TensorFlow目标检测API：随机水平翻转的边界框配置问询

Great question! Let me break this down clearly for you.

You’re exactly right about the default behavior: the basic random_horizontal_flip configuration only flips the image itself, leaving your ground truth bounding boxes unadjusted because it doesn’t receive the box data as input.

To fix this and ensure the flip operation syncs with your annotations, you just need to add the boxes: true parameter inside the random_horizontal_flip block. Here’s the updated configuration snippet:

data_augmentation_options {
  random_horizontal_flip {
    boxes: true
  }
}

What this change does: #

• When you enable boxes: true, the preprocessing pipeline automatically passes your ground truth bounding boxes to the random_horizontal_flip() function.
• Internally, the function adjusts the box coordinates to match the flipped image: it mirrors the x-coordinates relative to the image width (new x1 = image width - original x2, new x2 = image width - original x1) while keeping y-coordinates unchanged. This keeps your annotations perfectly aligned with the flipped training images.

Without this parameter, the function only processes the image tensor, leaving your bounding boxes mismatched with the augmented image—this would seriously harm your model’s training accuracy.

内容的提问来源于stack exchange，提问作者snowyBunny