Grounded SAM vs YOLO World
We don't benchmark Grounded SAM or YOLO World on Vision Evals. Grounded SAM is open-weight under Apache 2.0: free for commercial use. YOLO World is open-weight under GPL v3: commercial use needs a license (included on Roboflow cloud).
Compare Grounded SAM vs YOLO World live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Grounded SAM vs YOLO World Comparison Table
| Property | Grounded SAM | YOLO World |
|---|---|---|
| Organization | IDEA Research | Tencent AI Lab |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jan 2024 | Feb 2024 |
| Context Window | — | — |
| Parameters | Unknown | 13M |
| License | Apache 2.0 | GPL v3 |
| Commercial use | free for commercial use | commercial use needs a license (included on Roboflow cloud) |
| Vision Tasks | ||
| Object Detection | Supported | Demo |
| Phrase Grounding | Not listed | Supported |
| Vision Language | Supported | Not listed |
| Zero Shot Segmentation | Supported | Not listed |
Both support 1 more task | ||
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
Both support 2 more features | ||
Grounded SAM vs YOLO World: Overview
Grounded SAM is an open-vocabulary image segmentation model developed by IDEA Research, released in January 2024 under the Apache 2.0 license. It combines Grounding DINO, a zero-shot open-vocabulary object detector, with the Segment Anything Model to produce precise segmentation masks for objects identified through free-form text prompts. The two models are used sequentially: Grounding DINO localizes objects from a text query, and SAM generates the corresponding segmentation masks.
Grounded SAM enables zero-shot instance segmentation without task-specific training data, making it applicable to domains where labeled segmentation data is scarce. It supports arbitrary text queries and can segment objects not represented in standard training sets. The model is commonly used in automated labeling pipelines, robotic perception, and domain-specific vision applications requiring open-vocabulary segmentation.
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.
