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Repository: woshidandan/TANet
Branch: main
Commit: da7aebdb38ea
Files: 22
Total size: 116.7 MB
Directory structure:
gitextract_1n9q0h5w/
├── LICENSE
├── README.md
├── README_CN.md
├── code/
│ ├── AVA/
│ │ ├── SRCC_758_LCC_765.pth
│ │ ├── config.yml
│ │ ├── dataset/
│ │ │ ├── test.csv
│ │ │ ├── train.csv
│ │ │ └── val.csv
│ │ ├── dataset.py
│ │ ├── option.py
│ │ ├── search_space.json
│ │ ├── train_nni.py
│ │ └── util.py
│ └── TAD66K/
│ ├── SRCC_513_LCC_531_MSE_016.pth
│ ├── config.yml
│ ├── dataset.py
│ ├── option.py
│ ├── search_space.json
│ ├── train_nni.py
│ └── util.py
├── docs/
│ └── a
└── improved_work/
└── README.md
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
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FILE: README.md
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[](https://opensource.org/licenses/Apache-2.0)
[](https://pytorch.org/)
[[国内的小伙伴请看更详细的中文说明]](https://github.com/woshidandan/TANet/blob/main/README_CN.md)This repo contains the official implementation and the new IAA dataset TAD66K of the **IJCAI 2022** paper. Our new work on ICCV2023：[Link](https://github.com/woshidandan/Image-Color-Aesthetics-Assessment)
<div align="center">
<h1>
<b>
Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks
</b>
</h1>
<h4>
<b>
Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, Anlong Ming
Beijing University of Posts and Telecommunications
</b>
</h4>
</div>
<!-- 
 -->
------------------------------------------------------------------------------------------------------------
# TAD66K <a href=""><img width="48" src="docs/release_icon.png"></a>
## Introduction
* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models.
<div align="center">
</div>
## Download Dataset
* Download from [here google](https://drive.google.com/drive/folders/1b2D9LeeG5XZzhEa8ldnIZjGh0IHadHhU?usp=sharing), it contains images with the largest side scaled to 800, and labels categorized by different themes.
* or [here baidu](https://pan.baidu.com/s/1bAiDMwKLF_vLZKelz5ZfRg), code: 8888
------------------------------------------------------------------------------------------------------------
# TANet <a href=""><img width="48" src="docs/release_icon.png"></a>
## Introduction
We propose a baseline model, called the Theme and Aesthetics Network (TANet), which can maintain a constant perception of aesthetics to effectively deal with the problem of attention dispersion. Moreover, TANet can adaptively learn the rules for predicting aesthetics according to a recognized theme. By comparing 17 methods with TANet on three representative datasets: AVA, FLICKR-AES and the proposed TAD66K, TANet achieves state-of-the-art performance on all three datasets.
## Environment Installation
* pandas==0.22.0
* nni==1.8
* requests==2.18.4
* torchvision==0.8.2+cu101
* numpy==1.13.3
* scipy==0.19.1
* tqdm==4.43.0
* torch==1.7.1+cu101
* scikit_learn==1.0.2
* tensorboardX==2.5
## How to Run the Code
* We used the hyperparameter tuning tool [nni](https://github.com/microsoft/nni), maybe you should know how to use this tool first (it will only take a few minutes of your time), because our training and testing will be in this tool.
* Train or test, please run: nnictl create --config config.yml -p 8999
* The Web UI urls are: http://127.0.0.1:8999 or http://172.17.0.3:8999
* Note: nni is not necessary, if you don't want to use this tool, just make simple modifications to our code, such as changing param_group['lr'] to param_group.lr, etc.
* PS: The work of train on the FLICKR-AES dataset may not be made public, because we are currently cooperating with a company, and the relevant model has been embedded into the system, and there are some confidentiality requirements.
