# <img align="left" width="100" height="100" src="docs/pyiqa_logo.jpg"> PyTorch Toolbox for Image Quality Assessment
An IQA toolbox with pure python and pytorch. Please refer to [Awesome-Image-Quality-Assessment](https://github.com/chaofengc/Awesome-Image-Quality-Assessment) for a comprehensive survey of IQA methods and download links for IQA datasets.
<a href="https://colab.research.google.com/drive/14J3KoyrjJ6R531DsdOy5Bza5xfeMODi6?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
[](https://pypi.org/project/pyiqa/)
[](https://iqa-pytorch.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/chaofengc/Awesome-Image-Quality-Assessment)
[](https://github.com/chaofengc/IQA-PyTorch/blob/main/README.md#bookmark_tabs-citation)
<!--  -->
<!-- - [:open\_book: Introduction](#open_book-introduction)
- [:zap: Quick Start](#zap-quick-start)
- [Dependencies and Installation](#dependencies-and-installation)
- [Basic Usage](#basic-usage)
- [:1st\_place\_medal: Benchmark Performances and Model Zoo](#1st_place_medal-benchmark-performances-and-model-zoo)
- [Results Calibration](#results-calibration)
- [Performance Evaluation Protocol](#performance-evaluation-protocol)
- [Benchmark Performance with Provided Script](#benchmark-performance-with-provided-script)
- [:hammer\_and\_wrench: Train](#hammer_and_wrench-train)
- [Dataset Preparation](#dataset-preparation)
- [Example Train Script](#example-trai-script) -->
## :open_book: Introduction
This is a image quality assessment toolbox with **pure python and pytorch**. We provide reimplementation of many mainstream full reference (FR) and no reference (NR) metrics (results are calibrated with official matlab scripts if exist). **With GPU acceleration, most of our implementations are much faster than Matlab.** Please refer to the following documents for details:
<div align="center">
📦 [Model Cards](docs/ModelCard.md) | 🗃️ [Dataset Cards](docs/Dataset_Preparation.md) | 🤗 [Datasets](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets/tree/main) | 📚 [Documentation](https://iqa-pytorch.readthedocs.io/en/latest/)
</div>
---
### :triangular_flag_on_post: Updates/Changelog
- **Apr 24, 2024**. Add `inception_score` and console entry point with `pyiqa` command.
- **Mar 11, 2024**. Add `unique`, refer to official repo [here](https://github.com/zwx8981/UNIQUE). Thanks for the contribution from [Weixia Zhang](https://github.com/zwx8981) 🤗.
- :boom: **Jan 31, 2024**. Add `qalign` for both NR and IAA. It is our most powerful unified metric based on large vision-language models, and shows remarkable performance and robustness. Refer [Q-Align](https://github.com/Q-Future/Q-Align) for more details. Use it with the following codes:
```
qalign = create_metric('qalign').cuda()
quality_score = qalign(input, task_='quality')
aesthetic_score = qalign(input, task_='aesthetic')
```
- **Jan 19, 2024**. Add `wadiqam_fr` and `wadiqam_nr`. All implemented methods are usable now 🍻.
- **Dec 23, 2023**. Add `liqe` and `liqe_mix`. Thanks for the contribution from [Weixia Zhang](https://github.com/zwx8981) 🤗.
- **Oct 09, 2023**. Add datasets: [PIQ2023](https://github.com/DXOMARK-Research/PIQ2023), [GFIQA](http://database.mmsp-kn.de/gfiqa-20k-database.html). Add metric `topiq_nr-face`. We release example results on FFHQ [here](tests/ffhq_score_topiq_nr-face.csv) for reference.
- [**More**](docs/history_changelog.md)
---
## :zap: Quick Start
### Dependencies and Installation
- Ubuntu >= 18.04
- Python >= 3.8
- PyTorch >= 1.12
- Torchvision >= 0.13
- CUDA >= 10.2 (if use GPU)
```
# Install with pip
pip install pyiqa
# Install latest github version
pip uninstall pyiqa # if have older version installed already
pip install git+https://github.com/chaofengc/IQA-PyTorch.git
# Install with git clone
git clone https://github.com/chaofengc/IQA-PyTorch.git
cd IQA-PyTorch
pip install -r requirements.txt
python setup.py develop
```
### Basic Usage
#### Commandline Usage
You can now simply use the package with commandline interface.
