README.md
December 29, 2023 · View on GitHub
NIMA: Neural IMage Assessment
This is a Paddle implementation of the paper NIMA: Neural IMage Assessment (accepted at IEEE Transactions on Image Processing) by Hossein Talebi and Peyman Milanfar. You can learn more from this post at Google Research Blog.
Usage
To start training on the AVA dataset, first download the dataset from the link above and decompress which should create a directory named images/. Then download the curated annotation CSVs below
which already splits the dataset (You can create your own split of course). Then do
python main.py --img_path /path/to/images/ --train --train_csv_file /path/to/train_labels.csv --val_csv_file /path/to/val_labels.csv --conv_base_lr 5e-4 --dense_lr 5e-3 --decay --ckpt_path /path/to/ckpts --epochs 100 --early_stoppping_patience 10
For inference, do
python -W ignore test.py --model /path/to/your_model --test_csv /path/to/test_labels.csv --test_images /path/to/images --predictions /path/to/save/predictions
See predictions/ for dumped predictions as an example.
Pretrained Model
链接：链接：https://pan.baidu.com/s/1cg95cbA0VJieQfpSdUZvFw 提取码：NIMA