例如猫狗大战的任务，如果我有1000个狗的图片，100个猫的图片，d2l怎么处理不平衡数据集？
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D2L can handle imbalanced datasets by using techniques such as oversampling, undersampling, and data augmentation. Oversampling involves duplicating samples from the minority class to balance the dataset, while undersampling involves removing samples from the majority class. Data augmentation is a technique used to generate new data points from existing ones by applying random transformations such as rotation, flipping, and cropping. This can help increase the size of the dataset and improve model performance