Process Data for Image Curation#

Process image data you’ve loaded from tar archives using NeMo Curator’s suite of tools. These tools help you generate embeddings, filter images, and prepare your dataset to produce high-quality data for downstream AI tasks such as generative model training, dataset analysis, or quality control.

How it Works#

Image processing in NeMo Curator follows a pipeline-based approach with these stages:

  1. Partition files using FilePartitioningStage to distribute tar files

  2. Read images using ImageReaderStage with DALI acceleration

  3. Generate embeddings using ImageEmbeddingStage with CLIP models

  4. Apply filters using ImageAestheticFilterStage and ImageNSFWFilterStage

  5. Save results using ImageWriterStage to export curated datasets

Each stage processes ImageBatch objects containing images, metadata, and processing results. You can use built-in stages or create custom stages for advanced use cases.

Embedding Options#

CLIP Embedding Stage

Generate image embeddings using CLIP models with GPU acceleration. Supports various CLIP architectures and automatic model downloading.

ImageEmbeddingStage CLIP GPU-accelerated

CLIP ImageEmbeddingStage

Filter Options#

Aesthetic Filter Stage

Assess the subjective quality of images using a model trained on human aesthetic preferences. Filters images based on aesthetic score thresholds.

ImageAestheticFilterStage aesthetic_score

Aesthetic Filter
NSFW Filter Stage

Detect not-safe-for-work (NSFW) content in images using a CLIP-based filter. Filters explicit material from your datasets.

ImageNSFWFilterStage nsfw_score

NSFW Filter