Grounding DINO vs YOLO World
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Models in this comparison
Grounding DINO vs YOLO World Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Grounding DINO | YOLO World |
|---|---|---|
| Organization | IDEA Research | Tencent AI Lab |
| Category | open | open |
| Modality | vision | multimodal |
| Release Date | Mar 2023 | Feb 2024 |
| Context Window | — | 13.0M |
| Parameters | 172M-341M | |
| License | Apache 2.0 | GPL v3 |
| Vision Tasks | ||
| Object Detection | Demo | |
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Model Features | ||
| Zero-shot Detection | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| Real-Time Vision | ||
Grounding DINO vs YOLO World: Overview
Grounding DINO is an open-vocabulary object detection model developed by IDEA Research, released in March 2023 under the Apache 2.0 license. It extends the DINO transformer-based detector with grounded pre-training, enabling it to detect arbitrary objects described by free-form text queries rather than a fixed set of predefined categories. The model integrates a text encoder with a visual backbone through a feature fusion module that aligns language and visual representations at multiple scales.
Grounding DINO achieves strong zero-shot detection performance on COCO, LVIS, and ODinW benchmarks, and supports referring expression comprehension tasks. It is widely used as a foundation for open-vocabulary detection pipelines and as the detection backbone in systems such as Grounded-SAM. The model is particularly suited for applications requiring flexible, text-driven object localization across diverse domains.
YOLO-World v2 Small (YOLO-World-S-v2) is the smallest variant of Tencent AI Lab’s YOLO-World v2 family, released around February 2024 under GPL-v3. With ~13 million parameters, it adopts a prompt-then-detect paradigm using offline vocabularies and is pretrained on large-scale datasets such as Objects365 and GoldG. The model processes image inputs at 640×640 or 1280×1280 resolutions and supports zero-shot open-vocabulary object detection, enabling recognition of novel categories from text prompts without retraining.
Evaluations show competitive results across benchmarks like LVIS and COCO, while maintaining real-time efficiency. On an NVIDIA V100, the small variant reaches ~74 FPS at standard resolutions. Together with larger YOLO-World v2 models, it provides a scalable framework for efficient, open-vocabulary detection across diverse deployment settings.