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arkito/VeritaScan

sourceHugging Faceagpl-3.0updated 6mo agoView on Hugging Face
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Model Card

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.

MetricValue
mAP@.5-.9588.7%
Precision96.2%
Recall98.6%

Performance Visualization

The following graphs illustrate the training progress and model evaluation:

Training Curves

[image]

🎯 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)
[image][image]
AirCraft TrackingMilitary Vehicle Tracking

📦 Multi-Platform Deployment & Downloads

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

FrameworkTarget HardwareOptimizationOne-Click Download
<img src="https://files.svgcdn.io/simple-icons/onnx.png" width="40">Universal (PC, Cloud, Edge)Standard FP32/FP16**⬇️ Download ONNX**
<img src="https://cdn.iconscout.com/icon/free/png-256/free-nvidia-logo-icon-svg-download-png-2945060.png" width="45">NVIDIA Jetson / RTXCUDA Accelerated**⬇️ Download TensorRT (.engine)**
<img src="https://1000logos.net/wp-content/uploads/2021/05/Intel-logo.png" width="80">Intel CPU / iGPUOpenVINO Runtime**⬇️ Download OpenVINO**
<img src="https://storage.googleapis.com/gweb-developer-goog-blog-assets/imagesarchive/originalimages/image1_v7xhr8h.png" width="80">Mobile / AndroidMobile Quantized**⬇️ Download TFLite**
<img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjuUv6hVLci1L1ZfvwIXDkZGetljXlYeUaVyDRyd5SxDf7HtGKrrpQY1Y4vrBWAK9lq9Ezmzw4Vk7v-dNeKyFCg6H1Lqg43pGbNFHG5spM07j0ThWSPWGzkMdPg0PKPsFO3-DHfKpR7I/s1600/tensorflowjs.png" width="80">Web BrowsersTensorFlow.js**⬇️ Download TF.js**
<img src="https://www.tensorflow.org/images/tflogohorizontal.png" width="80">Legacy SystemsFrozen Graph (.pb)**⬇️ Download TF-PB**
<img src="https://pytorch.org/assets/images/pytorch-logo.png" width="40">C++ / ProductionTorchScript JIT**⬇️ Download TorchScript**
<img src="https://cdn-icons-png.flaticon.com/512/0/747.png" width="35">iOS / macOSApple Neural Engine**⬇️ Download CoreML**
<img src="https://github.com/alibaba/MNN/raw/master/doc/banner.png" width="60">Embedded DevicesMNN Optimized**⬇️ Download MNN**
<img src="https://raw.githubusercontent.com/Tencent/ncnn/master/images/256-ncnn.png" width="40">Embedded DevicesNCNN Optimized**⬇️ Download NCNN**

🛠 Usage

python
from ultralytics import YOLO

# Load VeritaMilitary
model = YOLO('veritascan.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 please cite

@software{XythicK_VeritaScan_2026,
  author = {M Mashhudur Rahim (XythicK)},
  title = {VeritaScan: Lightweight Nano Object Detection for Aerial Military Surveillance},
  year = {2026},
  publisher = {Arkito Lab},
  url = {https://huggingface.co/arkito/VeritaScan},
}