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

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

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

Fast object detection using YOLOX optimized for embedded edge devices.

AI-powered image analysis tool for defect detection in industrial environments, with a guided journey for customizing new defect types.

Real-time activity recognition using motion sensor data from wearable fitness devices.

Deep learning model to detect presentation attacks in biometric systems, enhancing security against spoofing attempts.

A time-series forecasting model designed to predict sensor data patterns in smart buildings, enabling proactive energy management and maintenance.

High-precision OCR application that detects and transcribes text from real-world images with superior accuracy, ideal for industrial and logistics use cases.

Lightweight OCR application that detects and reads text in images using a faster, lower-resource model designed for embedded and edge devices.

Specialized AI-based vision system for detecting surface and structural defects on printed circuit boards (PCBs) during manufacturing inspection.

A training pipeline for customizing YAMNet to recognize new environmental or industrial sounds.