Florence-2 or Grounding DINO: which to choose for object detection
Florence-2 from Microsoft is a very small MIT-licensed model that produces captions, object detection, segmentation and text reading from a single prompt; it runs on a CPU and works well as a general labelling tool. Grounding DINO from IDEA Research is built for one thing: finding an object in an image from a text description such as a person without a hard hat, with no training on your own data. The licensing difference matters: Florence-2 is plain MIT, while Grounding DINO's commercial use comes with conditions — Grounding DINO itself is Apache 2.0, but the newer Rex-Omni generation uses IDEA's own license based on the Qwen Research License, which you need to read before production.
Comparison based on catalog data
| Parameter | Florence-2 | Grounding DINO / Rex-Omni |
|---|---|---|
| Category | Image + text, Computer vision | Computer vision |
| Developer | Microsoft, USA | IDEA Research, China |
| Releases | Jun 2024 – Jun 2024 | Mar 2023 – Oct 2025 |
| Sizes | 0.23B – 0.77B | 172M – 3B |
| Hardware | Laptop | Laptop, 1 GPU |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| License | MIT | Grounding DINO: Apache 2.0; Rex-Omni: own IDEA License based on the Qwen Research License |
| Russian | Not supported | Not applicable |
| Ollama | No | No |
| Without GPU | Yes | Yes |
| Tasks |
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Choose Florence-2 if
- You want one tool covering captions, segmentation and text reading at once
- You want plain MIT licensing with no commercial conditions
- You have to run on a CPU, starting from 0.23B
Choose Grounding DINO / Rex-Omni if
- You need to find objects from an arbitrary text description with no labeling
- Your task is automatic data labeling to train your own model
- You check photos against requirements: video analytics, retail, security
Other comparisons
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- gpt-oss or Qwen: which one to run on your own server
- Llama or DeepSeek: which to run inside your own perimeter
- Mistral or Qwen: which to choose for business
- Gemma or Phi: small models for modest hardware
- YandexGPT or Qwen: which to choose for Russian
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