Object Detection is a computer vision task that involves identifying and localizing objects within an image. The goal is to detect instances or objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Object detection models typically output a set of bounding boxes with corresponding predicted class names.
Microsoft COCO is the gold standard for large-scale object detection, segmentation, and captioning, with 330k+ images, 1.5M+ object instances, and 80 categories. Primary metric is box mAP averaged over 10 IoU thresholds (0.5:0.95).
Leading models on COCO.
| # | Model | box-map | Year | Source |
|---|---|---|---|---|
| ★ | ScyllaNet | 66.1 | 2026 | paper ↗ |
| 2 | DINOv3 + Plain-DETR + TTA | 66.1 | 2025 | paper ↗ |
| 3 | Co-DETR (Swin-L) | 66.0 | 2022 | paper ↗ |
| 4 | Co-DETR (Swin-L) | 66.0 | 2026 | paper ↗ |
| 5 | SenseTime Basemodel | 66.0 | 2026 | paper ↗ |
| 6 | CW_Detection | 66.0 | 2026 | paper ↗ |
| 7 | Co-DETR (Swin-L) | 66.0 | 2025 | paper ↗ |
| 8 | Thinker | 66.0 | 2026 | paper ↗ |
| 9 | DINOv3 + Plain-DETR | 65.6 | 2025 | paper ↗ |
| 10 | InternImage-H (OneFormer) | 65.5 | 2026 | paper ↗ |
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11 datasets tracked for this task.
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