How do you deal with missing or incomplete datasets in computer vision?

[Igor335566]

Hey everyone!
I’m curious how people here handle dataset shortages for object detection / segmentation projects (YOLO, Mask R-CNN, etc.).

A few quick questions:

1. How often do you run into a lack of good labeled data for your models?

2. What do you usually do when there’s no dataset that fits — collect real data, label manually, or use synthetic/simulated data?

3. Have you ever tried generating synthetic data (Unity, Unreal, etc.) — did it actually help?

Would love to hear how different teams or researchers deal with this.

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[glenn-jocher]

Great questions, Igor—data scarcity is common; our go‑to is: fine‑tune a pretrained YOLO11 on a small real “seed” set, use strong augmentations, then add targeted synthetic data for rare/unsafe cases while keeping validation/test strictly real to track sim‑to‑real gap; you can mix real+synthetic like this and tune a few hyps:

# dataset.yaml
train:
  - data/real/images/train
  - data/synth/images/train
val: data/real/images/val
names: [class0, class1]  # edit to your classes

yolo train model=yolo11n.pt data=dataset.yaml epochs=100 imgsz=640 mosaic=1.0 mixup=0.15 copy_paste=0.1

If you generate with Unity/Unreal, prioritize domain randomization over photorealism and ensure perfect labels; for context see the synthetic data overview and sim‑to‑real tips in our articles on Synthetic Data, the YOLO data augmentation guide, and our Data collection and annotation strategies.

[Igor335566]

Thank you very much for the answer!

[glenn-jocher]

You’re welcome—if you end up trying a mixed real + synthetic setup, feel free to share a brief summary of your pipeline and results here, as those practical examples are very helpful for others.