这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。

Last update: Dec 28, 2022

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Overview

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在Pytorch当中的实现

性能情况

训练数据集	权值文件名称	测试数据集	输入图片大小	mIOU
VOC12+SBD	deeplab_mobilenetv2.pth	VOC-Val12	512x512	72.59
VOC12+SBD	deeplab_xception.pth	VOC-Val12	512x512	76.95

所需环境

torch==1.2.0

注意事项

代码中的deeplab_mobilenetv2.pth和deeplab_xception.pth是基于VOC拓展数据集训练的。训练和预测时注意修改backbone。

文件下载

训练所需的deeplab_mobilenetv2.pth和deeplab_xception.pth可在百度网盘中下载。
链接: https://pan.baidu.com/s/1KgLMbprQshlcpKgug9ECFg 提取码: 4ir8

VOC拓展数据集的百度网盘如下：
链接: https://pan.baidu.com/s/1BrR7AUM1XJvPWjKMIy2uEw 提取码: vszf

训练步骤

a、训练voc数据集

1、将我提供的voc数据集放入VOCdevkit中（无需运行voc_annotation.py）。
2、在train.py中设置对应参数，默认参数已经对应voc数据集所需要的参数了，所以只要修改backbone和model_path即可。
3、运行train.py进行训练。

b、训练自己的数据集

1、本文使用VOC格式进行训练。
2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的SegmentationClass中。
3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。
4、在训练前利用voc_annotation.py文件生成对应的txt。
5、在train.py文件夹下面，选择自己要使用的主干模型和下采样因子。本文提供的主干模型有mobilenet和xception。下采样因子可以在8和16中选择。需要注意的是，预训练模型需要和主干模型相对应。
6、注意修改train.py的num_classes为分类个数+1。
7、运行train.py即可开始训练。

预测步骤

a、使用预训练权重

1、下载完库后解压，如果想用backbone为mobilenet的进行预测，直接运行predict.py就可以了；如果想要利用backbone为xception的进行预测，在百度网盘下载deeplab_xception.pth，放入model_data，修改deeplab.py的backbone和model_path之后再运行predict.py，输入。

img/street.jpg

可完成预测。
2、在predict.py里面进行设置可以进行fps测试、整个文件夹的测试和video视频检测。

b、使用自己训练的权重

1、按照训练步骤训练。
2、在deeplab.py文件里面，在如下部分修改model_path、num_classes、backbone使其对应训练好的文件；model_path对应logs文件夹下面的权值文件，num_classes代表要预测的类的数量加1，backbone是所使用的主干特征提取网络。

_defaults = {
    #----------------------------------------#
    #   model_path指向logs文件夹下的权值文件
    #----------------------------------------#
    "model_path"        : 'model_data/deeplab_mobilenetv2.pth',
    #----------------------------------------#
    #   所需要区分的类的个数+1
    #----------------------------------------#
    "num_classes"       : 21,
    #----------------------------------------#
    #   所使用的的主干网络
    #----------------------------------------#
    "backbone"          : "mobilenet",
    #----------------------------------------#
    #   输入图片的大小
    #----------------------------------------#
    "input_shape"       : [512, 512],
    #----------------------------------------#
    #   下采样的倍数，一般可选的为8和16
    #   与训练时设置的一样即可
    #----------------------------------------#
    "downsample_factor" : 16,
    #--------------------------------#
    #   blend参数用于控制是否
    #   让识别结果和原图混合
    #--------------------------------#
    "blend"             : True,
    #-------------------------------#
    #   是否使用Cuda
    #   没有GPU可以设置成False
    #-------------------------------#
    "cuda"              : True,
}

3、运行predict.py，输入

img/street.jpg

可完成预测。
4、在predict.py里面进行设置可以进行fps测试、整个文件夹的测试和video视频检测。

评估步骤

1、设置get_miou.py里面的num_classes为预测的类的数量加1。
2、设置get_miou.py里面的name_classes为需要去区分的类别。
3、运行get_miou.py即可获得miou大小。

Reference

https://github.com/ggyyzm/pytorch_segmentation
https://github.com/bonlime/keras-deeplab-v3-plus

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Comments

输出坐标

if self.mix_type == 0: seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3)) for c in range(self.num_classes): seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8') seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8') seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')

请问如果我想得到这个猫的左上方坐标和右下方坐标该怎么print呢

opened by SSTato 3
TF/Keras 版和 PyTorch 版的性能差异

首先感谢提供多种版本的代码。我注意到同样是VOC12数据集，deeplabv3-plus-tf2/deeplabv3-plus-keras在测试集上的结果显著优于deeplabv3-plus-pytorch版本。是否因为前者使用的是dice_loss_with_CE而后者只使用DiceLoss进行训练？还是有其它别的原因？

opened by fyang93 3
关于替换为我自己的数据集训练的问题

我替换为自己的数据集进行训练，但是一直遇到ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 256, 1, 1])，这个报错，百度了都说是batch_size可能多出来了，把drop_last设置为true就好了。可是我看你的代码里是设置的true啊，请问up怎么解决呢，我这边cuda：10.1 pytorch：1.2.0

opened by Codingaworld 1
模型输出问题

老师好，想请问一下，默认导出onnx之后，模型输出是三通道的RGB图像，怎么将输出改为0或1的标签呀，比如我就对两个类进行分割（背景和目标），然后我希望输出二值图（背景为0，目标为1），就好像训练的数据集的标签一样模型转onnx格式之后，输出是3通道的float图，仔细观察后通道1和通道2都有点奇怪，通道3是我想要的（需要格式转换），怎么控制网络直接输出灰度标签呢

opened by YuriGao 4

Releases(v3.0)

v3.0(Apr 22, 2022)
重要更新

支持step、cos学习率下降法。

支持adam、sgd优化器选择。

支持学习率根据batch_size自适应调整。

支持不同预测模式的选择，单张图片预测、文件夹预测、视频预测、图片裁剪。

更新summary.py文件，用于观看网络结构。

增加了多GPU训练。

Source code(tar.gz)
Source code(zip)
v2.0(Mar 4, 2022)
重要更新

更新train.py文件，增加了大量的注释，增加多个可调整参数。

更新predict.py文件，增加了大量的注释，增加fps、视频预测、批量预测等功能。

更新deeplab.py文件，增加了大量的注释，增加先验框选择、置信度、非极大抑制等参数。

合并get_dr_txt.py、get_gt_txt.py和get_map.py文件，通过一个文件来实现数据集的评估。

更新voc_annotation.py文件，增加多个可调整参数。

更新summary.py文件，用于观看网络结构。

Source code(tar.gz)
Source code(zip)
v1.0(Sep 5, 2021)

Source code(tar.gz)
Source code(zip)
deeplab_mobilenetv2.pth(22.40 MB)
deeplab_xception.pth(209.61 MB)
mobilenet_v2.pth.tar(13.54 MB)
xception_pytorch_imagenet.pth(145.25 MB)

Owner

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