项目自荐 | 语义分割经典网络模型 PyTorch 实现

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项目地址：https://github.com/Charmve/Semantic-Segmentation-PyTorch
类别：Python、机器学习
目前已经完成的网络、模型
1. Vanilla FCN: FCN32, FCN16, FCN8, in the versions of VGG, ResNet and DenseNet respectively (Fully convolutional networks for semantic segmentation)
2. U-Net (U-net: Convolutional networks for biomedical image segmentation)
3. SegNet (Segnet: A deep convolutional encoder-decoder architecture for image segmentation)
4. PSPNet (Pyramid scene parsing network)
5. GCN (Large Kernel Matters)
6. DUC, HDC (understanding convolution for semantic segmentation)
7. Mask-RCNN (paper, code from FAIR, code PyTorch)
项目后续更新计划：

我将首先在PyTorch中实现The Image Segmentation Paper Top10 Net。

[ ] DeepLab v3
[ ] RefineNet
[ ] ImageNet
[ ] GoogleNet
[ ] More dataset (e.g. ADE)
项目描述：PyTorch implementation for Semantic Segmentation, include FCN, U-Net, SegNet, GCN, PSPNet, Deeplabv3, Deeplabv3+, Mask R-CNN, DUC, GoogleNet, and more dataset
推荐理由：最简单实用的语义分割源代码！对初学者友好，对工程实践者便利！
示例代码：（可选）长度：1-20 行

U-Net两步网络的实现


class _EncoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels, dropout=False):
        super(_EncoderBlock, self).__init__()
        layers = [
            nn.Conv2d(in_channels, out_channels, kernel_size=3),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        ]
        if dropout:
            layers.append(nn.Dropout())
        layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
        self.encode = nn.Sequential(*layers)

    def forward(self, x):
        return self.encode(x)

class _DecoderBlock(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels):
        super(_DecoderBlock, self).__init__()
        self.decode = nn.Sequential(
            nn.Conv2d(in_channels, middle_channels, kernel_size=3),
            nn.BatchNorm2d(middle_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(middle_channels, middle_channels, kernel_size=3),
            nn.BatchNorm2d(middle_channels),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=2, stride=2),
        )

截图：（可选）gif/png/jpg

521xueweihan / HelloGitHub

项目自荐 | 语义分割经典网络模型 PyTorch 实现 #1683

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