Image Aesthetic Assessment in PyTorch

Image Aesthetic Assessment in PyTorch with implemented popular datasets and models (possibly providing the pretrained ones).

Note that this is a work in process.

Supported Datasets

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
MP_ada MP_ada: Attention-based Multi-patch Aggregation for Image Aesthetic Assessment

python -u train.py --config path/to/your/config

Name		Name	Last commit message	Last commit date
Latest commit History 58 Commits
common		common
config		config
dataloader		dataloader
dataset		dataset
loss		loss
lr_scheduler		lr_scheduler
metric		metric
model		model
optimizer		optimizer
third_party/mpada		third_party/mpada
trainer		trainer
transform		transform
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
convert_d2_to_torchvision.py		convert_d2_to_torchvision.py
train.py		train.py