## If you find our work is useful, pleaes cite our paper:
```
@article{herethinking,
title={Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks},
author={He, Shuai and Zhang, Yongchang and Xie, Rui and Jiang, Dongxiang and Ming, Anlong},
journal={IJCAI},
year={2022},
}
```
## Try!
https://user-images.githubusercontent.com/15050507/164580816-f98d1dd9-50a0-47b7-b992-2f0374e8a418.mp4
https://user-images.githubusercontent.com/15050507/164580823-4ea8ff91-825b-43dc-a421-f75455e549ae.mp4
https://user-images.githubusercontent.com/15050507/164580840-b7f5624f-486d-46e6-9dd4-efaa92dde09c.mp4
## Related Work from Our Group
<table>
<thead align="center">
<tr>
<td><b>🎁 Projects</b></td>
<td><b>📚 Publication</b></td>
<td><b>🌈 Content</b></td>
<td><b>⭐ Stars</b></td>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://github.com/woshidandan/Attacker-against-image-aesthetics-assessment-model"><b>Attacker Against IAA Model【美学模型的攻击和安全评估框架】</b></a></td>
<td><b>TIP 2025</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Attacker-against-image-aesthetics-assessment-model?style=flat-square&labelColor=343b41"/></td>
</tr
<tr>
<td><a href="https://github.com/woshidandan/Rethinking-Personalized-Aesthetics-Assessment"><b>Personalized Aesthetics Assessment【个性化美学评估新范式】</b></a></td>
<td><b>CVPR 2025</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Rethinking-Personalized-Aesthetics-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Pixel-level-No-reference-Image-Exposure-Assessment"><b>Pixel-level image exposure assessment【首个像素级曝光评估】</b></a></td>
<td><b>NIPS 2024</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Pixel-level-No-reference-Image-Exposure-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Long-Tail-image-aesthetics-and-quality-assessment"><b>Long-tail solution for image aesthetics assessment【美学评估数据不平衡解决方案】</b></a></td>
<td><b>ICML 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Long-Tail-image-aesthetics-and-quality-assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Prompt-DeT"><b>CLIP-based image aesthetics assessment【基于CLIP多因素色彩美学评估】</b></a></td>
<td><b>Information Fusion 2024</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Prompt-DeT?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/SR-IAA-image-aesthetics-and-quality-assessment"><b>Compare-based image aesthetics assessment【基于对比学习的多因素美学评估】</b></a></td>
<td><b>ACMMM 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/SR-IAA-image-aesthetics-and-quality-assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Image-Color-Aesthetics-and-Quality-Assessment"><b>Image color aesthetics assessment【首个色彩美学评估】</b></a></td>
<td><b>ICCV 2023</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Image-Color-Aesthetics-and-Quality-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Image-Aesthetics-and-Quality-Assessment"><b>Image aesthetics assessment【通用美学评估】</b></a></td>
<td><b>ACMMM 2023</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Image-Aesthetics-and-Quality-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/TANet-image-aesthetics-and-quality-assessment"><b>Theme-oriented image aesthetics assessment【首个多主题美学评估】</b></a></td>
