Title: An Improved Baseline for Masked Contrastive Learning. Abstract: Contrastive learning has significantly advanced self-supervised visual representation learning, making linear probe accuracy close to its supervised counterpart on ImageNet. However, vision transformers pre-trained with contrastive learning typically underperform those pre-trained with masked image prediction, when evaluated on fine-tuning benchmarks, e.g., image classification, object detection, and segmentation. In this paper, we improve the fine-tuning transfer performance of prior state-of-the-art contrastive approaches, e.g., MoCo-v3 and BYOL, from the following empirical perspectives: (i) applying masking strategies to input views; (ii) studying and comparing the effectiveness of Batch Normalization and Layer Normalization in projection and prediction heads; (iii) investigating the effectiveness of data augmentation and finding lighter augmentation during pre-training improves fine-tuning performance. As a result, we come up with a better baseline for contrastive transformers that outperforms baseline MoCo-v3 by $0.6\%$ on ImageNet fine-tuning, and $2.1$ mAP on MS COCO detection and segmentation benchmark for ViT-B, rivaling that of masked image prediction. Furthermore, our approach is significantly more efficient than MoCo-v3 due to the use of masking. These results suggest that, contrary to recent trends, contrastive learning remains competitive with masked image prediction on standard vision tasks.