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JavisPeng
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unet liver

Unet network for liver CT image segmentation

data preparation

structure of project

  --project
    main.py
     --data
        --train
        --val
data and trained weight link: https://pan.baidu.com/s/1dgGnsfoSmL1lbOUwyItp6w code: 17yr

all dataset you can access from: https://competitions.codalab.org/competitions/15595

training

python main.py train

testing

load the last saved weight ``` python main.py test --ckpt=weights_19.pth

```

数据准备

项目文件分布如下

  --project
    main.py
     --data
        --train
        --val

数据和权重可以使用百度云下载 链接:

链接: https://pan.baidu.com/s/1dgGnsfoSmL1lbOUwyItp6w 提取码: 17yr

全部数据集: https://competitions.codalab.org/competitions/15595

模型训练

python main.py train

测试模型训练

加载权重,默认保存最后一个权重

python main.py test --ckpt=weights_19.pth

多类别

修改2个地方即可:unet最后一层的通道数设置为类别数;损失函数使用CrossEntropyLoss ```python bathsize,imgsize,num_classes=2,3,4

model = Unet(3, num_classes)

criterion = nn.CrossEntropyLoss()

assume the pred is the output of the model

pred=torch.rand(bathsize,numclasses,imgsize,imgsize) target=torch.randint(numclasses,(bathsize,imgsize,imgsize)) loss=criterion(pred,target) ```

Demo

liver

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