Abstract: Remote sensing object detection faces challenges such as small object sizes, complex backgrounds, and computational constraints. To overcome these challenges, we propose XSNet, an efficient ...

amlmodelmonitoring/ ├── .env # Environment variables (create from template) ├── set_env.ps1 # Loads .env variables into PowerShell session ├── requirements.txt # Python dependencies │ ├── ...

With the rapid development of marine resource exploitation and the increasing demand for underwater robot inspection, achieving reliable target perception in turbid, low-illumination, and spectrally ...

AWS Lambda provides a simple, scalable, and cost-effective solution for deploying AI models that eliminates the need for expensive licensing and tools. In the rapidly evolving landscape of artificial ...

The XGBoost-based approach demonstrated robust external validation across multiple centers, supporting clinical adoption to guide personalized treatment decisions. A machine learning (ML) model using ...

Traffic monitoring plays a vital role in smart city infrastructure, road safety, and urban planning. Traditional detection systems, including earlier deep learning models, often struggle with ...

A simple Flask application that can serve predictions machine learning model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict ...

Abstract: Object detection is essential in applications such as healthcare (e.g. organ detection in CT and ultrasound scans), autonomous driving, and surveillance. However, smaller models like YOLOv8 ...