README.md

---
license: apache-2.0          # must match Settings → Licensing
pipeline_tag: object-detection
library_name: ultralytics
model_type: yolov9
datasets:
  - benediktkol/DDOS
metrics:
  - accuracy
---

YOLO298B is a custom‑trained Ultralytics YOLO model (`best.pt`) built by **Team 6 (SJSU)**.  
It detects aerial classes in aerial imagery collected by autonomous drones.

| Attribute        | Value          |
|------------------|----------------|
| **Architecture** | YOLO‑v9‑S      |
| **Input size**   | 640 × 640 px   |
| **Classes**      | *n*            |
| **Checkpoint**   | 5.5 MB         |

## Intended uses & limitations

| Use‑case                             | ✅ Recommended | 🚫 Not recommended |
|--------------------------------------|---------------|--------------------|
| Real‑time obstacle detection on UAVs | ✔️            |                    |
| Academic research / benchmarking     | ✔️            |                    |
| Safety‑critical deployment w/o human |               | ❌                 |

## Training data  
Dataset: **benediktkol/DDOS** – contains drone‑view images with obstacles (*\<brief description\>*).  
Split: 80 % train · 10 % val · 10 % test

## Quick start

```python
from ultralytics import YOLO

model = YOLO("vsham001/Yolo298B")
results = model("https://ultralytics.com/images/bus.jpg")
results[0].show()