Nice  object detection projects can be a lot of fun (and frustrating, in a good way). The dataset you choose really depends on what you’re detecting, but some are just solid all around.
If you’re working on general-purpose detection — stuff like people, cars, animals, etc.  COCO is probably the go-to. It’s massive, diverse, and has great annotations (bounding boxes, segmentation masks, keypoints). Pascal VOC is a bit older, but still clean and good for smaller-scale testing or benchmarking.
If you’re dealing with specific domains, there are some really well-curated niche datasets:

• Open Images (by Google) – huge and broad, but the annotations can be messy.
• KITTI – great if you’re doing object detection in a self-driving or road scene context.
• Roboflow Universe – surprisingly helpful. It has tons of community-uploaded datasets, and sometimes you’ll find exactly what you need, already annotated and ready to go.

Honestly, I’ve found that combining a good base dataset (like COCO) with a small, clean custom dataset for your specific use case usually gives the best results. You don’t always need 100,000 images just the right ones.
If you tell me what you’re trying to detect, I might be able to suggest something more specific.