Edge AI Libraries#
A collection of libraries, microservices, and tools for Edge application development. This project also includes sample applications to showcase some generic AI use cases.
Tools#
Make applications based on sensor data faster, easier, and better.
Visual Pipeline and Platform Evaluation Tool
Computer vision AI models in a fraction of the time and with minimal data.
A robust platform for experimenting with visual prompting techniques.
Libraries#
Open-source anomaly detection library for training and evaluating visual inspection models.
Dataset management framework for preparing, converting, and validating vision datasets.
Media analytics framework for building video AI pipelines with GStreamer.
Motion-control library implementing PLCopen function blocks for industrial automation.
SDK for integrating Geti model training and deployment workflows into applications.
EtherCAT communication stack for deterministic industrial fieldbus integration.
Motion-control task framework for robotics applications and orchestration.
Utilities for segmenting and preprocessing video for downstream AI workflows.
Microservices#
Audio analysis microservice for extracting insights from sound inputs.
Containerized service for building and serving video analytics pipelines with DL Streamer.
Document ingestion service using pgvector for embedding storage and retrieval.
Service for collecting and exposing application and system metrics.
Service for fetching, packaging, and preparing models for deployment.
Service for generating and serving embeddings across text and visual inputs.
Service for generating speech audio from text input.
Analytics service for ingesting and analyzing time-series data.
Retrieval service backed by Milvus for semantic search workflows.
Service for preparing visual data for retrieval and search workflows.
Serving stack for OpenVINO-based vision-language models.
Service for hierarchical analysis and understanding of video content.
Computes camera parameters automatically.
For object clustering, cluster tracking, and analysis of cluster’s shape and movement patterns.
Combines and contextualizes multiple object detection inputs for tracking objects over time.
Generates meshes and camera parameters from camera-captured frames.
Sample Applications#
Sample app for conversational question answering over enterprise content.
Core sample for building retrieval-augmented chat workflows.
Sample application for summarizing documents with LLM-powered workflows.
Sample app for searching video content and generating summaries.
Model Deployment#
Toolkit for optimizing and deploying AI models on Intel hardware.
Model server for hosting and serving AI models over network APIs.