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Apprentissage automatique · GitHub

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

Projets GitHub utiles

7,572 projets

yolo_research

positive666

667étoiles

based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment C

MLH-Quizzet

PragatiVerma18

97étoiles
MLH-Quizzet preview

This is a smart Quiz Generator that generates a dynamic quiz from any uploaded text/PDF document using NLP. This can be used for self-analysis, question paper generation, and evaluation, thus reducing human effort.

  • implements automatic question generation (AQG) techniques Automatic question generation (AQG) is concerned with the construction of algorithms for producing questions from knowledge sources, which can be either structured (e.g. knowledge bases (KBs) or unstructured (e.g. text))
  • helps in resource saving(time, money and human effort)

BD3-Dataset

Praveenkottari

40étoiles
BD3-Dataset preview

This repository provides the BD3 dataset, containing over 3,965 annotated RGB images. It supports evaluating computer vision techniques for automatic defect identification to improve building inspections. A comprehensive study and comparison results simulate the identification and classification of defects in various real-world built environments.

landmarker

predict-idlab

49étoiles

PyTorch-based toolkit for landmark localization

  • Modular: Landmarker is designed to be modular. Almost all components can be used independently.
  • Flexible: Landmarker provides a flexible framework for landmark detection, allowing you to easily customize your model, loss function, and data loaders.