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Added examples
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- README.md +40 -1
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.ipynb_checkpoints
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test.ipynb
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README.md
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---
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license: wtfpl
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---
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Segformer
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---
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license: wtfpl
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tags:
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- vision
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- image-segmentation
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widget:
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- src: https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
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example_title: Person
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- src: https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
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example_title: Person
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---
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# Segformer B2 fine-tuned for clothes segmentation
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SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation
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```python
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from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation
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from PIL import Image
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import requests
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import matplotlib.pyplot as plt
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import torch.nn as nn
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extractor = AutoFeatureExtractor.from_pretrained("mattmdjaga/segformer_b2_clothes")
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model = SegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
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url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits.cpu()
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upsampled_logits = nn.functional.interpolate(
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logits,
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size=image.size[::-1],
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mode="bilinear",
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align_corners=False,
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)
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pred_seg = upsampled_logits.argmax(dim=1)[0]
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plt.imshow(pred_seg)
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```
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