Method for the classification of articles within a stream of articles, wherein the articles each have at least one transparent article wall with a minimum transmission of at least 40% across a wavelength range of at least 150 nm width in the visible wavelength region, such as from 550 nm to 700 nm, the transparent article wall enclosing at least one article volume, the method comprising the steps, - capturing at least a first digital image of an article, - inspecting the digital image for objects on or in the article wall, - determining at least one object parameter, - performing a first classification step using a first classification model, wherein the model classifies the object into at least one of at least two defect classes based on one or more object parameters determined from the at least one captured image, wherein the defect classes comprise at least a first defect class and a second defect class, - performing a second classification step using a set of second classification models for further classifying the objects within the defect classes as pre-classified by the first classification step according to a plurality of defect types, wherein the set of second classification models comprises at least two classification models, wherein at least one classification model within the set of second classification models is specific to a first defect type, and at least one classification model is specific to a second defect type, - wherein the first classification model and/or one or more of the models of the set of second classification models are trained using machine learning, - comparing at least one object parameter of a classified object with a predetermined defect type dependent threshold, - classifying the article as defective or non-defective based on said comparison, - separating defective articles from non-defective articles, wherein the stream of articles comprises at least 30 articles per minute, wherein at least 95% of the articles within the article stream are classified, and wherein at least the first classification model is a deterministic model. - capturing at least a first digital image of an article, - inspecting the digital image for objects on or in the article wall, - determining at least one object parameter, - performing a first classification step using a first classification model, wherein the model classifies the object into at least one of at least two defect classes based on one or more object parameters determined from the at least one captured image, wherein the defect classes comprise at least a first defect class and a second defect class, - performing a second classification step using a set of second classification models for further classifying the objects within the defect classes as pre-classified by the first classification step according to a plurality of defect types, wherein the set of second classification models comprises at least two classification models, wherein at least one classification model within the set of second classification models is specific to a first defect type, and at least one classification model is specific to a second defect type, - wherein the first classification model and/or one or more of the models of the set of second classification models are trained using machine learning, - comparing at least one object parameter of a classified object with a predetermined defect type dependent threshold, - classifying the article as defective or non-defective based on said comparison, - separating defective articles from non-defective articles,