<td><b>IJCAI 2022</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/TANet-image-aesthetics-and-quality-assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/AK4Prompts"><b>Select prompt based on image aesthetics assessment【基于美学评估的提示词筛选】</b></a></td>
<td><b>IJCAI 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/AK4Prompts?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/mRobotit/M2Beats"><b>Motion rhythm synchronization with beats【动作与韵律对齐】</b></a></td>
<td><b>IJCAI 2024</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/mRobotit/M2Beats?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Champion-Solution-for-CVPR-NTIRE-2024-Quality-Assessment-on-AIGC"><b>Champion Solution for AIGC Image Quality Assessment【NTIRE AIGC图像质量评估赛道冠军】</b></a></td>
<td><b>CVPRW NTIRE 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Champion-Solution-for-CVPR-NTIRE-2024-Quality-Assessment-on-AIGC?style=flat-square&labelColor=343b41"/></td>
</tr>
</tbody>
</table>
================================================
FILE: README_CN.md
================================================
[](https://opensource.org/licenses/Apache-2.0)
[](https://pytorch.org/)
这是我们组在IJCAI 2022关于图像美学评估最新的一篇工作:
<div align="center">
<h1>
<b>
Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks
</b>
</h1>
<h4>
<b>
Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, Anlong Ming
Beijing University of Posts and Telecommunications
</b>
</h4>
</div>
因我个人热衷于开源，希望更多的小伙伴关注到这篇工作，故额外写了一篇中文的介绍，不要忘记给我们一个小星星哦，Star一下吧！我们组在ICCV2023的最新工作：[链接](https://github.com/woshidandan/Image-Color-Aesthetics-Assessment)
<!-- 
 -->
------------------------------------------------------------------------------------------------------------
# TAD66K数据集 <a href=""><img width="48" src="docs/release_icon.png"></a>
## 介绍
* 简要版：一个新的美学数据集，6万6千张左右图像，按主题分类标注。
* 太长不看版：以主题为核心，以开源为理念，我们建立了一个包含6万6千张左右图像的数据集，可用于图像美学评估。建立这样一个数据集的初衷，源自于组内同学在标注图像美感时的困惑，我们如何去评价一朵花和一个人之间美感的区别呢？显然，不同主题的图片，通常包含了不同的评分规则，标注人员在标注图像的过程中，隐性的会考虑到当前图像的主题，但现有的数据集，通常将所有类别的图像混合在一起进行标注，这可能会引入大量的噪声。因此，我们通过半年多的时间，收集，整理和标注了一批图片，包含了47种常见的主题，每个主题包含1千张以上的图像，各个主题的图像分开标注，每张图像至少被1200以上的人浏览和评价过，计算出平均分作为分数。
<div align="center">
</div>
## Download
* 你可以从这里下载到数据集和标注分数 [here](https://drive.google.com/drive/folders/1b2D9LeeG5XZzhEa8ldnIZjGh0IHadHhU?usp=sharing)，如果失效了，记得cue我，每张图像的最大边按等比放缩至800，标注文件都按主题类别分开整理好了。
* 百度网盘：链接：https://pan.baidu.com/s/1bAiDMwKLF_vLZKelz5ZfRg 提取码：8888
------------------------------------------------------------------------------------------------------------
# TANet网络 <a href=""><img width="48" src="docs/release_icon.png"></a>
## 介绍
* 简要版：在通用美学数据集AVA，个性化美学数据集FLICKR-AES, 以及自建的数据集TAD66K，全SOTA。
* 太长不看版：我们提出了一个以主题为核心的网络架构TANet，在搭建这个网络的过程中，希望其能提取出当前图像的主题用于进一步的美感评估，因此将一个百万级别的数据集Place用来预训练我们其中的一个分支。Place数据集包含多数现实场景，虽然场景无法直接等效于主题，但据我们所知，这是目前最好的能进行主题感知的方法。值得注意的是，我们发现经过预训练的分支会出现注意力弥散现象，这会导致费尽力气预训练获得的主题感知能力丧失，这一点在此前用ImageNet进行预训练的工作中也有体现，因此我们会将该分支直接冻结。为了让网络能够自适应的利用主题信息，融合的权重是其学习得到的；为了能够让其获得图像中不同区域色彩的分布及关系信息，我们专门加了一个类似自注意力机制的分支。
* 在对比性能时，此前的工作对比指标通常不统一，我们索性把所有发布了开源代码的工作都自己跑了一遍，填补了相关缺失的指标，发布了一个目前最全的benchmark。
## 代码环境
* pandas==0.22.0
* nni==1.8
* requests==2.18.4
