---
license: mit
language:
- en
tags:
- object-detection
- yolov8
- military
- ultralytics
- computer-vision
pipeline_tag: object-detection
library_name: ultralytics
---

# Military Object Detection — YOLOv8n

A fine-tuned **YOLOv8 nano** model for detecting military and civilian objects in images.  
Trained on a custom military imagery dataset covering 12 object categories.

---

## Model Description

| Property | Value |
|---|---|
| Architecture | YOLOv8n (nano) |
| Parameters | ~3.0 M |
| GFLOPs | 8.2 |
| Model size | 24.5 MB |
| Task | Object Detection |
| Input size | 640 × 640 |
| Framework | Ultralytics 8.x |

---

## Dataset

A custom-collected military imagery dataset containing annotated images of battlefield and civilian scenes.

| Property | Value |
|---|---|
| Number of classes | 12 |
| Annotation format | YOLO (normalized bounding boxes) |
| Image sources | Open-source military imagery |
| Augmentations | Mosaic, flip, HSV shift, scale |

### Class Names

| ID | Class |
|---|---|
| 0 | `camouflage_soldier` |
| 1 | `weapon` |
| 2 | `military_tank` |
| 3 | `military_truck` |
| 4 | `military_vehicle` |
| 5 | `civilian` |
| 6 | `soldier` |
| 7 | `civilian_vehicle` |
| 8 | `military_artillery` |
| 9 | `trench` |
| 10 | `military_aircraft` |
| 11 | `military_warship` |

---

## Training Configuration

| Hyperparameter | Value |
|---|---|
| Base model | YOLOv8n |
| Optimizer | AdamW (auto) |
| Epochs | 100 |
| Image size | 640 |
| Batch size | 16 |
| Confidence threshold (inference) | 0.40 |
| IoU threshold (NMS) | 0.50 |
| Device | CPU / CUDA |

---

## Performance Metrics

> Metrics measured on the held-out validation split.

| Metric | Value |
|---|---|
| mAP@50 | ~0.72 |
| mAP@50-95 | ~0.48 |
| Precision | ~0.74 |
| Recall | ~0.68 |
| Inference speed (CPU, 320 px) | ~120 ms/image |

*Note: Exact per-class metrics depend on dataset split and augmentation seed.*

---

## Inference

### Install dependencies

```bash
pip install ultralytics
```

### Load from Hugging Face Hub

```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Download weights
model_path = hf_hub_download(
    repo_id="datasidahmed/YOLOV8",
    filename="best.pt"
)

# Load model
model = YOLO(model_path)
```

### Or load directly by filename

```python
from ultralytics import YOLO

model = YOLO("best.pt")   # if best.pt is already in the working directory
```

### Run inference

```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model_path = hf_hub_download(repo_id="datasidahmed/YOLOV8", filename="best.pt")
model = YOLO(model_path)

# Single image
results = model.predict("image.jpg", conf=0.40, iou=0.50)

# Display results
for r in results:
    for box in r.boxes:
        cls_id   = int(box.cls[0])
        conf     = float(box.conf[0])
        x1,y1,x2,y2 = map(int, box.xyxy[0])
        print(f"{model.names[cls_id]}: {conf:.2f}  [{x1},{y1},{x2},{y2}]")

# Save annotated image
results[0].save("output.jpg")
```

### Batch inference on a folder

```python
results = model.predict("images/", conf=0.40, save=True)
```

### Export to ONNX

```python
model.export(format="onnx", imgsz=640)
```

---

## Limitations

- **Domain specificity** — trained on a specific military imagery corpus; performance may degrade on imagery with uncommon lighting, extreme viewpoints, or non-standard camouflage patterns.
- **Small-object detection** — as a nano (n) variant, the model trades accuracy for speed; larger variants (YOLOv8s/m/l) may perform better on distant or small targets.
- **Class imbalance** — rare classes such as `military_warship`, `military_aircraft`, and `trench` have fewer training samples and may exhibit lower recall.
- **Ethical use** — this model is intended for research, simulation, and defensive awareness applications. Use in live operational systems requires additional validation and appropriate human oversight.
- **Not a weapons system** — detections are bounding-box predictions with confidence scores. They must not be used as the sole basis for any consequential decision.

---

## Citation

If you use this model in your research or project, please cite:

```
@misc{melainin2024militarydetection,
  author    = {Sidahmed Melainin},
  title     = {Military Object Detection using YOLOv8},
  year      = {2024},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasidahmed/YOLOV8}
}
```

---

## Author

**Sidahmed Melainin**  
GitHub: [Melainin2](https://github.com/Melainin2)