Computer-implemented method, the method comprising at least the following steps: - providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, - configuring an image annotation tool for classifying, for each image, each pixel by visually reviewing the respective image pixel-wise, using at least one image segmentation technique and annotating each pixel with a pigment label and/or a pigment class label in alignment with the visual appearance and the formula associated with the respective image, - providing, for each image , an associated pixel-wise annotated image , - training a first neural network , implemented and running on at least one computer processor , with the provided digital images as input and the associated pixel-wise annotated images as output wherein the first neural network is trained to correlate every pixel in a respective input image with a pigment label and/or pigment class label of a respective associated annotated image , - making the trained first neural network available in the at least one computer processor for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, wherein the pigment labels and/or the pigment class labels include both pigment or pigment class specific labels and background specific labels, - determining and/or outputting, for each input image, based on the assigned pigment labels and/or pigment class labels, a statistic of corresponding identified pigments and/or pigment classes, respectively. - providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, - configuring an image annotation tool for classifying, for each image, each pixel by visually reviewing the respective image pixel-wise, using at least one image segmentation technique and annotating each pixel with a pigment label and/or a pigment class label in alignment with the visual appearance and the formula associated with the respective image, - providing, for each image , an associated pixel-wise annotated image , - training a first neural network , implemented and running on at least one computer processor , with the provided digital images as input and the associated pixel-wise annotated images as output wherein the first neural network is trained to correlate every pixel in a respective input image with a pigment label and/or pigment class label of a respective associated annotated image , - making the trained first neural network available in the at least one computer processor for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, wherein the pigment labels and/or the pigment class labels include both pigment or pigment class specific labels and background specific labels, - determining and/or outputting, for each input image, based on the assigned pigment labels and/or pigment class labels, a statistic of corresponding identified pigments and/or pigment classes, respectively. <EOS>