license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
widget:
- src: >-
https://media.istockphoto.com/id/515788534/photo/cheerful-and-confidant.jpg?s=612x612&w=0&k=20&c=T0Z4DfameRpyGhzevPomrm-wjZp7wmGjpAyjGcTzpkA=
example_title: Person
- src: >-
https://storage.googleapis.com/pai-images/1484fd9ea9d746eb9f1de0d6778dbea2.jpeg
example_title: Person
datasets:
- sayeed99/human_parsing_fashion_dataset
model-index:
- name: segformer-b2-human
results: []
pipeline_tag: image-segmentation
library_name: transformers
segformer-b2-fashion
This model is a fine-tuned version of nvidia/mit-b2 on the sayeed99/human_parsing_fashion_dataset dataset.
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 : {"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"}
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}
}