Download
Image Quality Assessment is an open-source deep learning project that implements neural models for predicting the aesthetic and technical quality of digital images. The repository provides an implementation inspired by the NIMA (Neural Image Assessment) research approach, which uses convolutional neural networks trained on human-annotated datasets to estimate image quality scores. The goal of the project is to automatically evaluate images based on perceived quality factors such as composition, clarity, and visual appeal. Instead of relying on simple image statistics, the system learns patterns that correlate with human judgments about image aesthetics and technical quality. The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models.
Features
- Neural network implementation of the NIMA image quality assessment model
- Prediction of aesthetic and technical image quality scores
- Training pipelines for image datasets labeled with human quality ratings
- Pretrained models trained on datasets such as AVA and TID2013
- Image preprocessing utilities for dataset preparation
- Support for GPU training and Docker deployment environments
Project Samples
