diff --git a/docs/source/main/model_zoo/MODEL_ZOO.md b/docs/source/main/model_zoo/MODEL_ZOO.md
index 74c7df01..2ae6521f 100644
--- a/docs/source/main/model_zoo/MODEL_ZOO.md
+++ b/docs/source/main/model_zoo/MODEL_ZOO.md
@@ -41,6 +41,7 @@ The [Model ZOO](https://chmura.put.poznan.pl/s/2pJk4izRurzQwu3) is a collection
 | [Aerial Cars Detection](https://chmura.put.poznan.pl/s/vgOeUN4H4tGsrGm)                 | 640        | 10    | YOLOv7-m model for cars detection on aerial images. Based on the [ITCVD](https://arxiv.org/pdf/1801.07339.pdf).                                                                               | [Image](https://chmura.put.poznan.pl/s/cPzw1mkXlprSUIJ) |
 | [UAVVaste Instance Segmentation](https://chmura.put.poznan.pl/s/v99rDlSPbyNpOCH)        | 640        | 0.5   | YOLOv8-L Instance Segmentation model for litter detection on high-quality UAV images. Based on the [UAVVaste dataset](https://github.com/PUTvision/UAVVaste).                                 | [Image](https://chmura.put.poznan.pl/s/KFQTlS2qtVnaG0q) |
 | [Tree-Tops Detection](https://chmura.put.poznan.pl/s/A9zdp4mKAATEAGu)                    | 640        | 10    | YOLOv9 model for treetops detection on aerial images. Model is trained on the mix of publicly available datasets.                                                                            | [Image](https://chmura.put.poznan.pl/s/F0lkIX9xhcwD4PG) |
+| [Ship Detection](https://github.com/ArturWoz/ship-detection/releases/download/release/ship-detection-model.onnx)| 640        | 30    | YOLOv8s model for ship detection on aerial images. Trained on [mix of publicly available datasets](https://github.com/ArturWoz/ship-detection/blob/main/README.md).   | [Image](https://github.com/ArturWoz/ship-detection/blob/main/example.png) |
 
 ## Super Resolution Models
 | Model                                                                                 | Input size | CM/PX                                                  | Scale Factor | Description                                                                                                                                                                                                                                  | Example image                                                                                                                                                                      |