Segformer B3 Clothes

🚀 Segformer B3 fine-tuned for clothes segmentation

This is a SegFormer model fine-tuned on the ATR dataset for clothes segmentation. It can also be applied to human segmentation. The dataset on Hugging Face is named "mattmdjaga/human_parsing_dataset".

🚀 Quick Start

Installation

To use this model, you need to install the transformers library. You can install it using the following command:

pip install transformers

Usage

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

✨ Features

• Multi - purpose: Can be used for both clothes segmentation and human segmentation.
• New Training Code: Training code is available, and more user - friendly versions will be added soon.

📚 Documentation

Evaluation

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.

🔧 Technical Details

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}
}