torchvision.datasets
时间: 2023-11-08 21:06:26 浏览: 39
torchvision.datasets is a module in the PyTorch library that provides access to popular image datasets for machine learning tasks.
It includes datasets such as:
1. MNIST: a dataset of handwritten digits
2. CIFAR10 and CIFAR100: datasets of small color images
3. ImageNet: a dataset of over a million labeled images in 1000 categories
4. COCO: a large-scale object detection, segmentation, and captioning dataset
These datasets can be easily downloaded and used for training and testing deep learning models. The module also provides tools for data augmentation and preprocessing, making it easier to work with these datasets in PyTorch.
相关问题
torchvision.datasets.
The `torchvision.datasets` module provides a collection of popular datasets like CIFAR-10, CIFAR-100, MNIST, ImageNet and many more. These datasets can be used for training and testing various deep learning models.
The module provides a simple API to download and load these datasets. The datasets are returned as a subclass of `torch.utils.data.Dataset` and can be used with `torch.utils.data.DataLoader` to create a dataloader for training and testing.
Example usage:
```
import torchvision.datasets as datasets
# Download and load the CIFAR-10 dataset
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=None)
# Create a dataloader for training
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
```
In this example, we download the CIFAR-10 dataset and create a dataloader for training with a batch size of 32 and shuffling the data. The `transform` parameter can be used to apply transforms to the data, such as resizing, cropping, and normalization.
torchvision.datasets用法
torchvision.datasets是PyTorch中用于加载和处理各种标准数据集的模块。它包括了许多广泛使用的数据集,例如MNIST、CIFAR10、CIFAR100、ImageNet等。
使用torchvision.datasets可以轻松地加载这些标准数据集并将其转换为PyTorch的张量。以下是一个简单的例子:
```python
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)
# 获取一个样本
image, label = train_dataset[0]
```
在这个例子中,我们使用MNIST数据集。我们首先定义了一个train_dataset和test_dataset,它们分别代表训练集和测试集。我们还定义了一个transforms,它将数据集中的图像转换为PyTorch张量。我们还指定了数据集的根目录(root)和是否下载数据集(download)。
在数据集被加载后,我们可以像使用Python列表那样访问数据集。例如,train_dataset[0]将返回第一个样本的图像和标签。
除了MNIST之外,还有许多其他的数据集可供使用。要加载其他数据集,只需将datasets.MNIST替换为其他数据集的名称即可。