```
# list all available metrics
pyiqa -ls
# test with default settings
pyiqa [metric_name(s)] --target [image_path or dir] --ref [image_path or dir]
```
#### Code Usage
```
import pyiqa
import torch
# list all available metrics
print(pyiqa.list_models())
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# create metric with default setting
iqa_metric = pyiqa.create_metric('lpips', device=device)
# Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.
# iqa_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)
# create metric with custom setting
iqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)
# check if lower better or higher better
print(iqa_metric.lower_better)
# example for iqa score inference
# Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1
score_fr = iqa_metric(img_tensor_x, img_tensor_y)
# img path as inputs.
score_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')
# For FID metric, use directory or precomputed statistics as inputs
# refer to clean-fid for more details: https://github.com/GaParmar/clean-fid
fid_metric = pyiqa.create_metric('fid')
score = fid_metric('./ResultsCalibra/dist_dir/', './ResultsCalibra/ref_dir')
score = fid_metric('./ResultsCalibra/dist_dir/', dataset_name="FFHQ", dataset_res=1024, dataset_split="trainval70k")
```
#### Example Test script
Example test script with input directory/images and reference directory/images.
```
# example for FR metric with dirs
python inference_iqa.py -m LPIPS[or lpips] -i ./ResultsCalibra/dist_dir[dist_img] -r ./ResultsCalibra/ref_dir[ref_img]
# example for NR metric with single image
python inference_iqa.py -m brisque -i ./ResultsCalibra/dist_dir/I03.bmp
```
## :1st_place_medal: Benchmark Performances and Model Zoo
### Results Calibration
Please refer to the [results calibration](./ResultsCalibra/ResultsCalibra.md) to verify the correctness of the python implementations compared with official scripts in matlab or python.
### ⏬ Download Benchmark Datasets
For convenience, we upload all related datasets to [huggingface](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets/tree/main). **Note that we only collect the datasets for academic, research, and educational purposes. It is important for the users to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset.** Here are example codes to download datasets from huggingface:
```
import os
from huggingface_hub import snapshot_download
save_dir = './datasets'
os.makedirs(save_dir, exist_ok=True)
filename = "meta_info.tgz"
snapshot_download("chaofengc/IQA-Toolbox-Datasets", repo_type="dataset", local_dir=save_dir, allow_patterns=filename, local_dir_use_symlinks=False)
os.system(f"tar -xzvf {save_dir}/{filename} -C {save_dir}")
```
Examples to specific dataset options can be found in `./options/default_dataset_opt.yml`. Details of the dataloader inferface and meta information files can be found in [Dataset Preparation](docs/Dataset_Preparation.md)
### Performance Evaluation Protocol
**We use official models for evaluation if available.** Otherwise, we use the following settings to train and evaluate different models for simplicity and consistency:
| Metric Type | Train | Test | Results |
| ------------- | --------- | ------------------------------------------ | -------------------------------------------------------- |
| FR | KADID-10k | CSIQ, LIVE, TID2008, TID2013 | [FR benchmark results](tests/FR_benchmark_results.csv) |
| NR | KonIQ-10k | LIVEC, KonIQ-10k (official split), TID2013, SPAQ | [NR benchmark results](tests/NR_benchmark_results.csv) |
| Aesthetic IQA | AVA | AVA (official split) | [IAA benchmark results](tests/IAA_benchmark_results.csv) |
Results are calculated with:
- **PLCC without any correction**. Although test time value correction is common in IQA papers, we want to use the original value in our benchmark.
- **Full image single input.** We **do not** use multi-patch testing unless necessary.
Basically, we use the largest existing datasets for training, and cross dataset evaluation performance for fair comparison. The following models do not provide official weights, and are retrained by our scripts:
| Metric Type | Reproduced Models |
| ------------- | ----------------------------- |
| FR | `wadiqam_fr` |
| NR | `cnniqa`, `dbcnn`, `hyperiqa`, `wadiqam_nr` |
| Aesthetic IQA | `nima`, `nima-vgg16-ava` |
**Important Notes:**
- Due to optimized training process, performance of some retrained approaches may be different with original paper.
- Results of all **retrained models by ours** are normalized to [0, 1] and change to higher better for convenience.
- Results of KonIQ-10k, AVA are both tested with official split.
- NIMA is only applicable to AVA dataset now. We use `inception_resnet_v2` for default `nima`.
- MUSIQ is not included in the IAA benchmark because we do not have train/split information of the official model.
