Abstract: Systems/methods provide more efficient training of ML models used to classify objects within images and videos. The systems/methods provide an augmented training dataset that crops each image and retains a portion of the image context. The image cropping may be done by manually, semi-manually, or automatically identifying a location or coordinates of an object within an image, then cropping a predefined area around the image. The cropping may be performed once for each image, or multiple crops may be performed for each image, each cropping involving a different predefined area around the image depending on the method used to identify the coordinates of the object within the image. The resulting augmented set of images is then used to train, or further train, the ML models. Such an arrangement provides ML models that have improved object disambiguation and greater classification accuracy, and are especially useful in applications involving transfer learning.

Abstract: A method for selecting samples in a dataset to be labelled in a classification task, wherein the dataset includes N samples belonging to K classes. The method includes: extracting features of the N samples using a feature extractor; statistically inferring a probability distribution of the extracted features in the K classes, to obtain the density of each sample with respect to the K classes distributions; and selecting samples to be labelled using the density.

Abstract: A method for training at least one model able to predict a power consumption or production of at least one electric equipment, also called target.