Handling Imbalanced Data in PyTorch

There are several common methods for handling imbalanced data in PyTorch.

1. mass

weights = [0.1, 0.9] # 类别权重
criterion = nn.CrossEntropyLoss(weight=torch.Tensor(weights))

1. The torch module’s data utilities
2. Randomly samples elements from a dataset with a weighting factor.

from torch.utils.data import WeightedRandomSampler

weights = [0.1, 0.9] # 类别权重
sampler = WeightedRandomSampler(weights, len(dataset), replacement=True)

1. Data augmentation: Data augmentation involves expanding the dataset by adding variations of minority class samples, in order to balance the number of samples across different categories.

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.RandomResizedCrop(224),
])

The above are several commonly used methods for dealing with imbalanced data, in practical applications, appropriate methods can be selected based on the characteristics and needs of the dataset.