### Benchmark Performance with Provided Script
Here is an example script to get performance benchmark on different datasets:
```
# NOTE: this script will test ALL specified metrics on ALL specified datasets
# Test default metrics on default datasets
python benchmark_results.py -m psnr ssim -d csiq tid2013 tid2008
# Test with your own options
python benchmark_results.py -m psnr --data_opt options/example_benchmark_data_opts.yml
python benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml tid2013 tid2008
python benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml --data_opt options/example_benchmark_data_opts.yml
```
## :hammer_and_wrench: Train
### Example Train Script
Example to train DBCNN on LIVEChallenge dataset
```
# train for single experiment
python pyiqa/train.py -opt options/train/DBCNN/train_DBCNN.yml
# train N splits for small datasets
python pyiqa/train_nsplits.py -opt options/train/DBCNN/train_DBCNN.yml
```
## :beers: Contribution
Any contributions to this repository are greatly appreciated. Please follow the [contribution instructions](docs/Instruction.md) for contribution guidance.
## :scroll: License
This work is licensed under a [NTU S-Lab License](https://github.com/chaofengc/IQA-PyTorch/blob/main/LICENSE_NTU-S-Lab) and <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>
## :bookmark_tabs: Citation
If you find our codes helpful to your research, please consider to use the following citation:
```
@misc{pyiqa,
title={{IQA-PyTorch}: PyTorch Toolbox for Image Quality Assessment},
author={Chaofeng Chen and Jiadi Mo},
year={2022},
howpublished = "[Online]. Available: \url{https://github.com/chaofengc/IQA-PyTorch}"
}
```
Please also consider to cite our works on image quality assessment if it is useful to you:
```
@article{chen2023topiq,
title={TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment},
author={Chen, Chaofeng and Mo, Jiadi and Hou, Jingwen and Wu, Haoning and Liao, Liang and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
journal={arXiv preprint arXiv:2308.03060},
year={2023}
}
```
```
@article{wu2023qalign,
title={Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels},
author={Wu, Haoning and Zhang, Zicheng and Zhang, Weixia and Chen, Chaofeng and Li, Chunyi and Liao, Liang and Wang, Annan and Zhang, Erli and Sun, Wenxiu and Yan, Qiong and Min, Xiongkuo and Zhai, Guangtai and Lin, Weisi},
journal={arXiv preprint arXiv:2312.17090},
year={2023},
institution={Nanyang Technological University and Shanghai Jiao Tong University and Sensetime Research},
note={Equal Contribution by Wu, Haoning and Zhang, Zicheng. Project Lead by Wu, Haoning. Corresponding Authors: Zhai, Guangtai and Lin, Weisi.}
}
```
## :heart: Acknowledgement
The code architecture is borrowed from [BasicSR](https://github.com/xinntao/BasicSR). Several implementations are taken from: [IQA-optimization](https://github.com/dingkeyan93/IQA-optimization), [Image-Quality-Assessment-Toolbox](https://github.com/RyanXingQL/Image-Quality-Assessment-Toolbox), [piq](https://github.com/photosynthesis-team/piq), [piqa](https://github.com/francois-rozet/piqa), [clean-fid](https://github.com/GaParmar/clean-fid)
We also thanks the following public repositories: [MUSIQ](https://github.com/google-research/google-research/tree/master/musiq), [DBCNN](https://github.com/zwx8981/DBCNN-PyTorch), [NIMA](https://github.com/kentsyx/Neural-IMage-Assessment), [HyperIQA](https://github.com/SSL92/hyperIQA), [CNNIQA](https://github.com/lidq92/CNNIQA), [WaDIQaM](https://github.com/lidq92/WaDIQaM), [PieAPP](https://github.com/prashnani/PerceptualImageError), [paq2piq](https://github.com/baidut/paq2piq), [MANIQA](https://github.com/IIGROUP/MANIQA)
## :e-mail: Contact
If you have any questions, please email `chaofenghust@gmail.com`
