Resource efficient training of machine learning models that predict stochastic spread

A system comprising:

• one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• obtaining input features representative of a region of space;
• processing an input comprising the input features through a machine learning (ML) model to generate a prediction describing predicted features of the region of space;
• obtaining result features describing the region of space;
• determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric comprising one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, wherein determining the value of the at least one evaluation metric that relates the predicted features and the result features comprises:
• determining a first value of the distance score that relates the predicted features and the result features;
• determining a second value of the pyramiding density error that relates the predicted features and the result features;
• determining a third value of the min-max IOU score that relates the predicted features and the result features;
• and
• comparing the first value, the second value, and the third value to a first threshold, a second threshold, and a third threshold, respectively;
• and
• training the ML model responsive to the at least one evaluation metric whose value satisfies a respective threshold.

A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:

• obtaining input features representative of a region of space;
• processing an input comprising the input features through a machine learning (ML) model to generate a prediction describing predicted features of the region of space;
• obtaining result features describing the region of space;
• determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric comprising one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, wherein determining the value of the at least one evaluation metric that relates the predicted features and the result features comprises:
• determining a first value of the distance score that relates the predicted features and the result features;
• determining a second value of the pyramiding density error that relates the predicted features and the result features;
• determining a third value of the min-max IOU score that relates the predicted features and the result features;
• and
• comparing the first value, the second value, and the third value to a first threshold, a second threshold, and a third threshold, respectively;
• and
• training the ML model responsive to the at least one evaluation metric whose value satisfies a respective threshold.