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datasidahmed/
YOLOV8

Name: YOLOV8
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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

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

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

Export to ONNX

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