xlite-dev/lite.ai.toolkit

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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