An out-of-distribution (OOD) detector may be provided to efficiently identify OOD inputs to trained machine learning models, to prevent passing misleading predictions from the trained models to downstream systems. During training of a machine learning model including a deep neural network (e.g., an object detection and/or object classifier model based on sensor data), an out-of-distribution detector may be constructed by modeling the density of the intermediate features of the deep neural network (e.g., using a Gaussian mixture model). In some examples, the intermediate features may be within a finetuned model based on a pretrained foundation model, and the machine learning model may be finetuned with regularization toward the original weights of the pretrained model. During inference, the machine learning model may output predictions, such as multilabel object detection and/or classification outputs, and the out-of-distribution detector may be used to efficiently generate confidence values associated with the outputs of the model.