Graphic manipulation applications are often used to convert raster graphics, which depict content in an image using a grid of pixels, to vector graphics, which include data identifying various shapes (e.g., lines, curves, etc.) that are used to depict various content objects in an image. More specifically, but not by way of limitation, this disclosure relates to adapting image vectorization operations using machine learning. This disclosure relates generally to machine-learning systems that transform an image or other graphic to enhance its visual quality by converting the image from a raster graphic to a vector graphic. The content-creation computing system executes a vectorization algorithm that performs the first customization operation using the input raster graphic as an input and displays or otherwise outputs a vector graphic generated by the vectorization algorithm. The content-creation computing system selects the first customization operation as the customization specific to the input raster graphic. The content-creation computing system generates, with the multi-label classifier, a first probability that a first customization operation is applicable to the input raster graphic and a second probability that a second customization operation is applicable to the input raster graphic, wherein the first probability is greater than the second probability. The content-creation computing system provides the input raster graphic to a customization-identification network having a multi-label classifier. A content-creation computing system transforms an input raster graphic into a output vector graphic by applying a customization specific to visual characteristics of the input raster graphic.
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