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

> Overview of image data curation with NeMo Curator including loading, processing, filtering, and export workflows

# About Image Curation

Learn how to curate high-quality image datasets using NeMo Curator's powerful image processing pipeline. NeMo Curator enables you to efficiently process large-scale image-text datasets, applying quality filtering, content filtering, and semantic deduplication at scale.

## Use Cases

* Prepare high-quality image datasets for training generative AI models such as LLMs, VLMs, and WFMs
* Curate datasets for text-to-image model training and fine-tuning
* Process large-scale image collections for multimodal foundation model pretraining
* Apply quality control and content filtering to remove inappropriate or low-quality images
* Generate embeddings and semantic features for image search and retrieval applications
* Remove duplicate images from large datasets using semantic deduplication

## Architecture

NeMo Curator's image curation follows a modular pipeline architecture where data flows through configurable stages. Each stage performs a specific operation and passes processed data to the next stage in the pipeline.

```mermaid
flowchart LR
    A[Tar Archive Input] --> B[File Partitioning]
    B --> C[Image Reader<br />DALI GPU-accelerated]
    C --> D[CLIP Embeddings<br />ViT-L/14]
    D --> E[Aesthetic Filtering<br />Quality scoring]
    E --> F[NSFW Filtering<br />Content filtering]
    F --> G[Duplicate Removal<br />Semantic deduplication]
    G --> H[Export & Sharding<br />Tar + Parquet output]
    
    classDef input fill:#e1f5fe,stroke:#0277bd,color:#000
    classDef processing fill:#f3e5f5,stroke:#7b1fa2,color:#000
    classDef output fill:#e8f5e8,stroke:#2e7d32,color:#000
    
    class A input
    class B,C,D,E,F,G processing
    class H output
```

This pipeline architecture provides:

* **Modularity**: Add, remove, or reorder stages based on your workflow needs
* **Scalability**: Distributed processing across multiple GPUs and nodes using Ray
* **Flexibility**: Configure parameters for each stage independently
* **Efficiency**: GPU-accelerated processing with DALI and CLIP models

## Introduction

Master the fundamentals of NeMo Curator's image curation pipeline and set up your processing environment.

#### [Concepts](/about/concepts/image)

Learn about ImageBatch, ImageObject, and pipeline stages for efficient image curation
data-structures
distributed
architecture

#### [Get Started](/get-started/image)

Learn prerequisites, setup instructions, and initial configuration for image curation
setup
configuration
quickstart

## Curation Tasks

### Load Data

Load and process large-scale image datasets from local storage using tar archives with GPU-accelerated DALI for efficient distributed processing.

#### [Tar Archives](/curate-images/load-data/tar-archives)

Load and process JPEG images from tar archives using DALI
tar-archives
dali
gpu-accelerated

### Process Data

Transform and enhance your image data through embeddings, classification, and filters.

#### [Embeddings](/curate-images/process-data/embeddings)

Generate image embeddings using CLIP models.
embeddings

#### [Filters](/curate-images/process-data/filters)

Apply built-in filters for aesthetic quality and NSFW content filtering.
Aesthetic NSFW quality filtering

#### [Deduplication](/curate-images/tutorials/dedup-workflow)

Remove duplicate images using semantic similarity and clustering.
deduplication semantic clustering

#### [Nemotron OCR Synthetic Data](/curate-text/synthetic/nemotron-ocr)

Generate word-level OCR labels and verifier-scored visual question-answering conversations.
ocr multimodal synthetic-data

### Pipeline Management

Optimize and manage your image curation pipelines with advanced execution backends and resource management.

#### [Execution Backends](/reference/infra/execution-backends)

Configure Ray-based executors for distributed processing and resource management.
ray distributed resource-management

#### [Performance Optimization](/curate-images/load-data/tar-archives)

Optimize performance with DALI GPU acceleration and efficient resource allocation.
dali gpu-acceleration performance

### Save & Export

Export your curated image datasets with metadata preservation, custom resharding options, and support for downstream training pipelines.

#### [Save & Export](/curate-images/save-export)

Save metadata to Parquet and export filtered datasets with custom resharding.
parquet tar-archives resharding

> Overview of image data curation with NeMo Curator including loading, processing, filtering, and export workflows