6 repositorios
High-performance runtimes that execute neural network models across CPUs, GPUs, and specialized accelerators.
Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Deep Learning. Refine with filters or upvote what's useful.
PaddleOCR is a comprehensive optical character recognition framework designed for detecting and transcribing text from images and documents into structured, machine-readable formats. It provides a modular computer vision pipeline that decouples image preprocessing, text detection, and character recognition into independent, configurable stages. This architecture supports automated document digitization and multilingual text recognition, capable of identifying text in over one hundred languages across diverse environments ranging from scanned documents to industrial scenes. The framework disti
Executes neural network models on high-performance runtimes across CPUs, GPUs, and specialized hardware accelerators.
Ultimate Vocal Remover is a desktop application designed for AI-driven audio source separation. It utilizes deep learning models to isolate vocals, drums, and other individual instruments from mixed audio files, providing a utility for professional production and creative editing workflows. The software distinguishes itself by leveraging GPU-accelerated tensor computation to perform complex signal processing tasks, significantly reducing the time required for high-fidelity audio extraction. It incorporates a modular plugin architecture that integrates external utilities to support a wide rang
Executes pre-trained neural networks to perform complex pattern recognition and source separation on audio data.
Candle is a minimalist machine learning framework and deep learning inference engine designed for the Rust programming language. It functions as a low-level tensor computation library, providing the necessary primitives for multi-dimensional array operations and mathematical transformations required to execute pre-trained neural network models. The framework distinguishes itself through a focus on memory efficiency and hardware utilization. It employs static-typed tensor operations to enforce shape validation and memory safety at compile time, while utilizing a lazy-loaded computational graph
Functions as a high-performance deep learning inference engine optimized for low memory usage and efficient hardware utilization.
MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse
Acts as a high-performance inference engine for executing neural network models on mobile and embedded devices.
TensorRT es un motor de inferencia de aprendizaje profundo y un kit de desarrollo de software diseñado para optimizar y desplegar redes neuronales para una ejecución de alto rendimiento en GPUs NVIDIA. Funciona como un framework de aceleración de GPU que reduce la latencia y aumenta el rendimiento de los modelos entrenados durante el despliegue en producción. El kit de herramientas importa modelos desde el formato Open Neural Network Exchange y los transforma en motores optimizados. Utiliza optimización de modelos basada en grafos, generación de kernels de fusión de capas y cuantización basada en precisión para convertir pesos de punto flotante a formatos de menor precisión. El framework proporciona capacidades para la serialización de motores específicos de hardware y admite la extensión de las capacidades de inferencia a través de complementos personalizados para capas de redes neuronales especializadas.
Serves as a high-performance runtime environment that executes neural networks using NVIDIA GPU acceleration.
This project is a Rust interface for the PyTorch C++ library, serving as a deep learning framework and tensor computing library. It functions as a C++ API wrapper that enables the manipulation of multi-dimensional arrays and the execution of neural network architectures across CPU and GPU hardware accelerators. The library provides a TorchScript inference engine to load and execute just-in-time compiled models. It also supports Rust and Python interoperability, allowing for the creation of Python extensions that share tensor data through a common interface. The system covers deep learning mo
Implements high-performance runtimes that execute neural network models across CPUs and GPUs.