awesome-repositories.com
Blog
awesome-repositories.com

Entdecke die besten Open-Source-Repositories mit KI-gestützter Suche.

EntdeckenKuratierte SuchenOpen-Source-AlternativenSelf-hosted SoftwareBlogSitemap
ProjektÜber unsRanking-MethodikPresseMCP-Server
RechtlichesDatenschutzAGB
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 Repos

Awesome GitHub RepositoriesDataset Metadata Modifiers

Utilities for filtering and modifying dataset class subsets and metadata.

Distinguishing note: Focuses on runtime modification of dataset class lists and filtering.

Explore 2 awesome GitHub repositories matching data & databases · Dataset Metadata Modifiers. Refine with filters or upvote what's useful.

Awesome Dataset Metadata Modifiers GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • open-mmlab/mmdetectionAvatar von open-mmlab

    open-mmlab/mmdetection

    32,756Auf GitHub ansehen↗

    This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular

    Allows modifying dataset metadata to train on specific subsets of classes.

    Pythoncascade-rcnnconvnextdetr
    Auf GitHub ansehen↗32,756
  • conardli/easy-datasetAvatar von ConardLi

    ConardLi/easy-dataset

    13,394Auf GitHub ansehen↗

    Easy-dataset is a comprehensive platform designed for the end-to-end management of machine learning datasets, specifically tailored for language and vision model fine-tuning. It functions as a centralized environment for the entire data lifecycle, encompassing the automated generation of synthetic training data, the structural organization of document collections, and the systematic annotation of individual data points. The platform distinguishes itself through its integrated evaluation and orchestration capabilities. It provides a dedicated suite for benchmarking models, featuring blind side

    Tracks the state of data entries from raw ingestion through annotation and quality scoring to final export for training.

    JavaScriptdatasetfine-tuningjavascript
    Auf GitHub ansehen↗13,394
  1. Home
  2. Data & Databases
  3. Dataset Metadata Modifiers

Unter-Tags erkunden

  • Lifecycle ManagementSystems for tracking data states from ingestion through annotation and quality scoring to final export. **Distinct from Dataset Metadata Modifiers:** Distinct from Dataset Metadata Modifiers: focuses on the end-to-end state tracking of data entries rather than just modifying metadata fields.