> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/v25.09/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.
# Curate Images
## Docs
- [Image Curation Tutorials](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/tutorials.md): Hands-on tutorials for image curation workflows using NeMo Curator tools and techniques
- [Create an Image Curation Pipeline](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/tutorials/beginner.md): Beginner-friendly tutorial for creating your first image curation pipeline with quality filtering and NSFW detection
- [Image Duplicate Removal Workflow](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/tutorials/dedup-workflow.md): End-to-end workflow tutorial covering the image curation process from embedding generation through semantic deduplication
- [Image Data Loading](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/load-data.md): Load image data for curation using tar archives with distributed processing and GPU acceleration
- [Loading Images from Tar Archives](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/load-data/tar-archives.md): Load and process JPEG images from tar archives using DALI-powered GPU acceleration with distributed processing
- [Process Data for Image Curation](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/process-data.md): Process image data using embeddings, filters, and filtering for high-quality dataset curation
- [Image Embedding](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/process-data/embeddings.md): Generate image embeddings using built-in embedders for classification, filtering, and similarity search
- [CLIP ImageEmbeddingStage](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/process-data/embeddings/clip-embedder.md): Generate CLIP embeddings for images using OpenAI's ViT-L/14 model for downstream classification and filtering tasks
- [Image Filters](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/process-data/filters.md): Image filtering tools including aesthetic and NSFW filters for dataset quality control
- [Aesthetic Filter](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/process-data/filters/aesthetic.md): Aesthetic filter for predicting visual quality of images using CLIP embeddings and human preference training
- [NSFW Filter](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/process-data/filters/nsfw.md): NSFW filter for detecting inappropriate content in images using CLIP embeddings and MLP architecture
- [Saving and Exporting Image Datasets](https://docs.nvidia.com/nemo/curator/v25.09/curate-images/save-export.md): Save metadata, export filtered datasets, and configure output sharding for downstream use after image curation
> **Note:** This page contains both a page directory (above) and the landing page content (below). The page directory is generated for agent use and does not appear on the landing page.
> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/v25.09/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.
# About Image Curation
> Overview of image data curation with NeMo Curator including loading, processing, filtering, and export workflows
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
DALI GPU-accelerated]
C --> D[CLIP Embeddings
ViT-L/14]
D --> E[Aesthetic Filtering
Quality scoring]
E --> F[NSFW Filtering
Content filtering]
F --> G[Duplicate Removal
Semantic deduplication]
G --> H[Export & Sharding
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
#### [Get Started](/get-started/image)
Learn prerequisites, setup instructions, and initial configuration for image curation
## 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
### Process Data
Transform and enhance your image data through embeddings, classification, and filters.
#### [Process Data](/curate-images/process-data)
Generate embeddings, apply filters, and prepare images for training
#### [Deduplication Tutorial](/curate-images/tutorials/dedup-workflow)
Remove duplicate images using semantic similarity and clustering
### 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
### 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