Background class for Object detection

Adding background images to my dataset has helped drastically in the reduction of false positives. The images I added have annotations, but no bounding box, and thus don’t belong to any particular class.

I’m curious if this is the correct approach, or would it make sense to create an actual background class with bounding boxes?


@Isaac_Padberg Welcome to Tensorflow Forum!

Adding background images to your dataset is a valid approach to reduce false positives in object detection models. Annotating background images with bounding boxes is not necessary, as the model can learn to distinguish between objects and background without explicit annotations. In fact, annotating background images with bounding boxes could potentially introduce noise and negatively impact the model’s performance.

The reason why adding background images is beneficial is that it helps the model learn the distribution of background pixels and distinguish them from objects. By providing the model with examples of background images, the model is less likely to misclassify background pixels as objects.

Here’s why annotating background images with bounding boxes is not necessary:

  • Object detection models are trained to distinguish between objects and background, not to classify every pixel in the image.
  • Background pixels are typically not spatially localized, unlike objects that have a defined shape and location.
  • Creating bounding boxes for background pixels would be a time-consuming and unnecessary task.

Instead of annotating background images with bounding boxes, you can focus on annotating the objects of interest in your dataset. This will provide the model with the necessary information to learn the appearance and location of objects, while the background images will help the model understand the context in which objects appear.

Let us know if this helps!