> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/llms.txt.
> For full documentation content, see https://docs.nvidia.com/nemo/curator/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server.

> Essential concepts for image data curation including loading, processing, and export with GPU acceleration

# Image Curation Concepts

This document covers the essential concepts for image data curation in NVIDIA NeMo Curator. These concepts assume basic familiarity with data science and machine learning principles.

## Core Concept Areas

Image curation in NVIDIA NeMo Curator focuses on these key areas:

Core concepts for loading and managing image datasets

Concepts for embedding generation, classification, filtering, and deduplication

Concepts for saving, exporting, and resharding curated image datasets

## Infrastructure Components

The image curation concepts build on NVIDIA NeMo Curator's core infrastructure components, which are shared across all modalities (text, image, video). These components include:

Optimize memory usage when processing large datasets
partitioning
batching
monitoring

Leverage NVIDIA GPUs for faster data processing
cuda
dali
performance

Continue interrupted operations across large datasets
checkpoints
recovery
batching