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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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widget: |
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- src: >- |
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https://media.istockphoto.com/id/515788534/photo/cheerful-and-confidant.jpg?s=612x612&w=0&k=20&c=T0Z4DfameRpyGhzevPomrm-wjZp7wmGjpAyjGcTzpkA= |
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example_title: Person |
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- src: >- |
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https://storage.googleapis.com/pai-images/1484fd9ea9d746eb9f1de0d6778dbea2.jpeg |
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example_title: Person |
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datasets: |
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- sayeed99/fashion_segmentation |
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model-index: |
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- name: segformer-b3-fashion |
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results: [] |
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pipeline_tag: image-segmentation |
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--- |
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# segformer-b3-fashion |
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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. |
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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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processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer-b3-fashion") |
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model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer-b3-fashion") |
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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 = processor(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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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"} |
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### Framework versions |
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- Transformers 4.30.0 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.3 |
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### License |
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The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). |
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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2105-15203, |
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author = {Enze Xie and |
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Wenhai Wang and |
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Zhiding Yu and |
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Anima Anandkumar and |
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Jose M. Alvarez and |
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Ping Luo}, |
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title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with |
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Transformers}, |
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journal = {CoRR}, |
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volume = {abs/2105.15203}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2105.15203}, |
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eprinttype = {arXiv}, |
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eprint = {2105.15203}, |
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timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