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"description": "# <img align=\"left\" width=\"100\" height=\"100\" src=\"docs/pyiqa_logo.jpg\"> PyTorch Toolbox for Image Quality Assessment\n\nAn IQA toolbox with pure python and pytorch. Please refer to [Awesome-Image-Quality-Assessment](https://github.com/chaofengc/Awesome-Image-Quality-Assessment) for a comprehensive survey of IQA methods and download links for IQA datasets.\n\n<a href=\"https://colab.research.google.com/drive/14J3KoyrjJ6R531DsdOy5Bza5xfeMODi6?usp=sharing\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"google colab logo\"></a> \n[](https://pypi.org/project/pyiqa/)\n \n[](https://iqa-pytorch.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/chaofengc/Awesome-Image-Quality-Assessment)\n[](https://github.com/chaofengc/IQA-PyTorch/blob/main/README.md#bookmark_tabs-citation)\n\n<!--  -->\n\n<!-- - [:open\\_book: Introduction](#open_book-introduction)\n- [:zap: Quick Start](#zap-quick-start)\n - [Dependencies and Installation](#dependencies-and-installation)\n - [Basic Usage](#basic-usage)\n- [:1st\\_place\\_medal: Benchmark Performances and Model Zoo](#1st_place_medal-benchmark-performances-and-model-zoo)\n - [Results Calibration](#results-calibration)\n - [Performance Evaluation Protocol](#performance-evaluation-protocol)\n - [Benchmark Performance with Provided Script](#benchmark-performance-with-provided-script)\n- [:hammer\\_and\\_wrench: Train](#hammer_and_wrench-train)\n - [Dataset Preparation](#dataset-preparation)\n - [Example Train Script](#example-trai-script) -->\n \n## :open_book: Introduction\n\nThis is a image quality assessment toolbox with **pure python and pytorch**. We provide reimplementation of many mainstream full reference (FR) and no reference (NR) metrics (results are calibrated with official matlab scripts if exist). **With GPU acceleration, most of our implementations are much faster than Matlab.** Please refer to the following documents for details: \n\n<div align=\"center\">\n\n\ud83d\udce6 [Model Cards](docs/ModelCard.md) | \ud83d\uddc3\ufe0f [Dataset Cards](docs/Dataset_Preparation.md) | \ud83e\udd17 [Datasets](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets/tree/main) | \ud83d\udcda [Documentation](https://iqa-pytorch.readthedocs.io/en/latest/) \n\n</div>\n\n---\n\n### :triangular_flag_on_post: Updates/Changelog\n- **Apr 24, 2024**. Add `inception_score` and console entry point with `pyiqa` command.\n- **Mar 11, 2024**. Add `unique`, refer to official repo [here](https://github.com/zwx8981/UNIQUE). Thanks for the contribution from [Weixia Zhang](https://github.com/zwx8981) \ud83e\udd17.\n- :boom: **Jan 31, 2024**. Add `qalign` for both NR and IAA. It is our most powerful unified metric based on large vision-language models, and shows remarkable performance and robustness. Refer [Q-Align](https://github.com/Q-Future/Q-Align) for more details. Use it with the following codes:\n ```\n qalign = create_metric('qalign').cuda()\n quality_score = qalign(input, task_='quality')\n aesthetic_score = qalign(input, task_='aesthetic')\n ```\n- **Jan 19, 2024**. Add `wadiqam_fr` and `wadiqam_nr`. All implemented methods are usable now \ud83c\udf7b. \n- **Dec 23, 2023**. Add `liqe` and `liqe_mix`. Thanks for the contribution from [Weixia Zhang](https://github.com/zwx8981) \ud83e\udd17.\n- **Oct 09, 2023**. Add datasets: [PIQ2023](https://github.com/DXOMARK-Research/PIQ2023), [GFIQA](http://database.mmsp-kn.de/gfiqa-20k-database.html). Add metric `topiq_nr-face`. We release example results on FFHQ [here](tests/ffhq_score_topiq_nr-face.csv) for reference. \n- [**More**](docs/history_changelog.md)\n\n---\n\n## :zap: Quick Start\n\n### Dependencies and Installation\n- Ubuntu >= 18.04\n- Python >= 3.8\n- PyTorch >= 1.12\n- Torchvision >= 0.13\n- CUDA >= 10.2 (if use GPU)\n```\n# Install with pip\npip install pyiqa\n\n# Install latest github version\npip uninstall pyiqa # if have older version installed already \npip install git+https://github.com/chaofengc/IQA-PyTorch.git\n\n# Install with git clone\ngit clone https://github.com/chaofengc/IQA-PyTorch.git\ncd IQA-PyTorch\npip install -r requirements.txt\npython setup.py develop\n```\n\n### Basic Usage \n\n#### Commandline Usage\n\nYou can now simply use the package with commandline interface. \n```\n# list all available metrics\npyiqa -ls\n\n# test with default settings\npyiqa [metric_name(s)] --target [image_path or dir] --ref [image_path or dir]\n```\n\n#### Code Usage\n\n```\nimport pyiqa\nimport torch\n\n# list all available metrics\nprint(pyiqa.list_models())\n\ndevice = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\n# create metric with default setting\niqa_metric = pyiqa.create_metric('lpips', device=device)\n# Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.