Relationship between the bounding box dimension and detection rate performance

[Rajesh-ParaxialTech]

Hello

Which of the following bounding boxes used while training a nodule detection model results in a good performing nodule detector ?

1. A tighter bounding box as shown below
   

or 2. Or a loose bounding box like below

Thanks
Rajesh

[KumoLiu]

Hi @Rajesh-ParaxialTech, the choice between tighter and looser bounding boxes should be balanced based on the specific application and dataset characteristics.
Here are some tips:

• If high-quality annotation, precise annotations are available, tighter bounding boxes can be beneficial. For datasets with more variability in annotation quality, looser bounding boxes might be more appropriate.
• For models that are highly sensitive to overfitting, such as complex deep learning models, looser bounding boxes might help in preventing overfitting.
• Implementing effective post-processing techniques like non-maximum suppression (NMS) can help mitigate some issues with loose bounding boxes by refining the final detected bounding boxes.

I would suggest experimenting with both types during the validation phase can provide insights into which approach yields the best performance for your specific nodule detection model.

Hope it helps, thanks.