<p>When studying bounding box-based detectors, it's not clear to me if data augmentation includes adding random rotations.</p>
<p>If random rotations are added, how is the new bounding box calculated?</p>

CC BY-SA 4.0
2020-11-15T22:43:52.340Z
I don't have much practical experience with them, but, I guess, if you rotate the images, you just need to rotate the boxes (i.e. labels) too. Check [this](https://github.com/anhtuan85/Data-Augmentation-for-Object-Detection/blob/master/augmentation.ipynb) (which may not be a reliable implementation: I don't know!).
nbro
CC BY-SA 4.0
2020-11-15T22:47:51.700Z
The problem is the predicted boxes are not rotated - so would you just create a new box from the rotated images bounding box corners? This works but might make the predicted box a little larger,.
FourierFlux
<deep-learning><object-detection><object-recognition><yolo><data-augmentation>
If random rotations are included in the data augmentation process, how are the new bounding boxes calculated?
FourierFlux
# If random rotations are included in the data augmentation process, how are the new bounding boxes calculated?

(Asked by: username_0 on 2020-11-15)

When studying bounding box-based detectors, it's not clear to me if data augmentation includes adding random rotations.

If random rotations are added, how is the new bounding box calculated?

**Tags:** deep-learning, object-detection, object-recognition, yolo, data-augmentation

> **Comments:**
> **username_1**: I don't have much practical experience with them, but, I guess, if you rotate the images, you just need to rotate the boxes (i.e. labels) too. Check [this](https://github.com/anhtuan85/Data-Augmentation-for-Object-Detection/blob/master/augmentation.ipynb) (which may not be a reliable implementation: I don't know!).  
> **username_0**: The problem is the predicted boxes are not rotated - so would you just create a new box from the rotated images bounding box corners? This works but might make the predicted box a little larger,.  

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## 0 answers

