Instructions to use zjunlp/Ocean-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use zjunlp/Ocean-router with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("zjunlp/Ocean-router") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,187 Bytes
510250c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ---
language: en
license: apache-2.0
library_name: ultralytics
tags:
- yolov11
- yolov5
- image-classification
- routing
- marine-images
- oceangpt-x
---
# Ocean-router: Marine Image Routing Classifiers
Two-stage lightweight classifiers used to dynamically route marine images to specialized detectors based on image modality and content.
## Model Files & Tasks
| File | Task | Architecture | Input/Output |
|------|------|--------------|--------------|
| `cls_bio_sonar/best.pt` | Sonar vs. Biological routing | YOLOv11-cls | Image → `[sonar_prob, bio_prob]` |
| `fish_coral_cls/best.pt` | Fish vs. Coral routing | YOLOv5 | Image → `[fish_prob, coral_prob]` |
## Usage
### 1. Sonar/Biological Router
```python
from ultralytics import YOLO
router = YOLO("cls_bio_sonar/best.pt")
results = router.predict("input.jpg")
```
### 2. Fish/Coral Classifier (YOLOv5)
Requires the official YOLOv5 repo
```python
import torch
model = torch.hub.load("ultralytics/yolov5", "custom", path="fish_coral_cls/best.pt", force_reload=True)
results = model("input.jpg")
```
cls_bio_sonar decides if input is sonar or biological.
If biological, fish_coral_cls routes to the appropriate species detector. |