Research Paper
Auto-Labeling Data for Object Detection
How far can zero-shot auto labeling take us in the quest for labeled datasets and performant models? To find out, we rigorously benchmarked Verified Auto Labeling across popular computer vision datasets.
Get key insights and practical best practices from our latest research, including:
- Real-world performance: How auto-labeling can achieve up to 95% of human accuracy—at 100,000× lower cost.
- Foundation model comparisons: See how YOLO-World, YOLOE, and Grounding DINO differ in accuracy, speed, and scalability.
- Choosing confidence thresholds: Why a higher threshold isn’t always better
- Downstream model training: When auto-labels outperform human annotations — and why.
- Pitfalls to avoid: Where auto-labeling falls short, and how hybrid approaches with targeted human annotation can help.