* torchvision==0.8.2+cu101
* numpy==1.13.3
* scipy==0.19.1
* tqdm==4.43.0
* torch==1.7.1+cu101
* scikit_learn==1.0.2
* tensorboardX==2.5
## 如何训练和测试
* 炼丹是一个痛苦的过程，特别是像TANet这种的多分支网络，每个分支若设置相同的学习率，训练起来无法达到最优的性能，若单独手工设置各分支学习率，耗时又耗力，所以这里面我们用了微软的自动调参工具[nni](https://github.com/microsoft/nni)，网上有很多nni相关的[使用教程](https://blog.csdn.net/weixin_43653494/article/details/101039198)，强烈推荐同学们使用这个工具，不仅能自动调参，还能替代TensorBoard对训练过程的各项指标可视化。
* 如果你安装好了nni之后，训练时请配置好config.yml和超参数文件search_space.json，然后运行nnictl create --config config.yml -p 8999，训练的可视化后台可以在本地的http://127.0.0.1:8999 或 http://172.17.0.3:8999 看到。
* 如果你不想用这个工具训练或测试，只需要将代码中类似于param_group['lr']这样的超参数的中括号都改为param_group.lr就可以了。
* PS：FLICKR-AES这个数据集上train的工作可能不会公开，因为目前和一个公司合作，相关的模型已经嵌入进系统中，有一些保密的需求。
## 其它
* 事实上，以TAD66K为起点，后续还有很多工作可以尝试，即使是我们自己发布的TANet，性能也远未达到上限，因为TAD66K每个主题都对应了不同的评分规则，任务难度更大，这点可以在SRCC这种的指标上看出，目前只有0.513，有兴趣的同学，可以继续加油，把指标刷上去。
* 我们实验室的主页：[视觉机器人与智能技术实验室](http://www.mrobotit.cn/Default.aspx)。
* 我的个人主页：[博客](https://xiaohegithub.cn/)，[知乎](https://www.zhihu.com/people/wo-shi-dan-dan-87)。
## 如果你觉得这篇工作对你有帮助，请引用，不要白嫖-_-:
```
@article{herethinking,
title={Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks},
author={He, Shuai and Zhang, Yongchang and Xie, Rui and Jiang, Dongxiang and Ming, Anlong},
journal={IJCAI},
year={2022},
}
```
## 快来尝试一下吧!
https://user-images.githubusercontent.com/15050507/164580816-f98d1dd9-50a0-47b7-b992-2f0374e8a418.mp4
https://user-images.githubusercontent.com/15050507/164580823-4ea8ff91-825b-43dc-a421-f75455e549ae.mp4
https://user-images.githubusercontent.com/15050507/164580840-b7f5624f-486d-46e6-9dd4-efaa92dde09c.mp4
## Related Work from Our Group
<table>
<thead align="center">
<tr>
<td><b>🎁 Projects</b></td>
<td><b>📚 Publication</b></td>
<td><b>🌈 Content</b></td>
<td><b>⭐ Stars</b></td>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://github.com/woshidandan/Attacker-against-image-aesthetics-assessment-model"><b>Attacker Against IAA Model【美学模型的攻击和安全评估框架】</b></a></td>
<td><b>TIP 2025</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Attacker-against-image-aesthetics-assessment-model?style=flat-square&labelColor=343b41"/></td>
</tr
<tr>
<td><a href="https://github.com/woshidandan/Rethinking-Personalized-Aesthetics-Assessment"><b>Personalized Aesthetics Assessment【个性化美学评估新范式】</b></a></td>
<td><b>CVPR 2025</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Rethinking-Personalized-Aesthetics-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Pixel-level-No-reference-Image-Exposure-Assessment"><b>Pixel-level image exposure assessment【首个像素级曝光评估】</b></a></td>
<td><b>NIPS 2024</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Pixel-level-No-reference-Image-Exposure-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Long-Tail-image-aesthetics-and-quality-assessment"><b>Long-tail solution for image aesthetics assessment【美学评估数据不平衡解决方案】</b></a></td>
<td><b>ICML 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Long-Tail-image-aesthetics-and-quality-assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Prompt-DeT"><b>CLIP-based image aesthetics assessment【基于CLIP多因素色彩美学评估】</b></a></td>
