Semi-Supervised, in the context of object detection, semi-supervised learning refers to a scenario where a model is trained on a dataset that contains a combination of labeled and unlabeled images. Object detection involves identifying and locating objects within an image, typically by drawing bounding boxes around them and assigning class labels.

In traditional supervised object detection, a model is trained on a dataset where each image is meticulously annotated with bounding boxes and class labels for the objects present. However, creating such labeled datasets can be labor-intensive and expensive, especially when dealing with a large number of object categories or when aiming for high precision.