In this work, we explore effective contrastive learning strategies and tailor them to learn better image representations from imbalanced **data** in order to boost long-tailed image classification. Specifically, we propose a novel hybrid network structure composed of a contrastive loss for learning image representations and a cross-entropy loss to learn classifiers. To embody the idea that better features make better classifiers, we follow a curriculum to progressively transit the learning from feature learning to classifier learning. We realize two variants of supervised contrastive learning strategies, as shown in Fig. 1, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. By doing this, less skewed features and consequently less biased classifiers are expected to be obtained.