<td><b>Information Fusion 2024</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Prompt-DeT?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/SR-IAA-image-aesthetics-and-quality-assessment"><b>Compare-based image aesthetics assessment【基于对比学习的多因素美学评估】</b></a></td>
<td><b>ACMMM 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/SR-IAA-image-aesthetics-and-quality-assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Image-Color-Aesthetics-and-Quality-Assessment"><b>Image color aesthetics assessment【首个色彩美学评估】</b></a></td>
<td><b>ICCV 2023</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Image-Color-Aesthetics-and-Quality-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Image-Aesthetics-and-Quality-Assessment"><b>Image aesthetics assessment【通用美学评估】</b></a></td>
<td><b>ACMMM 2023</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Image-Aesthetics-and-Quality-Assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/TANet-image-aesthetics-and-quality-assessment"><b>Theme-oriented image aesthetics assessment【首个多主题美学评估】</b></a></td>
<td><b>IJCAI 2022</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/TANet-image-aesthetics-and-quality-assessment?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/AK4Prompts"><b>Select prompt based on image aesthetics assessment【基于美学评估的提示词筛选】</b></a></td>
<td><b>IJCAI 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/AK4Prompts?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/mRobotit/M2Beats"><b>Motion rhythm synchronization with beats【动作与韵律对齐】</b></a></td>
<td><b>IJCAI 2024</b></td>
<td><b>Code, Dataset</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/mRobotit/M2Beats?style=flat-square&labelColor=343b41"/></td>
</tr>
<tr>
<td><a href="https://github.com/woshidandan/Champion-Solution-for-CVPR-NTIRE-2024-Quality-Assessment-on-AIGC"><b>Champion Solution for AIGC Image Quality Assessment【NTIRE AIGC图像质量评估赛道冠军】</b></a></td>
<td><b>CVPRW NTIRE 2024</b></td>
<td><b>Code</b></td>
<td><img alt="Stars" src="https://img.shields.io/github/stars/woshidandan/Champion-Solution-for-CVPR-NTIRE-2024-Quality-Assessment-on-AIGC?style=flat-square&labelColor=343b41"/></td>
</tr>
</tbody>
</table>
================================================
FILE: code/AVA/SRCC_758_LCC_765.pth
================================================
[File too large to display: 53.3 MB]
================================================
FILE: code/AVA/config.yml
================================================
authorName: default
experimentName: HyperNet_NNI_Come_on
trialConcurrency: 1
maxExecDuration: 1000h
maxTrialNum: 200
#choice: local, remote, pai
trainingServicePlatform: local
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
tuner:
#choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
#SMAC (SMAC should be installed through nnictl)
builtinTunerName: TPE
classArgs:
#choice: maximize, minimize
optimize_mode: maximize
trial:
command: python3 train_nni.py
codeDir: .
gpuNum: 1
localConfig:
useActiveGpu: true
================================================
FILE: code/AVA/dataset/test.csv
================================================
