This disclosure relates to a model training method, apparatus, vehicle, storage medium, and
computer program product, belonging to the field of
image processing technology. The method includes iteratively executing the following steps to obtain a target
network model: inputting a collected image of a storage location into an initial
network model to predict the predicted three-dimensional coordinates of a target point in the storage location; converting the predicted three-dimensional coordinates into predicted two-dimensional coordinates; updating the network parameters in the initial
network model based on the loss value between the predicted two-dimensional coordinates and the actual two-dimensional coordinates; the actual two-dimensional coordinates are obtained by labeling the target point in the storage location image; if the loss value meets the convergence condition, the initial network model that meets the convergence condition is used as the target network model; the target network model is used to obtain the three-dimensional coordinates of the target point from the storage location image. Using the model training method proposed in this disclosure, the cost of manual labeling can be reduced while obtaining highly accurate predicted three-dimensional coordinates.