Abstract: A machine learning (ML) operating mode is selected for a camera. A physical orientation of a housing of a camera is determined. In response to determining the orientation, an operating mode from a set of operating modes is selected. The set of operating modes includes at least a ML inference mode and a ML training mode. Based on the selected operating mode, images obtained by an image capturing unit are processed. In the ML inference mode, a ML controller applies a ML model to the images to infer or predict characteristics of the image (e.g., detecting objects within the images). In the ML training mode, the ML controller is configured to cause a ML model to be trained using images captured by the image capturing unit, either local to the camera or on a remotely located computing device.

Abstract: A machine learning (ML) operating mode is selected for a camera. A physical orientation of a housing of a camera is determined. In response to determining the orientation, an operating mode from a set of operating modes is selected. The set of operating modes includes at least a ML inference mode and a ML training mode. Based on the selected operating mode, images obtained by an image capturing unit are processed. In the ML inference mode, a ML controller applies a ML model to the images to infer or predict characteristics of the image (e.g., detecting objects within the images). In the ML training mode, the ML controller is configured to cause a ML model to be trained using images captured by the image capturing unit, either local to the camera or on a remotely located computing device.