Abstract: Examples of methods for training using rendered images are described herein. In some examples, a method may include, for a set of iterations, randomly positioning a three-dimensional (3D) object model in a virtual space with random textures. In some examples, the method may include, for the set of iterations, rendering a two-dimensional (2D) image of the 3D object model in the virtual space and a corresponding annotation image. In some examples, the method may include training a machine learning model using the rendered 2D images and corresponding annotation images.

Abstract: A region of interest is extracted from a captured image of a physical object. An autoencoder model is applied to the extracted region of interest to reconstruct the region of interest. A location of an anomaly of the physical object within the extracted region of interest, if any, is identified based on the extracted and reconstructed regions of interest.

Abstract: Examples of methods for training using rendered images are described herein. In some examples, a method may include, for a set of iterations, randomly positioning a three-dimensional (3D) object model in a virtual space with random textures. In some examples, the method may include, for the set of iterations, rendering a two-dimensional (2D) image of the 3D object model in the virtual space and a corresponding annotation image. In some examples, the method may include training a machine learning model using the rendered 2D images and corresponding annotation images.

Abstract: An example method includes capturing a target image of a print product printed by a printer. The method also includes aligning the target image with a reference image corresponding to the target image. The method further includes analyzing the reference image and the target image using a machine learning model. The method includes labeling each of a plurality of pixels as having a defect based on the analysis. The label is applied to each individual pixel of the plurality of pixels.

Abstract: An example of an apparatus is provided. The apparatus includes a preprocessing engine to preprocess an image of a print into a reduced format wherein the reduced format includes a plurality of pixels. The apparatus further includes a segmentation analysis engine to generate a plurality of labels. Each label of the plurality of labels is associated with a pixel of the plurality of pixels. The plurality of labels identifies each pixel of the plurality of pixels as a defective pixel or a non-defective pixel. The apparatus also includes a rendering engine to display defects in the reduced format based on the plurality of labels.