\n# iqa_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)\n\n# create metric with custom setting\niqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)\n\n# check if lower better or higher better\nprint(iqa_metric.lower_better)\n\n# example for iqa score inference\n# Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1\nscore_fr = iqa_metric(img_tensor_x, img_tensor_y)\n\n# img path as inputs.\nscore_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')\n\n# For FID metric, use directory or precomputed statistics as inputs\n# refer to clean-fid for more details: https://github.com/GaParmar/clean-fid\nfid_metric = pyiqa.create_metric('fid')\nscore = fid_metric('./ResultsCalibra/dist_dir/', './ResultsCalibra/ref_dir')\nscore = fid_metric('./ResultsCalibra/dist_dir/', dataset_name=\"FFHQ\", dataset_res=1024, dataset_split=\"trainval70k\")\n```\n\n#### Example Test script\n\nExample test script with input directory/images and reference directory/images. \n```\n# example for FR metric with dirs\npython inference_iqa.py -m LPIPS[or lpips] -i ./ResultsCalibra/dist_dir[dist_img] -r ./ResultsCalibra/ref_dir[ref_img]\n\n# example for NR metric with single image\npython inference_iqa.py -m brisque -i ./ResultsCalibra/dist_dir/I03.bmp\n```\n\n## :1st_place_medal: Benchmark Performances and Model Zoo\n\n### Results Calibration\n\nPlease refer to the [results calibration](./ResultsCalibra/ResultsCalibra.md) to verify the correctness of the python implementations compared with official scripts in matlab or python.\n\n### \u23ec Download Benchmark Datasets\n\nFor convenience, we upload all related datasets to [huggingface](https://huggingface.co/datasets/chaofengc/IQA-Toolbox-Datasets/tree/main). **Note that we only collect the datasets for academic, research, and educational purposes. It is important for the users to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset.** Here are example codes to download datasets from huggingface:\n\n```\nimport os\nfrom huggingface_hub import snapshot_download\n\nsave_dir = './datasets'\nos.makedirs(save_dir, exist_ok=True)\n\nfilename = \"meta_info.tgz\"\nsnapshot_download(\"chaofengc/IQA-Toolbox-Datasets\", repo_type=\"dataset\", local_dir=save_dir, allow_patterns=filename, local_dir_use_symlinks=False)\n\nos.system(f\"tar -xzvf {save_dir}/{filename} -C {save_dir}\")\n```\n\nExamples to specific dataset options can be found in `./options/default_dataset_opt.yml`. Details of the dataloader inferface and meta information files can be found in [Dataset Preparation](docs/Dataset_Preparation.md)\n\n### Performance Evaluation Protocol\n\n**We use official models for evaluation if available.** Otherwise, we use the following settings to train and evaluate different models for simplicity and consistency:\n\n| Metric Type | Train | Test | Results | \n| ------------- | --------- | ------------------------------------------ | -------------------------------------------------------- |\n| FR | KADID-10k | CSIQ, LIVE, TID2008, TID2013 | [FR benchmark results](tests/FR_benchmark_results.csv) |\n| NR | KonIQ-10k | LIVEC, KonIQ-10k (official split), TID2013, SPAQ | [NR benchmark results](tests/NR_benchmark_results.csv) |\n| Aesthetic IQA | AVA | AVA (official split) | [IAA benchmark results](tests/IAA_benchmark_results.csv) |\n\nResults are calculated with:\n- **PLCC without any correction**. Although test time value correction is common in IQA papers, we want to use the original value in our benchmark.\n- **Full image single input.** We **do not** use multi-patch testing unless necessary.\n\nBasically, we use the largest existing datasets for training, and cross dataset evaluation performance for fair comparison. The following models do not provide official weights, and are retrained by our scripts:\n\n| Metric Type | Reproduced Models |\n| ------------- | ----------------------------- |\n| FR | `wadiqam_fr` |\n| NR | `cnniqa`, `dbcnn`, `hyperiqa`, `wadiqam_nr` |\n| Aesthetic IQA | `nima`, `nima-vgg16-ava` |\n\n**Important Notes:**\n- Due to optimized training process, performance of some retrained approaches may be different with original paper.\n- Results of all **retrained models by ours** are normalized to [0, 1] and change to higher better for convenience.