For training object detection deep learning models, it is generally recommended to have a sizable portion of the training data be background images without objects - typically around 50-70% of the dataset. Some key reasons for this:

- Having a large amount of background data helps the model learn the patterns of background scenes and understand what is "normal", so it can better detect anomalies (objects). Studies like Liu et al. (2016) have shown improvements in detection accuracy by increasing background data.
- Class imbalance is an issue, and having too few background examples could cause the model to be biased towards always predicting objects and having high false positive rates.
- Background data provides useful context for the model to understand the relative scale of objects and their surroundings.

So overall, while the exact percentage will vary based on factors like dataset size and complexity, having a significant portion (majority) of background images is recommended. As an experienced data scientist, you would likely want to experiment with the ratios and evaluate the trade-offs for your particular application and dataset.
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