A method for inspecting ophthalmic lenses and assigning classification to images of the ophthalmic lenses comprising: a) accessing a first image with a computer system, the first image comprising a lens edge image of a first ophthalmic lens wherein the first image provides a view of the lens edge, in focus at a focal plane such that the lens edge is in focus, and contains a single, complete image of the entire lens edge, wherein the first ophthalmic lens represented in the first image is represented in a polar coordinate system, wherein the polar coordinate system has been converted from a cartesian coordinate system; b) identifying a region on the first image to analyze for lens defect by processing the first image with an "edge" convolutional neural network implemented with a hardware processor and a memory of the computer system, the "edge" convolutional neural network being trained using a dataset comprising images with different defect types for lens edge defects; c) generating a class activation map, CAM, based on the first image; d) labelling a defect region on the CAM with at least one of a heatmap and a bounding box to define a labeled CAM and, thereafter outputting the labeled CAM; and e) generating and outputting a classification for the first image with the "edge" convolutional neural network to produce a first classified image, the classification based at least in part on the defect region, the classification being one of a plurality of lens edge defect classes; f) accessing a second image with a computer system, the second image comprising a lens surface image of the first ophthalmic lens, wherein the second image provides a single, complete view of the lens surface, the second image being in focus at a focal plane intersecting the lens above the lens edge such that the lens surface is in focus; g) identifying a region on the second image to analyze for lens defect by processing the second image with a "surface" convolutional neural network implemented with a hardware processor and a memory of the computer system, the "surface" convolutional neural network being trained using a dataset comprising images with different defect types for lens surface defects and being separate from the "edge" convolutional neural network; h) generating a class activation map, CAM, based on the second image; i) labelling a defect region on the CAM with at least one of a heatmap and a bounding box to define a labeled CAM and, thereafter, outputting the labeled CAM; and j) generating and outputting a classification for the second image with the "surface" convolutional neural network to produce a second classified image, the classification based at least in part on the defect region, the classification being one of a plurality of surface defect classes. a) accessing a first image with a computer system, the first image comprising a lens edge image of a first ophthalmic lens wherein the first image provides a view of the lens edge, in focus at a focal plane such that the lens edge is in focus, and contains a single, complete image of the entire lens edge, wherein the first ophthalmic lens represented in the first image is represented in a polar coordinate system, wherein the polar coordinate system has been converted from a cartesian coordinate system; b) identifying a region on the first image to analyze for lens defect by processing the first image with an "edge" convolutional neural network implemented with a hardware processor and a memory of the computer system, the "edge" convolutional neural network being trained using a dataset comprising images with different defect types for lens edge defects; c) generating a class activation map, CAM, based on the first image; d) labelling a defect region on the CAM with at least one of a heatmap and a bounding box to define a labeled CAM and, thereafter outputting the labeled CAM; and e) generating and outputting a classification for the first image with the "edge" convolutional neural network to produce a first classified image, the classification based at least in part on the defect region, the classification being one of a plurality of lens edge defect classes; f) accessing a second image with a computer system, the second image comprising a lens surface image of the first ophthalmic lens, wherein the second image provides a single, complete view of the lens surface, the second image being in focus at a focal plane intersecting the lens above the lens edge such that the lens surface is in focus; g) identifying a region on the second image to analyze for lens defect by processing the second image with a "surface" convolutional neural network implemented with a hardware processor and a memory of the computer system, the "surface" convolutional neural network being trained using a dataset comprising images with different defect types for lens surface defects and being separate from the "edge" convolutional neural network; h) generating a class activation map, CAM, based on the second image; i) labelling a defect region on the CAM with at least one of a heatmap and a bounding box to define a labeled CAM and, thereafter, outputting the labeled CAM; and j) generating and outputting a classification for the second image with the "surface" convolutional neural network to produce a second classified image, the classification based at least in part on the defect region, the classification being one of a plurality of surface defect classes.