\n- Results of KonIQ-10k, AVA are both tested with official split.\n- NIMA is only applicable to AVA dataset now. We use `inception_resnet_v2` for default `nima`.\n- MUSIQ is not included in the IAA benchmark because we do not have train/split information of the official model.\n\n### Benchmark Performance with Provided Script\n\nHere is an example script to get performance benchmark on different datasets:\n```\n# NOTE: this script will test ALL specified metrics on ALL specified datasets\n# Test default metrics on default datasets\npython benchmark_results.py -m psnr ssim -d csiq tid2013 tid2008\n\n# Test with your own options\npython benchmark_results.py -m psnr --data_opt options/example_benchmark_data_opts.yml\n\npython benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml tid2013 tid2008\n\npython benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml --data_opt options/example_benchmark_data_opts.yml\n```\n\n## :hammer_and_wrench: Train\n\n\n\n### Example Train Script\n\nExample to train DBCNN on LIVEChallenge dataset\n```\n# train for single experiment\npython pyiqa/train.py -opt options/train/DBCNN/train_DBCNN.yml\n\n# train N splits for small datasets\npython pyiqa/train_nsplits.py -opt options/train/DBCNN/train_DBCNN.yml\n```\n\n\n## :beers: Contribution\n\nAny contributions to this repository are greatly appreciated. Please follow the [contribution instructions](docs/Instruction.md) for contribution guidance.\n\n## :scroll: License\n\nThis work is licensed under a [NTU S-Lab License](https://github.com/chaofengc/IQA-PyTorch/blob/main/LICENSE_NTU-S-Lab) and <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.\n\n<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a>\n\n## :bookmark_tabs: Citation\n\nIf you find our codes helpful to your research, please consider to use the following citation:\n\n```\n@misc{pyiqa,\n title={{IQA-PyTorch}: PyTorch Toolbox for Image Quality Assessment},\n author={Chaofeng Chen and Jiadi Mo},\n year={2022},\n howpublished = \"[Online]. Available: \\url{https://github.com/chaofengc/IQA-PyTorch}\"\n}\n```\n\nPlease also consider to cite our works on image quality assessment if it is useful to you:\n```\n@article{chen2023topiq,\n title={TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment},\n author={Chen, Chaofeng and Mo, Jiadi and Hou, Jingwen and Wu, Haoning and Liao, Liang and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},\n journal={arXiv preprint arXiv:2308.03060},\n year={2023}\n}\n``` \n```\n@article{wu2023qalign,\n title={Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels},\n author={Wu, Haoning and Zhang, Zicheng and Zhang, Weixia and Chen, Chaofeng and Li, Chunyi and Liao, Liang and Wang, Annan and Zhang, Erli and Sun, Wenxiu and Yan, Qiong and Min, Xiongkuo and Zhai, Guangtai and Lin, Weisi},\n journal={arXiv preprint arXiv:2312.17090},\n year={2023},\n institution={Nanyang Technological University and Shanghai Jiao Tong University and Sensetime Research},\n note={Equal Contribution by Wu, Haoning and Zhang, Zicheng. Project Lead by Wu, Haoning. Corresponding Authors: Zhai, Guangtai and Lin, Weisi.}\n}\n```\n\n## :heart: Acknowledgement\n\nThe code architecture is borrowed from [BasicSR](https://github.com/xinntao/BasicSR). Several implementations are taken from: [IQA-optimization](https://github.com/dingkeyan93/IQA-optimization), [Image-Quality-Assessment-Toolbox](https://github.com/RyanXingQL/Image-Quality-Assessment-Toolbox), [piq](https://github.com/photosynthesis-team/piq), [piqa](https://github.com/francois-rozet/piqa), [clean-fid](https://github.com/GaParmar/clean-fid)\n\nWe also thanks the following public repositories: [MUSIQ](https://github.com/google-research/google-research/tree/master/musiq), [DBCNN](https://github.com/zwx8981/DBCNN-PyTorch), [NIMA](https://github.com/kentsyx/Neural-IMage-Assessment), [HyperIQA](https://github.com/SSL92/hyperIQA), [CNNIQA](https://github.com/lidq92/CNNIQA), [WaDIQaM](https://github.com/lidq92/WaDIQaM), [PieAPP](https://github.com/prashnani/PerceptualImageError), [paq2piq](https://github.com/baidut/paq2piq), [MANIQA](https://github.com/IIGROUP/MANIQA) \n\n## :e-mail: Contact\n\nIf you have any questions, please email `chaofenghust@gmail.com`\n",
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