src

Training examples on TID2013 and LIVE datasets

git clone https://github.com/xialeiliu/RankIQA.git
cd RankIQA

Requirements

1. Requirements for caffe and pycaffe (see: Caffe installation instructions). Caffe must be built with support for Python layers!

# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1

2. Requirements for GPU (Titan X (~11G of memory) is needed to train VGG).

Preparing Ranking and IQA datasets

The details can be found in data

Pre-trained ImageNet VGG-16 model

Download the pre-trained VGG-16 ImageNet model and put it in the folder models.

RankIQA

To train the RankIQA models on tid2013 dataset:

./src/RankIQA/tid2013/train_vgg.sh

To train the RankIQA models on LIVE dataset:

./src/RankIQA/live/train_vgg.sh

FT

To train the RankIQA+FT models on tid2013 dataset:

./src/FT/tid2013/train_vgg.sh

To train the RankIQA+FT models on LIVE dataset:

./src/FT/live/train_live.sh

Evaluation for RankIQA on tid2013:

python src/eval/Rank_eval_each_tid2013.py  # evaluation for each distortions in tid2013
python src/eval/Rank_eval_all_tid2013.py   # evaluation for all distortions in tid2013

Evaluation for RankIQA+FT on tid2013:

python src/eval/FT_eval_each_tid2013.py  # evaluation for each distortions in tid2013
python src/eval/FT_eval_all_tid2013.py   # evaluation for all distortions in tid2013

Evaluation for RankIQA on LIVE:

python src/eval/Rank_eval_all_live.py   # evaluation for all distortions in LIVE

Evaluation for RankIQA+FT on LIVE:

python src/eval/FT_eval_all_live.py   # evaluation for all distortions in LIVE

Folder introdcutions

1. data_layer includes the Python functions to read the input data for different datasets.
2. MyLossLayer contains the Python functions of our efficient back-propagation method for different datasets.
3. tools provides the Python functions to calculate the evaluation matrix during training.
4. eval provides the Python functions to evaluate the trained models.