awesome-repositories.com
Blog
awesome-repositories.com

Descubre los mejores repositorios open-source con nuestra búsqueda potenciada por IA.

ExplorarBúsquedas curadasAlternativas open-sourceSoftware autohospedableBlogMapa del sitio
ProyectoAcerca deCómo clasificamosPrensaServidor MCP
Aviso legalPrivacidadTérminos
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 repositorios

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

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • open-mmlab/mmdetectionAvatar de open-mmlab

    open-mmlab/mmdetection

    32,756Ver en GitHub↗

    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
    Ver en GitHub↗32,756
  • conardli/easy-datasetAvatar de ConardLi

    ConardLi/easy-dataset

    13,394Ver en GitHub↗

    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
    Ver en GitHub↗13,394
  1. Home
  2. Data & Databases
  3. Dataset Metadata Modifiers

Explorar subetiquetas

  • 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.