Image Aesthetic Assessment in PyTorch with implemented popular datasets and models (possibly providing the pretrained ones).
Note that this is a work in process.
- AVA Datasets A large-scale database for aesthetic visual analysis
- 250,000 images, 235556 (training) and 19926 (testing)
- aesthetic scores for each image
- semantic labels for over 60 categories
- labels related to photographic style
- AADB Datasets A aesthetics and attributes database
- 10,000 images in total, 8458 (training) and 1,000 (testing)
- aesthetic scores for each image
- meaningful attributes assigned to each image (TODO)
- CHAED A chinese handwriting aesthetic evaluation database
- 1000 Chinese handwriting images
- diverse aesthetic qualities for each image from three level
- rated by 33 subjects
- Unified Net Image Aesthetic Assessment Based on Pairwise Comparison – A Unified Approach to Score Regression, Binary Classification, and Personalization (In Progress)
- PAC-Net PAC-NET: PAIRWISE AESTHETIC COMPARISON NETWORK FOR IMAGE AESTHETIC ASSESSMENT (TODO)
- NIMA NIMA: Neural Image Assessment
- MPada MPada: Attention-based Multi-patch Aggregation for Image Aesthetic Assessment
python -u train.py --config path/to/your/config