image_id,score2,score3,score4,score5,score6,score7,score8,score9,score10,score11,tag1,tag2,tag3
687050,1,5,6,51,117,78,29,10,6,1,12,30,873
167329,0,0,3,26,90,75,19,10,7,1,25,0,329
172297,7,16,48,99,119,39,19,9,1,2,0,0,335
642856,2,12,28,67,66,31,4,3,1,0,0,0,811
384757,41,37,61,59,32,8,1,2,0,0,1,22,547
837741,1,3,3,13,41,65,42,12,12,6,20,21,1135
781717,0,2,2,27,42,47,30,15,4,4,21,29,1021
36192,0,7,15,58,46,14,7,0,0,1,0,0,135
23506,44,31,45,39,26,14,11,2,2,2,1,34,103
118440,2,10,18,55,85,85,42,20,5,4,1,22,274
673555,0,0,6,24,64,37,28,7,3,1,8,9,852
55797,2,3,15,34,55,40,27,19,8,5,0,0,178
223951,1,3,8,25,86,74,37,20,6,3,0,0,372
711347,3,4,15,21,37,46,19,6,3,1,5,53,905
777366,1,1,10,22,51,27,4,1,1,1,19,27,1003
804656,0,6,5,12,55,55,19,7,3,1,0,0,1059
857003,0,0,4,5,37,52,35,2,2,0,9,21,1174
660675,2,3,4,13,37,62,40,18,11,4,15,34,836
613051,0,1,9,40,65,37,12,6,3,2,0,0,772
842708,14,17,31,81,82,31,10,3,1,1,0,0,1145
863893,0,3,1,6,29,37,46,15,5,4,17,0,1180
93846,1,7,26,58,80,52,24,10,2,4,6,29,239
623144,1,3,6,36,68,54,22,8,5,0,4,40,787
945100,3,3,8,14,41,42,32,16,3,2,1,15,1367
742806,0,1,5,6,49,57,25,11,3,2,19,0,944
861088,1,4,5,15,30,77,73,34,16,11,14,0,1182
538466,1,0,6,11,55,69,28,9,5,3,15,18,697
38341,3,5,20,52,48,19,3,1,0,0,15,22,140
197137,0,6,20,58,68,63,34,14,2,2,22,0,353
590512,1,0,2,15,40,44,10,6,1,0,15,19,738
47296,1,3,13,42,42,24,7,6,0,1,19,0,157
418936,0,0,7,13,42,75,46,24,13,8,14,15,576
501525,1,1,2,12,50,54,23,10,1,3,0,0,672
867589,6,13,31,81,133,105,41,24,8,4,3,17,1196
511920,0,1,2,8,39,75,47,35,14,3,2,32,682
895792,0,0,0,6,22,38,32,16,6,0,14,53,1237
395447,1,2,6,7,57,92,64,34,13,8,0,0,555
54227,1,10,26,36,76,42,18,9,3,1,1,28,177
428148,1,0,17,48,64,22,4,1,0,0,0,0,589
10716,2,8,33,51,65,51,19,10,1,2,14,15,57
498134,0,0,2,11,63,65,37,14,3,1,15,19,667
876039,0,1,2,34,63,26,7,3,0,1,14,0,1193
509517,1,2,5,37,73,52,28,10,4,1,19,28,677
815829,1,0,5,17,49,45,14,2,1,1,1,22,1082
102699,3,13,22,65,75,51,17,6,1,2,18,29,253
263627,1,13,43,67,72,46,8,2,3,1,41,0,413
463048,0,3,15,42,68,29,10,5,0,2,0,0,630
545934,0,0,1,11,49,31,10,1,0,1,14,17,703
146308,1,6,17,54,144,66,35,14,6,2,14,20,309
113340,3,3,28,62,81,42,6,6,2,0,10,12,267
743204,1,2,9,40,119,109,43,17,7,1,14,21,945
215024,1,9,21,57,103,62,16,14,4,0,11,27,367
429115,1,2,8,43,62,33,12,5,0,0,14,0,589
861354,2,7,12,27,72,68,43,17,7,2,0,0,1182
911340,1,9,9,19,39,44,44,25,14,13,0,0,1274
521637,1,6,12,49,50,28,10,1,1,1,12,14,686
8728,1,2,18,28,53,61,52,37,15,9,2,25,43
67795,0,0,3,12,46,51,34,23,15,5,15,38,195
239337,0,2,6,21,69,50,28,4,3,2,15,28,385
625958,9,12,23,63,76,37,12,1,1,0,0,0,793
764468,3,12,29,72,86,29,18,4,1,1,0,0,991
629865,0,3,9,28,89,73,38,20,5,1,17,21,799
221971,3,2,32,48,110,92,64,32,5,9,5,40,371
450216,1,1,10,49,110,66,27,16,5,0,1,12,613
468662,0,0,1,21,46,64,42,22,8,2,13,0,634
564411,1,4,12,36,54,14,1,0,0,0,17,50,707
523428,1,0,7,39,73,36,13,4,1,1,6,23,687
881927,0,2,5,21,56,23,11,4,2,0,22,0,1211
593488,0,0,0,8,40,44,16,3,3,0,18,21,738
914012,1,3,11,28,68,50,22,11,3,1,0,0,1282
942067,1,3,3,14,71,73,43,11,4,1,57,0,1361
643615,5,7,12,36,73,60,24,11,11,1,19,0,816
118144,0,6,14,51,67,32,10,3,1,0,17,21,275
508284,4,5,12,69,98,40,18,5,2,0,18,45,678
