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.

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.

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.

Process Data

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

Pipeline Management

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

Save & Export

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