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Apprentissage automatique · GitHub

Explorez les outils pour entraîner, évaluer et exécuter des modèles, et comparez leur popularité.

Projets GitHub utiles

4,848 projets

AtlasNet

ThibaultGROUEIX

724étoiles
AtlasNet preview

This repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image.

Apprentissage automatiquePythonMITGitHub

model_analyzer

triton-inference-server

531étoiles

Triton Model Analyzer is a CLI tool to help with better understanding of the compute and memory requirements of the Triton Inference Server models.

  • Detailed and summary reports: Model Analyzer is able to generate summarized and detailed reports that can help you better understand the trade-offs between different model configurations that can be used for your model.
  • QoS Constraints: Constraints can help you filter out the Model Analyzer results based on your QoS requirements. For example, you can specify a latency budget to filter out model configurations that do not satisfy the specified latency threshold.
Apprentissage automatiquePythonApache-2.0GitHub

Kinetic.jl

vavrines

150étoiles

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Apprentissage automatiqueJuliaMITGitHub

VisionPilot

visionpilot-project

43étoiles
VisionPilot preview

Open-source modular autonomous driving simulation platform with computer vision, deep learning, and sensor fusion. Features lane detection, object recognition, various safety features, and adaptive control in BeamNG.tech, with real-time visualization via Foxglove.

Apprentissage automatiquePythonMITGitHub

RadQy

viswanath-lab

126étoiles
RadQy preview

RadQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) and computed tomography (CT) data.

  • MRI and CT quality assessment
  • DICOM metadata/tag extraction
Apprentissage automatiqueJavaScriptGitHub

SMACT

WMD-group

148étoiles
SMACT preview

Python package to aid materials design and informatics

  • At the core of SMACT are Element and Species (element in a given oxidation state) classes that have various properties associated with them.
  • Oxidation states that are accessible to each element are included in their properties.
Apprentissage automatiquePythonGitHub