sayeed99/segformer_b3_clothes

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}
}