PaddlePaddle Data Augmentation Guide

Various data augmentation methods can be utilized in the PaddlePaddle framework through the paddle.vision.transforms module. Here are some commonly used data augmentation methods:

1. Flip the image horizontally at random.

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    # 其他数据增强方法
])

1. Randomly flip the input vertically.

transform = transforms.Compose([
    transforms.RandomVerticalFlip(),
    # 其他数据增强方法
])

1. Randomly rotating

transform = transforms.Compose([
    transforms.RandomRotation(degrees=45),
    # 其他数据增强方法
])

1. Randomly resizing and cropping in various sizes

transform = transforms.Compose([
    transforms.RandomResizedCrop(size=(224, 224)),
    # 其他数据增强方法
])

1. Convert to tensor

transform = transforms.Compose([
    transforms.ToTensor(),
    # 其他数据增强方法
])

Combining these data augmentation methods together can create a data augmentation transform, which can then be used to enhance images during data loading. For example:

train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True)