898267,5,8,18,42,64,68,28,14,8,3,34,65,1241
464912,0,0,4,6,38,85,50,37,13,6,17,29,629
743253,0,1,4,22,60,46,19,5,0,0,14,0,944
315472,1,1,19,57,67,20,6,2,2,0,22,38,472
457456,2,6,6,33,51,61,40,22,4,6,16,42,621
401426,2,16,24,49,101,50,15,6,1,1,15,38,560
879,8,11,19,35,39,40,29,11,1,5,0,0,11
607119,0,4,9,33,40,16,6,3,0,1,0,0,751
771685,0,2,22,29,129,164,89,40,7,4,22,38,1005
778010,3,6,14,22,47,33,22,12,4,1,0,0,1014
483128,0,1,2,16,67,68,35,6,3,1,0,0,650
746061,1,1,5,19,49,45,43,25,7,1,21,0,962
496995,1,10,24,55,65,59,24,11,7,3,14,27,668
792071,1,5,9,19,51,47,26,12,4,3,27,39,1038
179098,9,23,46,104,88,28,7,2,0,7,5,40,341
9644,6,6,9,15,16,15,11,2,6,1,15,19,47
263373,0,6,20,51,71,36,8,6,2,1,18,0,414
740278,1,3,14,25,62,37,18,4,5,0,0,0,951
20280,0,1,4,6,19,34,25,25,11,3,14,20,94
244260,4,4,13,28,45,68,44,35,26,22,22,31,392
60322,3,12,52,101,64,26,8,6,1,2,18,38,190
608975,6,10,15,49,75,37,10,2,2,3,0,0,765
882273,0,1,3,23,46,32,12,6,2,1,31,35,1211
524880,1,1,1,3,26,59,55,19,2,2,14,28,679
471246,0,3,5,38,69,37,6,4,0,1,14,15,638
55460,0,3,9,37,64,53,34,22,9,11,2,20,179
794839,0,3,2,11,49,33,29,8,0,0,6,20,1032
130844,0,5,15,43,74,82,32,13,6,4,18,0,292
78448,0,1,6,16,44,42,24,15,7,3,5,15,221
803130,0,0,2,8,26,49,24,10,6,0,21,0,1045
633951,2,10,12,31,81,60,37,15,3,3,5,18,802
156043,8,10,31,75,111,52,18,9,3,0,8,16,318
824356,4,2,5,24,55,39,19,4,2,3,0,0,1099
573595,0,0,2,7,46,58,24,9,4,1,2,10,721
640042,2,4,21,44,101,40,23,5,2,2,39,0,808
400922,0,0,7,19,44,54,20,11,6,3,1,22,559
715653,1,0,1,5,39,57,33,14,7,0,0,0,901
96527,3,7,26,47,54,31,7,8,2,1,1,26,243
765852,3,3,16,42,79,30,9,7,1,1,57,0,996
54669,0,3,10,19,67,38,26,10,2,2,23,31,176
283585,5,13,34,86,89,30,9,4,1,0,31,39,438
2557,11,23,36,49,49,29,16,8,2,1,0,0,20
946257,3,5,18,28,74,21,3,3,1,0,0,0,1377
285480,2,12,29,56,72,34,17,4,1,7,38,0,440
30520,3,6,11,20,52,52,34,22,19,2,12,21,121
489006,2,1,7,15,53,49,20,5,6,3,22,29,656
772605,2,0,11,22,63,27,11,3,3,0,15,19,1006
448037,0,0,2,17,99,133,66,23,6,0,14,21,616
663843,0,3,2,10,57,43,22,16,8,3,17,36,840
337382,1,3,19,33,75,51,27,11,1,3,17,21,498
849374,0,1,3,8,22,52,64,34,18,8,10,51,1159
671388,0,0,3,17,56,46,20,11,2,0,15,22,849
856154,2,1,8,16,69,93,50,24,10,8,10,21,1172
278254,0,1,7,14,55,74,48,21,15,6,20,0,432
547906,1,0,1,14,34,57,27,15,5,1,0,0,705
442704,1,1,11,28,68,51,38,14,5,3,0,0,606
736948,2,8,11,36,58,28,10,6,0,0,5,40,948
392125,1,0,0,5,32,61,44,29,24,7,15,21,552
395397,0,4,3,21,62,79,62,47,25,7,0,0,553
689247,0,6,12,17,38,41,32,26,14,3,7,14,877
821871,18,22,30,51,42,15,3,2,1,0,0,0,1096
867701,2,7,15,30,49,31,6,2,2,0,1,15,1193
454256,0,1,8,34,94,61,30,6,2,1,0,0,620
804988,4,3,16,17,50,46,18,6,0,1,20,0,1062
558976,0,0,0,10,44,51,17,13,1,0,18,29,714
131167,2,8,29,59,91,54,20,6,3,0,0,0,292
444352,1,7,21,71,81,15,5,1,1,1,19,0,610
751403,0,0,3,9,31,57,33,18,7,2,14,27,960
425982,0,0,4,9,48,68,30,9,1,1,15,21,583
325391,1,4,17,53,97,41,24,13,2,3,15,22,484
787567,0,1,3,15,40,43,24,11,8,0,5,0,1029
943978,1,1,6,20,54,45,14,6,0,1,15,57,1356
73446,11,14,25,38,37,23,5,6,1,1,1,12,212
701451,3,3,12,26,68,51,16,8,2,2,39,0,894
497566,3,15,27,103,74,20,8,2,1,1,0,0,668
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gitextract_1n9q0h5w/
