Instructions to use MagistrTheOne/CERBER-CV-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use MagistrTheOne/CERBER-CV-v2 with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("MagistrTheOne/CERBER-CV-v2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
metadata
license: agpl-3.0
library_name: ultralytics
tags:
- object-detection
- yolov8
- aerial
- drone
- uav
- visdrone
- seraphim
- nullxes
- cerber
pipeline_tag: object-detection
NULLXES CERBER-CV v2 (UAV fine-tune)
Fine-tune of CERBER-CV with Seraphim drone (uav) + VisDrone keep.
Metrics (val 1800 imgs)
| Class | P | R | mAP50 | mAP50-95 |
|---|---|---|---|---|
| all | 0.816 | 0.655 | 0.709 | 0.419 |
| human | 0.717 | 0.409 | 0.465 | 0.188 |
| vehicle | 0.811 | 0.669 | 0.735 | 0.458 |
| uav | 0.922 | 0.888 | 0.926 | 0.612 |
Train: 40 ep @640 from v1 best · ~0.43 h RTX PRO 6000.
Note: human/vehicle below v1 (prefer v1 for scene-only; v2 when UAV needed).