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Object detection is a central downstream task used to test if pre-trainednetwork parameters confer benefits, such as improved accuracy or trainingspeed. The complexity of object detection methods can make this benchmarkingnon-trivial when new architectures, such as Vision Transformer (ViT) models,arrive. These difficulties (e.g., architectural incompatibility, slow training,high memory consumption, unknown training formulae, etc.) have prevented recentstudies from benchmarking detection transfer learning with standard ViT models.In this paper, we present training techniques that overcome these challenges,enabling the use of standard ViT models as the backbone of Mask R-CNN. Thesetools facilitate the primary goal of our study: we compare five ViTinitializations, including recent state-of-the-art self-supervised learningmethods, supervised initialization, and a strong random initializationbaseline. Our results show that recent masking-based unsupervised learningmethods may, for the first time, provide convincing transfer learningimprovements on COCO, increasing box AP up to 4% (absolute) over supervised andprior self-supervised pre-training methods. Moreover, these masking-basedinitializations scale better, with the improvement growing as model sizeincreases.

Benchmarking Detection Transfer Learning with Vision Transformers