Human-Object Interaction (HOI) detection is a task to predict interactions between humans and objects in an image. In real-world scenarios, HOI detection models are required systematic generalization, i.e., generalization to novel combinations of objects and interactions, because it is highly probable that the train data only cover a limited portion of all possible combinations. However, to our knowledge, no open benchmark or existing work evaluates the systematic generalization in HOI detection. To address this issue, we created two new sets of HOI detection data splits named HICO-DET-SG and V-COCO-SG based on HICO-DET and V-COCO datasets. We evaluated representative HOI detection models on our data splits and observed large degradation in the test performances compared to those on the original datasets. This result shows that systematic generalization is a challenging goal in HOI detection. We hope our new data splits encourage more research toward this goal.