YOLOV8

Dataset

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

Class Names

Training Configuration

Performance Metrics

Metrics measured on the held-out validation split.

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

Inference

Install dependencies

pip install ultralytics

Load from Hugging Face Hub

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

from ultralytics import YOLO

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

Run inference

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