ModelListedExternal

Segformer B2 Clothes

SegFormer model fine-tuned on ATR dataset for clothes segmentation but can also be used for human segmentation. The dataset on hugging face is called "mattmdjaga/humanparsingdataset".

Sourcehuggingface.co/lsau/segformer_b2_clothes/tree/22d64bb61ea66c6173e75b0d81bcd4694fe3d051

KindOtherVersionRevision 22d64bb

Model source

Kind: Other
File size: 104–105 MB
Version: vRevision 22d64bb
Updated: Sep 27, 2026
Downloads: 7
Tasks: Image segmentation
Source: Hugging Face

Source description

Segformer B2 fine-tuned for clothes segmentation

SegFormer model fine-tuned on ATR 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.

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.

BibTeX entry and citation info

@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}
}

Sources

1 source

Hugging FacePrimaryVersion 22d64bb61ea66c6173e75b0d81bcd4694fe3d051

huggingface.co/lsau/segformer_b2_clothes/tree/22d64bb61ea66c6173e75b0d81bcd4694fe3d051

Verified Sep 27

Model artifacts

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model.safetensors

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onnx/model.onnx

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pytorch_model.bin

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Source excerpts

2 excerpts

ReadmeSep 27huggingface.co/lsau/segformer_b2_clothes

--- license: other 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 |...

NoteSep 27huggingface.co/lsau/segformer_b2_clothes

Source context: 7 downloads · 0 likes · Pipeline image-segmentation · Repo lsau/segformer_b2_clothes