Method for creating a deep learning-based model and device for implementing the model created by said method

A method for creating a deep learning based model to solve a question for a pre-determined category of objects, comprising:

• I) providing a plurality of samples to form a dataset, store such dataset and partition such dataset into three sample groups:
• training sample group, evaluation sample group, and prediction sample group,
• II) determining an architecture of a neural network and providing a corresponding neural network,
• III) providing the training sample group of samples from the dataset to the neural network, and thereby creating a model,
• IV) causing the model to learn from the training sample group of samples in the dataset by training the model through detecting features and patterns,
• V) saving the trained model,
• VI) providing the group of evaluation samples from the dataset to the trained model to output a result for each sample, and evaluate the accuracy of the results,
• VII) if the accuracy of the results according to the evaluation of step VI does not exceed a predetermined threshold, adjusting the trained model and repeating steps IV to VII;
• if the accuracy of the results according to the evaluation of step VI exceeds a predetermined accuracy threshold, validating the model;
• wherein the samples of the training sample group of the dataset are produced by the following procedure:
• i) a stage is provided, in which an object from the pre-determined object category is placed,
• ii) a background of a first color is placed on the stage and a light source of a first wavelength illuminates the stage-object set, which is a first combination of stage background color and illumination wavelength of the stage-object set,
• iii) at least one image of the scene-object set is captured,
• iv) one or more different combinations of stage background color and illumination wavelength of the stage-object set are arranged consecutively and at least one image of the stage-object set is captured with each combination,
• V the captured images are processed by an object detection component and cropped in such a way that the parts of the images that do not contain the object are removed,
• vi) each of the processed images are tagged with a label that identifies the category of the object, the resulting labelled images being part of the plurality of samples to form the dataset.