@ -141,13 +141,7 @@ The COCO-Seg dataset includes several key features:
The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
| Model | size< br > < sup > (pixels) | mAP< sup > box< br > 50-95 | mAP< sup > mask< br > 50-95 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-seg ](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt ) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLO11s-seg ](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt ) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLO11m-seg ](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt ) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLO11l-seg ](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt ) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLO11x-seg ](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt ) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
{% include "macros/yolo-seg-perf.md" %}
### How is the COCO-Seg dataset structured and what subsets does it contain?