torchvision.datasets.CIFAR10怎么下载
时间: 2023-04-04 09:01:16 浏览: 80
你可以使用以下代码下载CIFAR10数据集:
```
import torchvision
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True)
```
其中,`root`参数指定数据集的存储路径,`train`参数指定是否下载训练集,`download`参数指定是否下载数据集。
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torchvision.datasets.CIFAR10
torchvision.datasets.CIFAR10 is a dataset class in PyTorch that provides access to the CIFAR-10 image classification dataset. CIFAR-10 consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 testing images.
This dataset is commonly used in image classification tasks as a benchmark dataset for evaluating the performance of deep learning models. The classes in CIFAR-10 are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
To use the CIFAR-10 dataset in PyTorch, you can create an instance of the CIFAR10 class and pass in the location of the data and whether you want to use the training or testing dataset. For example:
```python
import torchvision.datasets as datasets
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True)
```
This will download the CIFAR-10 dataset to the './data' directory and create a CIFAR10 object for the training and testing datasets.
torchvision.datasets.CIFAR10无法使用
引用中提到,torchvision.datasets.CIFAR10是torch.utils.data.Dataset的子类,因此可以传递给torch.utils.data.DataLoader来使用。根据引用中的示例代码,可以看到在使用torchvision.datasets.CIFAR10时,需要指定数据集的根目录、是否训练集、是否下载、以及数据的转换方式。同时还可以指定批量大小、是否随机打乱数据和使用的工作线程数等参数。在引用中也提到,如果要训练集和测试集都使用相同的数据集,并且已经下载了数据集,可以将download参数设置为False来避免重新下载数据。所以如果torchvision.datasets.CIFAR10无法使用,可能是因为没有指定正确的路径、没有下载数据集或者其他参数设置有误。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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