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--- |
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license: mit |
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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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datasets: |
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- mattmdjaga/human_parsing_dataset |
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--- |
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# Segformer B0 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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The dataset on hugging face is called "mattmdjaga/human_parsing_dataset". |
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```python |
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from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation |
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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 = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b0_clothes") |
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model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b0_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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``` |
