Among these methods, CE denotes the simplest baseline which directly uses cross-entropy to train the network on the long-tailed datasets. As expected, this baseline method achieves the worst performance, which reveals the limitation of cross-entropy in dealing with imbalanced data. Although the performance can be improved by using advanced loss functions tailored for long-tailed **data** [3,7,22], these methods ignore the different properties of feature learning and classifier learning. BBN [37] takes a step further by decoupling the head **data** and tailed **data** modeling. But several factors of BBN compromise the full potential of decoupling learning: 1) It unifies the representation for two **data** streams with different properties in the penultimate layer; 2) Crossentropy loss is not an ideal loss for imbalanced **data** in both streams; 3) The final predication in testing phase is calculated as the sum of two prediction functions from two branches with equal weights, which is inconsistent with the training phase. Our methods address such limitations in that: 1) The projection module in our feature learning branch adapts the image representation to a space more suitable for contrastive loss; 2) We use different loss functions to learn the features and classifiers and conclude supervised contrastive loss can be a better substitute for cross-entropy in learning features from imbalanced data; 3) We use a single classifier learning function to predict the class labels for each sample, which avoids the gap between training and testing. Within our methods, SC based hybrid network, a.k.a Hybrid-SC, performs better than the PSC counterpart, a.k.a Hybrid-PSC, but the latter still performs on par with or better than the compared methods.