VeritaScan 🛡️

VeritaScan is a high-performance, lightweight object detection model specifically optimized for Aerial Military Surveillance. It is designed to be deployed on tactical drones with limited onboard computing power.

Developed by M Mashhudur Rahim (XythicK) at Arkito Lab (A Non-Profit Research Organization).

🚀 Model Overview

• Model Size: 5.4 MB
• Target Domain: Aerial/Drone View
• Inference Speed: ~2.7ms per image

📊 Training Results

The model was trained to achieve a solid balance between speed and accuracy for edge devices.

Metric	Value
mAP@.5-.95	88.7%
Precision	96.2%
Recall	98.6%

Performance Visualization

The following graphs illustrate the training progress and model evaluation:

Training Curves

🎯 Domain Specialization (Aerial vs Ground)

VeritaMilitary is highly specialized for top-down perspectives. While it may show sensitivity to perspective shifts in ground-level imagery, it demonstrates exceptional precision in aerial views with confidence scores up to 90%.

Aerial Detection (Success)
AirCraft Tracking

📦 Multi-Platform Deployment & Downloads

VeritaMilitary supports a wide range of hardware. Click the buttons below to download the optimized formats:

Framework	Target Hardware	Optimization	One-Click Download
	Universal (PC, Cloud, Edge)	Standard FP32/FP16	⬇️ Download ONNX
	NVIDIA Jetson / RTX	CUDA Accelerated	⬇️ Download TensorRT (.engine)
	Intel CPU / iGPU	OpenVINO Runtime	⬇️ Download OpenVINO
	Mobile / Android	Mobile Quantized	⬇️ Download TFLite
	Web Browsers	TensorFlow.js	⬇️ Download TF.js
	Legacy Systems	Frozen Graph (.pb)	⬇️ Download TF-PB
	C++ / Production	TorchScript JIT	⬇️ Download TorchScript
	iOS / macOS	Apple Neural Engine	⬇️ Download CoreML
	Embedded Devices	MNN Optimized	⬇️ Download MNN
	Embedded Devices	NCNN Optimized	⬇️ Download NCNN

🛠 Usage

from ultralytics import YOLO

# Load VeritaMilitary
model = YOLO('best.pt')

# Run Inference
results = model.predict(source='drone_footage.mp4', imgsz=640, conf=0.25)
results[0].show()

🏢 About Arkito Lab

Arkito Lab is a non-profit organization dedicated to open-source research and the development of AI solutions for humanitarian and defense technology.

Maintained by: M Mashhudur Rahim (XythicK)

Cite This Project

If you use this model in your research or in your use please cite

  author = {M Mashhudur Rahim (XythicK)},
  title = {VeritaScan: Lightweight YOLOv11-Nano Object Detection for Aerial Military Surveillance},
  year = {2026},
  publisher = {Arkito Lab},
  url = {https://huggingface.co/XythicK/VeritaScan},
}```