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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
---
# Segformer B2 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".
**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**.
```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("mattmdjaga/segformer_b2_clothes")
model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_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}
}
``` |