LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection

Understand

Real-time single-stage object detectors based on deep learning still remain less accurate than more complex ones.

• The trade-off between model performance and computational speed is a major challenge.
• In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise between these two objectives.
• To this end, we introduce LapNet, an anchor based detector, trained end-to-end without any sampling strategy.