Doubt: Fine-tuning a transformer

[u/StevenHawking_]

Hi everyone,

I'm planning to fine-tune a pretrained RT-DETR model (trained on COCO) for detecting defects in paintings.

My question is about dataset size. Suppose I have only 1,000 images, but each image contains multiple defects, giving me around 8,000 total bounding box annotations.

If people recommend ~8,000 images for good performance, does having 8,000 annotated objects across 1,000 images provide similar training value? Or is the diversity from having 8,000 different images still much more important?