Lite.ai.toolkit

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices.

The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed.

The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender and age estimation, and head pose analysis. It also provides tools for facial recognition through the extraction of feature embeddings and the computation of cosine similarity to verify identities.

Additional capabilities include image matting for foreground isolation, grayscale image colorization, and artistic style transfer.

Features

Edge AI Model Deployment - Provides an optimized C++ implementation for deploying pre-trained computer vision models on edge devices.
Edge AI Model Deployment - Optimizes and deploys pre-trained computer vision models to run efficiently on resource-constrained edge devices.
Computer Vision Inference - Provides a C++ toolkit for performing real-time object detection, image classification, and style transfer across various hardware backends.
Face Detection - Identifies facial bounding boxes within image frames using multiple inference backends.
Facial Analysis Tools - Includes tools for detecting facial landmarks, estimating head pose, and analyzing facial attributes.
Image Classification Models - Implements image classification using pre-trained models to assign descriptive labels and confidence scores.
Object Detection - Identifies and locates multiple objects within images or video streams via bounding boxes.
GPU-Accelerated Vision Pipelines - Features a GPU-accelerated pipeline leveraging NVIDIA hardware to increase processing speed for complex vision tasks.
Face Detection - Locates human faces within images using high-speed specialized detection models.
Identity Matching - Extracts facial embeddings from images to match detected faces against known identities.
Face Embeddings - Generates numerical vector embeddings from facial features for identity matching.
Face Recognition - Implements automated identification and verification of individuals using facial biometrics.
GPU-Accelerated Inference - Provides a GPU-accelerated pipeline for NVIDIA hardware to reduce latency and increase processing speed.
Multi-Backend Inference Support - Provides a multi-backend inference engine that executes AI models across various CPU and GPU hardware targets.
Inference Model Deployment - Executes AI models across multiple hardware targets by optimizing compute graphs via various inference engines.
Inference Performance Optimization - Increases model execution performance on NVIDIA hardware by integrating high-performance acceleration engines.
ONNX Model Runtimes - Utilizes an ONNX model runtime to ensure cross-framework compatibility and efficient execution across diverse hardware.
Cosine Similarity Metrics - Computes cosine similarity between high-dimensional feature embeddings to verify visual identities.
Facial Embeddings - Extracts high-dimensional facial embeddings to enable identity verification and comparison.
Image Classifiers - Categorizes images into predefined labels using various optimized inference backends.
Multi-Backend Inference Executions - Executes AI models across different hardware targets by switching between various inference engines.
Facial Similarity Matching - Compares facial feature vectors to determine identity matches via cosine similarity.
Optimized Edge Implementations - Provides high-performance C++ implementations designed to run computer vision models on resource-constrained edge devices.
Biometric Face Verification - Verifies identities by matching a face against known embeddings using cosine similarity.
Head Pose Estimation - Calculates 3D head orientation using yaw, pitch, and roll Euler angles.
Head Region Detection - Identifies head regions in images to generate a segmentation mask for subject isolation.
Facial Region Segmentations - Segments facial features into specific regions using optimized inference backends.
Facial Region Segmentations - Isolates face and hair regions using AI runtimes optimized for edge deployment.
Face Pose Estimators - Estimates the 3D orientation of a face using Euler angles and pre-trained models.
Image Segmentation - Provides techniques for partitioning images into distinct regions to isolate objects from the background.
Hair Segmentation - Provides a specialized segmentation model to detect and isolate hair regions within images.
Facial Segmentations - Identifies and segments facial features from images using pre-trained models.
Attribute Classifiers - Estimates general facial attributes including age, gender, and emotion.
Face Embedding Extractors - Generates normalized numerical vector representations from facial images to enable similarity comparison and identity verification.
Facial Emotion Classifiers - Analyzes facial images to identify human emotional states using optimized models across multiple backends.
Facial Landmark Detection - Identifies key facial feature points in images using optimized C++ inference engines.
Facial Recognition - Generates facial feature embeddings and computes cosine similarity to verify and compare individual identities.
Gender Predictions - Identifies human gender through visual facial analysis using pre-trained neural networks.
Head Segmentation - Implements optimized models to identify and mask human head regions in images.
Style Transfers - Generates stylized versions of images based on pre-trained fast style transfer models.
CPU Inference Runtimes - Enables the execution of AI models on central processing units using standard backend engines.
Pre-trained Weight Loading - Implements mechanisms for importing serialized pre-trained weights into models for immediate inference.
Object Detection Models - Runs image classification models to detect objects and retrieve associated labels and confidence scores.
Automated Image Labeling - Retrieves class labels and confidence scores from images using pre-trained models across multiple inference backends.
Image Segmentations - Processes visual data through neural networks to produce alpha mattes and segmentation maps for isolating foreground objects.
Portrait Segmentation - Isolates a person's portrait from a background image by applying a specialized segmentation model.
Pre-trained Model Implementations - Integrates pre-trained neural network weights for object detection, face recognition, and segmentation to provide immediate AI capabilities.
Style Transfer - Transforms the visual style of images into artistic or cartoon representations using neural networks.
Temporal Video Matting - Removes backgrounds from video streams while maintaining temporal consistency for foreground subjects.
Portrait Masking - Identifies and isolates human portraits using specialized portrait masking for background removal.
Alpha Matting - Creates alpha matte masks to separate human subjects from their backgrounds.
General Image Colorization - Transforms grayscale images into full-color RGB images using neural colorization models.
AI & Machine Learning - Toolkit for 100+ AI models

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xlite-devlite.ai.toolkit

Features