Segformer B2 Human

🚀 segformer-b2-fashion

This model is a fine - tuned version of nvidia/mit-b2 on the sayeed99/human_parsing_fashion_dataset dataset. It is designed for image - segmentation tasks, specifically for human parsing in fashion - related images.

🚀 Quick Start

This model is a fine - tuned version of nvidia/mit-b2 on the sayeed99/human_parsing_fashion_dataset dataset.

💻 Usage Examples

Basic 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-b2-human")
model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer-b2-human")

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

Labels : {"0":"Background","1":"shirt, blouse","2":"top, t - shirt, sweatshirt","3":"sweater","4":"cardigan","5":"jacket","6":"vest","7":"pants","8":"shorts","9":"skirt","10":"coat","11":"dress","12":"jumpsuit","13":"cape","14":"glasses","15":"hat","16":"headband, head covering, hair accessory","17":"tie","18":"glove","19":"watch","20":"belt","21":"leg warmer","22":"tights, stockings","23":"sock","24":"shoe","25":"bag, wallet","26":"scarf","27":"umbrella","28":"hood","29":"collar","30":"lapel","31":"epaulette","32":"sleeve","33":"pocket","34":"neckline","35":"buckle","36":"zipper","37":"applique","38":"bead","39":"bow","40":"flower","41":"fringe","42":"ribbon","43":"rivet","44":"ruffle","45":"sequin","46":"tassel","47":"Hair","48":"Sunglasses","49":"Upper - clothes","50":"Left - shoe","51":"Right - shoe","52":"Face","53":"Left - leg","54":"Right - leg","55":"Left - arm","56":"Right - arm"}

📚 Documentation

Framework versions

• Transformers 4.30.0
• Pytorch 2.2.2+cu121
• Datasets 2.18.0
• Tokenizers 0.13.3

📄 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}
}