Bounding Box Augmentation for Object Detection with Albumentations

If you're new to image augmentation, two earlier posts provide the broader context: Image Augmentation in Practice — Lessons from 10 Years of Training CV Models and Building Albumentations Designing Image Augmentation Pipelines for Generalization This post builds on those ideas and focuses on one specific practical question: how to apply augmentations correctly when your labels are bounding boxes. It is based on the Albumentations documentation, with additional context and examples for object detection workflows. Albumentations is an open-source image augmentation library with 15k+ GitHub stars and 140M+ downloads. Contents Bounding box formats Building a detection pipeline Passing labels and metadata A.BboxParams explained Cropping strategies Common mistakes Further reading When you augment images for object detection, bounding box coordinates must transform in sync with the pixels. A horizontal flip mirrors the image — but if the box coordinates stay the same, every box now points at