Human beings not only have the ability of recogniz-ing novel unseen classes, but also can incrementally incorporatethe new classes to existing knowledge preserved. However, thezero-shot learning models assume that all seen classes should beknown beforehand, while incremental learning models cannotrecognize unseen classes. This paper introduces a novel andchallenging task of Incrementally Zero-Shot Detection (IZSD),a practical strategy for both zero-shot learning and class-incremental learning in real-world object detection. An innovativeend-to-end model – IZSD-EVer was proposed to tackle this taskthat requires incrementally detecting new classes and detectingthe classes that have never been seen. Specifically, we proposea novel extreme value analyzer to simultaneously detect objectsfrom old seen, new seen, and unseen classes. Additionally andtechnically, we propose two innovative losses, i.e., background-foreground mean squared error loss alleviating the extremeimbalance of the background and foreground of images, andprojection distance loss aligning the visual space and semanticspaces of old seen classes. Experiments demonstrate the efficacyof our model in detecting objects from both the seen and unseenclasses, outperforming the alternative models on Pascal VOC andMSCOCO datasets.