Author

V. Iglovikov

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Preprint Sep 2026

RGB Input Pipelines: Throughput, GPU Memory, and Transformation Coverage

An image-augmentation pipeline must deliver a complete batch before a model can use it. We compare seven input paths from five libraries, starting with RGB JPEG files and ending with a synchronized CUDA float16 batch. We manually matched transformation recipes and parameters across libraries to make the workloads as co...

V. Iglovikov · 0 citations

Preprint Aug 2026

AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations

Augmentation can corrupt a training example when an image and its annotations receive different random changes. A crop must use the same coordinates for the image, mask, boxes, keypoints, stereo views, video frames, or volume. Code paths that choose these values separately can silently misalign the data. Albumentations...

V. Iglovikov · 1 citation