Methods, apparatuses, devices and storage media for training object detection network and for detecting object

Provided are a training and detection method and apparatus of an object detection network and a device and a storage medium. The method of training an object detection network includes: obtaining, by performing object detection for images in an image data set input into the object detection network and for each of one or more objects involved in each of the images, a confidence levels that the object is predicted as each of a plurality of preset categories, wherein the plurality of preset categories comprise one or more labeled categories labeled by the image data set and one or more non-labeled categories unlabeled by the image data set; for each of the objects, according to a non-concerned confidence level that the object is predicted as each of the non-labeled categories, determining reference labeling information of the object with respect to each of the non-labeled categories; for each of the objects, according to the confidence level that the object is predicted as each of the preset categories, actual labeling information of the object and the reference labeling information of the object with respect to each of the non-labeled categories, determining loss information that the object is predicted as each of the preset categories; and adjusting a network parameter of the object detection network based on the loss information that each of the objects is predicted as each of the preset categories.