Learning Lightweight Object Detectors via Progressive Knowledge Distillation

Resource-constrained perception systems such as edge computing and vision-for-robotics require vision models to be both accurate and lightweight in computation and memory usage. Knowledge distillation is one effective strategy to improve the performance of lightweight classification models, but it is less well-explored for structured outputs such as object detection and instance segmentation, where the variable number of outputs and complex internal network modules complicate the distillation. In this paper, we propose a simple yet surprisingly effective sequential approach to knowledge distillation that progressively transfers the knowledge of a set of teachers to a given lightweight student. Our approach is inspired by curriculum learning: To distill knowledge from a highly accurate but complex teacher model, we construct a sequence of teachers to help the student gradually adapt. Our progressive distillation strategy can be easily combined with existing distillation mechanisms to consistently maximize student performance in various settings. To the best of our knowledge, we are the first to successfully distill knowledge from Transformer-based teacher detectors to convolution-based students, and unprecedentedly boost the performance of ResNet-50 based RetinaNet from 36.5% to 42.0% AP and Mask R-CNN from 38.2% to 42.5% AP on the MS COCO benchmark.