Thanks for the clarification. Based on your description of looking for guidance as an experienced data scientist, here are some key points on object detection training data composition:

- Having a sizable portion (30-50% according to studies) of background images is important for training the detector to properly classify non-objects and avoid false positives. The exact ratio will depend on factors like dataset size and scene complexity.
- However, the majority of the data should be object images, since the detector must learn positive examples well. A 60-70%+ portion of object images is common.
- The optimal ratio will vary for different use cases and datasets. More complex backgrounds or small datasets may call for a higher background percentage. Testing different ratios on a hold-out set is recommended to determine what works best for your scenario.
- Data augmentation techniques like cropping, flipping, and rotating can also help vary training data and reduce need for as high a percentage of background images.

Does this help summarize the key considerations? Let me know if you would like elaboration on any of the points or have additional questions.
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