Hi Kevin Jansen,
To answer your question regarding how optimized CNN could be, a single CNN can be effective in both classification and coordinate prediction, and it is possible to achieve good results with a single CNN. However, as you mentioned, using two sequential CNNs may have some advantages, such as better accuracy and easier interpretation of the output.
Regarding your concern about the ground truth label for images without a ball, using [0,0] as the ground truth coordinate for images without a ball may introduce a bias for coordinate prediction of actual ball images, as the CNN may learn to predict coordinates closer to [0,0] for all images, regardless of whether there is a ball or not. One solution to this is to use a different label, such as [-1,-1], for images without a ball, which is outside the range of the possible coordinates. However, this may not be a perfect solution, as the CNN may still predict coordinates close to [-1,-1] for actual ball images.
Another possible solution is to use a CNN architecture with both a classification and regression layer. This can help to reduce the bias introduced by the ground truth labels for images without a ball, as the classification layer can help the CNN distinguish between images with and without a ball, and the regression layer can predict the coordinates only for the images with a ball. This approach can also help to improve the accuracy of both classification and coordinate prediction tasks.
Please refer the following documentation pages for more information on training CNN for Regression:
