Secure face recognition HMI system for personalized user access in industrial environments.

Real-time AI-powered vision system for detecting defects in manufacturing environments

Lightweight real-time people counting model based on quantized YOLOX, optimized for edge devices using LiteRT (TensorFlow Lite) .

Lightweight and quantized PoseNet model for real-time multi-person pose estimation on edge hardware.

Lightweight semantic segmentation model optimized for real-time street scene analysis on edge devices.

Efficient single-camera depth estimation using quantized MiDaS v2 model optimized for edge AI deployment.

Neural network model for forecasting household electricity consumption using historical time series data.

Real-time environmental sound classifier optimized for low-power edge devices using MobileNet v1.

Lightweight anomaly detector for time series data using Isolation Forest, designed for edge environments.

Efficient CNN model for image classification on mobile and embedded edge devices.

Fast object detection using YOLOX optimized for embedded edge devices.

High-accuracy speech transcription optimized for automated documentation and voice-controlled operations in demanding environments.

Energy-efficient, real-time object detection optimized for low-power edge devices with strict battery and thermal constraints.

AI vision application to detect human falls in real time for safety, elderly care, and industrial risk monitoring.

Deep learning solution that classifies human actions in video streams for workflow analysis and operational safety.

Guided training environment for creating and preparing custom Face Recognition models, with optimized deployment and inference on SECO hardware.