| --- |
| license: agpl-3.0 |
| library_name: ultralytics |
| tags: |
| - object-detection |
| - yolo |
| - yolov8 |
| - aerial |
| - drone |
| - visdrone |
| - onnx |
| - nullxes |
| - cerber |
| pipeline_tag: object-detection |
| --- |
| |
| # NULLXES CERBER-CV (v1) |
|
|
| Civil aerial **scene detector** for the NULLXES CERBER perception stack. |
| YOLOv8s fine-tuned on VisDrone-DET with CERBER class remap. |
|
|
| ## Metrics (VisDrone val, 548 images) |
|
|
| | Class | P | R | mAP50 | mAP50-95 | |
| |---------|-------|-------|-------|----------| |
| | all | 0.818 | 0.692 | 0.760 | 0.439 | |
| | human | 0.779 | 0.604 | 0.672 | 0.313 | |
| | vehicle | 0.857 | 0.779 | 0.848 | 0.565 | |
|
|
| Train: imgsz 1280, batch 32, 100 epochs, ~2.0 h on RTX PRO 6000. |
| Export ONNX: imgsz 640, opset 17. |
| `detector_alpha.onnx` sha256: `40151159e7bf59fcfc24b591124ff7aeec88ff4365619ee701fc186efdce199a` |
|
|
| ## Classes (head nc=13) |
|
|
| Trained with labels: **human (0), vehicle (1)**. |
| Other CERBER ids (uav, landing_zone, … cargo) are in the head but **untrained** in v1. |
| |
| ## Files |
| |
| - `best.pt` — Ultralytics weights |
| - `best.onnx` / `detector_alpha.onnx` — flight ONNX (`yolo_v8_raw`) |
| - `results.csv` / `results.png` — train curves |
|
|
| ## Cite |
|
|
| VisDrone: https://github.com/VisDrone/VisDrone-Dataset |
| Ultralytics YOLO (AGPL-3.0) |
|
|
