作者主頁(文火冰糖的硅基工坊)：文火冰糖（王文兵）的博客_文火冰糖的硅基工坊_CSDN博客

本文網址：https://blog.csdn.net/HiWangWenBing/article/details/121184391

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目錄

步驟1：torchvision概述

步驟2：如何獲取框架提供的預訓練模型

步驟3：常見預訓練模型地址

步驟4：通過IE瀏覽器手工下載模型

步驟5：模型加載

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步驟1：torchvision概述

[Pytorch系列-37]：工具集 - torchvision庫詳解(數據集、數據預處理、模型)_文火冰糖（王文兵）的博客-CSDN博客作者主頁(文火冰糖的硅基工坊)：文火冰糖（王文兵）的博客_文火冰糖的硅基工坊_CSDN博客本文網址：目錄第1章Pytorch常見的工具集簡介第2章Pytorch的torchvision工具集簡介第3章torchvision.datasets 簡介3.1 簡介3.2 支持的數據集列表第4章torchvision.models簡介4.1 簡介4.2 支持的模型4.3構造具有隨機權重的模型4.4 使用預預訓練好的模型第5章 torchvision.tr...https://blog.csdn.net/HiWangWenBing/article/details/121149809

步驟2：如何獲取框架提供的預訓練模型

import torchvision.models as modelsalexnet = models.alexnet(pretrained=True) # AlexNet vgg16 = models.vgg16(pretrained=True) # VGG16 resnet18 = models.resnet18(pretrained=True) # ResetNet模型 googlenet = models.googlenet(pretrained=True) # googlenet inception = models.inception_v3(pretrained=True) # inceptionsqueezenet = models.squeezenet1_0(pretrained=True) # 序列網絡 densenet = models.densenet161(pretrained=True) # 稠密網絡 shufflenet = models.shufflenet_v2_x1_0(pretrained=True)mobilenet_v2 = models.mobilenet_v2(pretrained=True) mobilenet_v3_large = models.mobilenet_v3_large(pretrained=True) mobilenet_v3_small = models.mobilenet_v3_small(pretrained=True) resnext50_32x4d = models.resnext50_32x4d(pretrained=True) wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) mnasnet = models.mnasnet1_0(pretrained=True)#efficientnet_b0 = models.efficientnet_b0() #efficientnet_b1 = models.efficientnet_b1() #efficientnet_b2 = models.efficientnet_b2() #efficientnet_b3 = models.efficientnet_b3() #efficientnet_b4 = models.efficientnet_b4() #efficientnet_b5 = models.efficientnet_b5() #efficientnet_b6 = models.efficientnet_b6() #efficientnet_b7 = models.efficientnet_b7() #regnet_y_400mf = models.regnet_y_400mf() #regnet_y_800mf = models.regnet_y_800mf() #regnet_y_1_6gf = models.regnet_y_1_6gf() #regnet_y_3_2gf = models.regnet_y_3_2gf() #regnet_y_8gf = models.regnet_y_8gf() #regnet_y_16gf = models.regnet_y_16gf() #regnet_y_32gf = models.regnet_y_32gf() #regnet_x_400mf = models.regnet_x_400mf() #regnet_x_800mf = models.regnet_x_800mf() #regnet_x_1_6gf = models.regnet_x_1_6gf() #regnet_x_3_2gf = models.regnet_x_3_2gf() #regnet_x_8gf = models.regnet_x_8gf() #regnet_x_16gf = models.regnet_x_16gf() #regnet_x_32gf = models.regnet_x_32gf()

下列鏈接就是框架提供的預訓練模型：Downloading: "https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth" to C:\Users\Administrator/.cache\torch\hub\checkpoints\alexnet-owt-4df8aa71.pth

步驟3：常見預訓練模型地址

將下載好的模型放在~/.cache/torch/checkpoints文件夾中即可（windows為C:\用戶名\.cache\torch\.checkpoints）

Resnet:

model_urls = {
? ? 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
? ? 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
? ? 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
? ? 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
? ? 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

inception:

model_urls = {
? ? # Inception v3 ported from TensorFlow
? ? 'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
}

Densenet:

model_urls = {
? ? 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
? ? 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
? ? 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
? ? 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
}

Alexnet:

model_urls = {
? ? 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}

vggnet:

model_urls = {
? ? 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
? ? 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
? ? 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
? ? 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
? ? 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
? ? 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
? ? 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
? ? 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}

步驟4：通過IE瀏覽器手工下載模型

https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth

步驟5：模型加載

[Pytorch系列-40]：卷積神經網絡 - 模型的恢復/加載 - 搭建LeNet-5網絡與MNIST數據集手寫數字識別_文火冰糖（王文兵）的博客-CSDN博客作者主頁(文火冰糖的硅基工坊)：文火冰糖（王文兵）的博客_文火冰糖的硅基工坊_CSDN博客本文網址：https://blog.csdn.net/HiWangWenBing/article/details/121132377目錄第1章 模型的恢復與加載1.1 概述1.2模型的恢復與加載類型1.3模型的保存的API函數：代碼示例1.4模型的恢復與加載的API函數：代碼示例第2章 定義前向運算：加載CFAR10數據集2.1 前置條件2.2 定義數據預處理（數據強化）...https://blog.csdn.net/HiWangWenBing/article/details/121181287

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作者主頁(文火冰糖的硅基工坊)：文火冰糖（王文兵）的博客_文火冰糖的硅基工坊_CSDN博客

本文網址：https://blog.csdn.net/HiWangWenBing/article/details/121184391

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