YOLO or Grounding DINO: which detector for video analytics
YOLO from Ultralytics is the most widely used real-time detector: 2.4M to 68M parameters, running on modest hardware and counting people, vehicles and goods straight from a video feed. Grounding DINO finds objects from a text description and needs no labeling or fine-tuning for each new class. Both have conditional commercial licensing, and the conditions differ: YOLO is AGPL-3.0, meaning you would have to open your product's code, or you buy an Ultralytics enterprise license; Grounding DINO's base version is Apache 2.0, while Rex-Omni uses IDEA's own license based on the Qwen Research License. YOLO was updated through 2026-01 (YOLO26), Grounding DINO through 2025-10.
Comparison based on catalog data
| Parameter | YOLO (Ultralytics) | Grounding DINO / Rex-Omni |
|---|---|---|
| Category | Computer vision | Computer vision |
| Developer | Ultralytics, USA | IDEA Research, China |
| Releases | Jan 2023 – Jan 2026 | Mar 2023 – Oct 2025 |
| Sizes | 2.4M – 68M | 172M – 3B |
| Hardware | Laptop | Laptop, 1 GPU |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| License | AGPL-3.0 (you would have to open your product's code) or a paid Ultralytics enterprise license | Grounding DINO: Apache 2.0; Rex-Omni: own IDEA License based on the Qwen Research License |
| Russian | Not applicable | Not applicable |
| Ollama | No | No |
| Without GPU | Yes | Yes |
| Tasks |
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Choose YOLO (Ultralytics) if
- You have a constant camera feed: counting people, vehicles and goods in real time
- Your hardware is modest: models start at 2.4M parameters
- You can either open your product's code under AGPL-3.0 or buy the Ultralytics license
Choose Grounding DINO / Rex-Omni if
- Your object classes change often and labeling a dataset for each makes no sense
- You need description-based search such as a person without a hard hat, with no training
- Your task is preparing labeled data to train another model
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