I am working with object detection using Tensorflow. I have mix of 7-8 classes. Initially, we had an image classification model and now moving it to object detection model. For once class alone, the object to be detected occupies the entire image. Can we have the bounding box dimension to be the entire width and height of the image? Will it hinder the performance?
It shouldn't hinder the performance as long as there's enough such examples in the training set. the OD API clips detections outbounding the image, so in these cases the resulting bounding box would be the of the entire image (or one axis would be the entire size, and the other less, depending on the object occupation). Assuming your OD model uses anchors, make sure you have anchors which are responsible for such cases (i.e. with scale of about the entire image).