Dump Readme. Need to update properly.

README.md

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+---
+license: mit
+tags:
+- vision
+- image-segmentation
+widget:
+- src: >-
+    https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
+  example_title: Person
+- src: >-
+    https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
+  example_title: Person
+datasets:
+- mattmdjaga/human_parsing_dataset
+pipeline_tag: image-segmentation
+---
+# Segformer B3 fine-tuned for clothes segmentation
+
+SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
+The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
+
+
+**NEW** - 
+**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version
+ and a blog post with it to make it more friendly. 
+
+```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_clothes")
+model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer_b3_clothes")
+
+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: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf"
+
+### Evaluation
+
+|  Label Index  |    Label Name    | Category Accuracy | Category IoU |
+|:-------------:|:----------------:|:-----------------:|:------------:|
+|       0       |    Background    |       0.99        |     0.99     |
+|       1       |        Hat       |       0.73        |     0.68     |
+|       2       |        Hair      |       0.91        |     0.82     |
+|       3       |    Sunglasses    |       0.73        |     0.63     |
+|       4       |  Upper-clothes   |       0.87        |     0.78     |
+|       5       |       Skirt      |       0.76        |     0.65     |
+|       6       |       Pants      |       0.90        |     0.84     |
+|       7       |       Dress      |       0.74        |     0.55     |
+|       8       |       Belt       |       0.35        |     0.30     |
+|       9       |    Left-shoe     |       0.74        |     0.58     |
+|      10       |   Right-shoe     |       0.75        |     0.60     |
+|      11       |       Face       |       0.92        |     0.85     |
+|      12       |    Left-leg      |       0.90        |     0.82     |
+|      13       |   Right-leg      |       0.90        |     0.81     |
+|      14       |    Left-arm      |       0.86        |     0.74     |
+|      15       |   Right-arm      |       0.82        |     0.73     |
+|      16       |        Bag       |       0.91        |     0.84     |
+|      17       |       Scarf      |       0.63        |     0.29     |
+
+Overall Evaluation Metrics:
+- Evaluation Loss: 0.15
+- Mean Accuracy: 0.80
+- Mean IoU: 0.69
+
+### 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}
+}
+```