idealo/image-quality-assessment

Health v2 · maintenance only

Flags: no_releases archived

Adoption not part of the score

2243 stars · 455 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python implementation of Google's NIMA (Neural Image Assessment) models that predict the aesthetic and technical quality of images using fine-tuned CNNs like MobileNet. It ships pre-trained models on the AVA and TID2013 datasets plus Docker images for CPU training locally and GPU training on AWS EC2.

Use cases

• score the aesthetic quality of photos automatically
• rank millions of product or hotel images by visual appeal
• predict technical image quality like noise and blur
• filter low-quality user-uploaded images
• train an image quality model on custom data
• run NIMA image assessment in a Docker container

When to choose

• you need pre-trained aesthetic or technical image quality scoring with Keras/TensorFlow
• you want to fine-tune NIMA models on your own image dataset
• you need to rank large image collections by quality at scale

When to avoid

• you need a maintained library with recent framework support (Python 3.6 era, sparse recent activity)
• you need general-purpose image processing rather than quality scoring
• you work outside TensorFlow/Keras ecosystems

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision image-processing machine-learning deep-learning python cloud nima image-quality aesthetics tensorflow keras mobilenet transfer-learning aws docker gpu

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markdown · JSON · MCP: product_card(name="idealo/image-quality-assessment")