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segformer_b3_clothes

Image Segmentation
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segformer_b3_clothes / README.md
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metadata
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
pipeline_tag: image-segmentation

Segformer B3 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".

NEW - Training code. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version and a blog post with it to make it more friendly. 

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("sayeed99/segformer_b3_clothes")
model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer_b3_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}
}