Input partitioning for deep learning of large image data
摘要:
In an approach to processing large high dimensional images in parallel without losing accuracy, one or more computer processors determine a required amount of graphics processing unit memory for an image. The one or more computer processors determine one or more coordinate partitions based on the determined required amount of graphics and one or more characteristics of the image. The one or more computer processors determine a padding size for each of the determined one or more coordinate partitions. The one or more computer processors partition the image based on the one or more determined coordinate partitions and the determined padding size. The one or more computer processors generate a prediction for the partitioned image utilizing a trained model.
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