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Runki
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Begginer
Asked: 2025-05-31T01:32:26+00:00 In:

I'm working on an object detection project. Which datasets have you found most useful?

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Selecting the right dataset is crucial for object detection tasks. Share datasets that have been effective in your computer vision projects.

object detection

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2 Answers

  1. Maya
    Begginer

    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.

  2. Hassaan Arif
    Enlightened

    For object detection, datasets are like your training buddies some are way more reliable than others.

    COCO is the classic gym partner: huge, diverse, and always pushing you hard with lots of everyday objects.

    If you want something a bit simpler but still solid, Pascal VOC is like the friendly coach who keeps things straightforward.

    If you’re feeling adventurous, Open Images has a ton of data but can sometimes feel like herding cats with all its labels.

    For niche tasks, don’t forget to check out specialized datasets like KITTI for self-driving cars or Wider Face for faces because one size rarely fits all.

    Pick your dataset like you pick your squad someone who challenges you but won’t make you want to quit after the first round.