基于Pytorch实现优秀的自然图像分割框架！(包括FCN、U-Net和Deeplab)

Last update: Dec 21, 2022

Overview

语义分割学习实验-基于VOC数据集

usage：

下载VOC数据集，将JPEGImages SegmentationClass两个文件夹放入到data文件夹下。
终端切换到目标目录，运行python train.py -h查看训练

(torch) [email protected]:~/语义分割$ python train.py -h
usage: train.py [-h] [-m {Unet,FCN,Deeplab}] [-g GPU]

choose the model

optional arguments:
  -h, --help            show this help message and exit
  -m {Unet,FCN,Deeplab}, --model {Unet,FCN,Deeplab}
                        输入模型名字
  -g GPU, --gpu GPU     输入所需GPU

选择模型和GPU编号进行训练，例如运行python train.py -m Unet -g 0

预测需要手动修改predict.py中的模型

Owner

Li Xiang

A CVer, love Deep Learning and AI Security💁🏻, have questions to contact me！

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基于Pytorch实现优秀的自然图像分割框架！(包括FCN、U-Net和Deeplab)

Related tags

Overview

语义分割学习实验-基于VOC数据集

usage：

Owner

Li Xiang

git《Beta R-CNN: Looking into Pedestrian Detection from Another Perspective》(NeurIPS 2020) GitHub:[fig3]

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