***
description: >-
Process image data using embeddings, filters, and filtering for high-quality
dataset curation
categories:
* workflows
tags:
* data-processing
* embedding
* filtering
* gpu-accelerated
personas:
* data-scientist-focused
* mle-focused
difficulty: intermediate
content\_type: workflow
modality: image-only
***
# 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
Generate image embeddings using CLIP models with GPU acceleration. Supports various CLIP architectures and automatic model downloading.
ImageEmbeddingStage CLIP GPU-accelerated
## Filter Options
Assess the subjective quality of images using a model trained on human aesthetic preferences. Filters images based on aesthetic score thresholds.
ImageAestheticFilterStage aesthetic\_score
Detect not-safe-for-work (NSFW) content in images using a CLIP-based filter. Filters explicit material from your datasets.
ImageNSFWFilterStage nsfw\_score