  Real-world data exhibiting skewed distributions pose a serious challenge to
existing object detectors. Moreover, the samplers in detectors lead to shifted
training label distributions, while the tremendous proportion of background to
foreground samples severely harms foreground classification. To mitigate these
issues, in this paper, we propose Logit Normalization (LogN), a simple
technique to self-calibrate the classified logits of detectors in a similar way
to batch normalization. In general, our LogN is training- and tuning-free (i.e.
require no extra training and tuning process), model- and label
distribution-agnostic (i.e. generalization to different kinds of detectors and
datasets), and also plug-and-play (i.e. direct application without any bells
and whistles). Extensive experiments on the LVIS dataset demonstrate superior
performance of LogN to state-of-the-art methods with various detectors and
backbones. We also provide in-depth studies on different aspects of our LogN.
Further experiments on ImageNet-LT reveal its competitiveness and
generalizability. Our LogN can serve as a strong baseline for long-tail object
detection and is expected to inspire future research in this field. Code and
trained models will be publicly available at https://github.com/MCG-NJU/LogN.
