AlbumentationsX：适用于图像及相关标注的统一增强流水线
AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations
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中文总结 AI 辅助
AlbumentationsX 是一款统一图像及相关标注的增强流水线库，可避免数据错位，支持自定义变换、保存流水线，适用于 PyTorch 框架的训练流程。
中文摘要 AI 辅助
当图像与其标注接收不同的随机变化时，数据增强可能会破坏训练样本。裁剪操作必须对图像、掩码、边界框、关键点、立体视图、视频帧或体素使用相同的坐标，若代码路径分别选择这些值，会导致数据静默错位。AlbumentationsX 将变换列表、概率、标注设置和随机种子保存在一个 Compose 对象中，每次调用仅选择一次随机值并将其应用于训练样本的所有支持部分。该库会将每个对象的掩码、边界框和标签保持在一起，允许项目添加自定义变换，还可保存流水线定义、展示单次调用的操作结果并重新运行该调用。示例中 Compose 位于文件解码为数组后、PyTorch 将样本分组为批次前，AlbumentationsX 会执行声明的变换，从业者仍需根据任务决定翻转、裁剪、颜色变化等操作是否保留正确标签。
英文摘要
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. AlbumentationsX keeps the transform list, probabilities, annotation settings, and random seed in one Compose object. Each call chooses random values once and applies them to every supported part of the training example. The library keeps each object's mask, box, and label together and lets projects add their own transforms. It can also save the pipeline definition, show what happened in one call, and run that call again. The examples place Compose after files have been decoded into arrays and before PyTorch groups examples into a batch. AlbumentationsX executes the declared transforms. Practitioners still decide whether a flip, crop, color change, or other operation preserves the correct label for their task.
发表机构
- Albumentations LLC（阿尔布门泰森有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
