "skirt", "10": "coat", "11": "dress", "12": "jumpsuit", "13": "cape", "14": "glasses", "15": "hat", "16": "headband, head covering, hair accessory", "17": "tie", "18": "glove", "19": "watch", "20": "belt", "21": "leg warmer", "22": "tights, stockings", "23": "sock", "24": "shoe", "25": "bag, wallet", "26": "scarf", "27": "umbrella", "28": "hood", "29": "collar", "30": "lapel", "31": "epaulette", "32": "sleeve", "33": "pocket", "34": "neckline", "35": "buckle", "36": "zipper", "37": "applique", "38": "bead", "39": "bow", "40": "flower", "41": "fringe", "42": "ribbon", "43": "rivet", "44": "ruffle", "45": "sequin", "46": "tassel"}
+
+### Framework versions
+
+- Transformers 4.30.0
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.13.3
+
+
+### 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}
+}