Awesome GitHub RepositoriesDeep Learning Execution

This project is a TensorFlow and Keras implementation of the Mask R-CNN architecture. It provides a framework for performing simultaneous object detection and instance segmentation, transforming raw images into segmented masks and bounding boxes for individual object identification. The toolset enables custom computer vision training through fine-tuning pre-trained weights and integrating user-provided datasets. It includes capabilities for distributed GPU training to accelerate the optimization of large vision models. The framework covers model evaluation using standard precision metrics an

Magenta is an AI creative suite and TensorFlow generative art framework used to train and deploy models for the production of artistic media. It functions as a generative music library and a deep learning art generator, providing tools to automate the creation of original musical compositions and visual artwork. The project covers AI music composition and generative visual art through neural art generation and machine learning creativity. It enables the training of generative models to produce original songs, images, and drawings based on learned patterns.

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

PlaidML ist ein Deep-Learning-Compiler-Framework und eine plattformübergreifende Runtime, die darauf ausgelegt ist, Machine-Learning-Modelle auf einer Vielzahl von Hardware-Zielen auszuführen. Es fungiert als hardwareunabhängige Tensor-Engine, die Tensor-Modelle in ausführbaren Code übersetzt, wodurch Deep-Learning-Netzwerke auf verschiedenen Rechengeräten ausgeführt werden können, ohne spezifische Treiberabhängigkeiten zu benötigen. Das System ermöglicht die Ausführung von Modellen auf benutzerdefinierter oder eingeschränkter Hardware durch die Verwendung von JSON-Spezifikationen zur Definition der Gerätehardware. Es verwendet eine domänenspezifische Sprache zur Beschreibung von Tensor-Berechnungen und bietet eine mittlere Schicht, um verschiedene Machine-Learning-Frameworks mit seinem hardwareorientierten Compiler zu integrieren. Die Engine unterstützt eine Reihe von Tensor-Operationen, einschließlich Tensor-Kontraktionen mit Index-Constraints, mehrdimensionalen Faltungen mit konfigurierbaren Strides und Padding sowie elementweisen Operationen unter Verwendung von Broadcasting. Zudem enthält sie eine Test-Suite, um Ausführungsgeschwindigkeit und Effizienz über verschiedene Hardwarekomponenten und Umgebungen hinweg zu benchmarken.

TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering materials from 2D images. It utilizes a sparse voxel representation to handle complex topologies and internal structures without relying on iso-surface fields. The project features a structured latent space representation that maps geometry and texture attributes to maintain visual fidelity. It employs an optimization-free geometry reconstruction process to decode latent representations directly into voxel grids and includes a PBR texture generator for synthesizing base color, r

This project provides a TensorFlow implementation of the Stable Diffusion model, serving as a generative engine for creating and modifying visual content. It functions as a machine learning architecture that translates natural language descriptions into high-quality images by iteratively refining noise within a compressed latent space. The system enables a variety of generative tasks, including text-to-image synthesis, image inpainting to fill missing or masked regions, and image editing to transform existing visuals based on text prompts. Beyond static imagery, the framework supports the gen