Computer-implemented method, the method comprising at least the following steps: - providing digital images (101, 103, 105, 301) 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 (44, 46), 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 (44, 46) 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 (101, 103, 105, 301) 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 (44, 46), 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 (44, 46) 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.