🚀 用于衣物分割的微调Segformer B0模型
本项目是基于ATR数据集对SegFormer模型进行微调,以实现衣物分割任务。该数据集在Hugging Face上的名称为 "mattmdjaga/human_parsing_dataset"。
🚀 快速开始
SegFormer模型经过微调,可用于衣物分割任务。以下是使用该模型进行衣物分割的代码示例:
from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import requests
import matplotlib.pyplot as plt
import torch.nn as nn
extractor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b0_clothes")
model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b0_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 = extractor(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)
💻 使用示例
基础用法
from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import requests
import matplotlib.pyplot as plt
import torch.nn as nn
extractor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b0_clothes")
model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b0_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 = extractor(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)
📄 许可证
本项目采用MIT许可证。
相关信息
属性 |
详情 |
标签 |
视觉、图像分割 |
数据集 |
mattmdjaga/human_parsing_dataset |
示例图片1 |
Person |
示例图片2 |
Person |