├── LICENSE
├── README.md
├── README_CN.md
├── code/
│ ├── AVA/
│ │ ├── SRCC_758_LCC_765.pth
│ │ ├── config.yml
│ │ ├── dataset/
│ │ │ ├── test.csv
│ │ │ ├── train.csv
│ │ │ └── val.csv
│ │ ├── dataset.py
│ │ ├── option.py
│ │ ├── search_space.json
│ │ ├── train_nni.py
│ │ └── util.py
│ └── TAD66K/
│ ├── SRCC_513_LCC_531_MSE_016.pth
│ ├── config.yml
│ ├── dataset.py
│ ├── option.py
│ ├── search_space.json
│ ├── train_nni.py
│ └── util.py
├── docs/
│ └── a
└── improved_work/
└── README.md
SYMBOL INDEX (93 symbols across 8 files)
FILE: code/AVA/dataset.py
class AVADataset (line 14) | class AVADataset(Dataset):
method __init__ (line 15) | def __init__(self, path_to_csv, images_path, if_train):
method __len__ (line 32) | def __len__(self):
method __getitem__ (line 35) | def __getitem__(self, item):
FILE: code/AVA/option.py
function init (line 3) | def init():
FILE: code/AVA/train_nni.py
function adjust_learning_rate (line 25) | def adjust_learning_rate(params, optimizer, epoch):
function conv_bn (line 32) | def conv_bn(inp, oup, stride):
function conv_1x1_bn (line 39) | def conv_1x1_bn(inp, oup):
class InvertedResidual (line 46) | class InvertedResidual(nn.Module):
method __init__ (line 47) | def __init__(self, inp, oup, stride, expand_ratio):
method forward (line 68) | def forward(self, x):
class MobileNetV2 (line 74) | class MobileNetV2(nn.Module):
method __init__ (line 75) | def __init__(self, n_class=1000, input_size=224, width_mult=1.):
method forward (line 120) | def forward(self, x):
method _initialize_weights (line 127) | def _initialize_weights(self):
function resnet365_backbone (line 142) | def resnet365_backbone():
function mobile_net_v2 (line 153) | def mobile_net_v2(pretrained=False):
function Attention (line 162) | def Attention(x):
function MV2 (line 175) | def MV2():
class L5 (line 181) | class L5(nn.Module):
method __init__ (line 182) | def __init__(self):
method forward (line 193) | def forward(self, x):
class L1 (line 199) | class L1(nn.Module):
method __init__ (line 201) | def __init__(self):
method forward (line 210) | def forward(self, x):
class TargetNet (line 219) | class TargetNet(nn.Module):
method __init__ (line 220) | def __init__(self):
method forward (line 236) | def forward(self, x, paras):
class TargetFC (line 251) | class TargetFC(nn.Module):
method __init__ (line 252) | def __init__(self, weight, bias):
method forward (line 257) | def forward(self, input_):
class TANet (line 261) | class TANet(nn.Module):
method __init__ (line 262) | def __init__(self):
method forward (line 292) | def forward(self, x):
function get_score (line 312) | def get_score(opt, y_pred):
function create_data_part (line 323) | def create_data_part(opt):
function train (line 338) | def train(opt, model, loader, optimizer, criterion, writer=None, global_...
function validate (line 360) | def validate(opt,model, loader, criterion, writer=None, global_step=None...
function start_train (line 390) | def start_train(opt):
FILE: code/AVA/util.py
function download_file (line 7) | def download_file(url, local_filename, chunk_size=1024):
class AverageMeter (line 17) | class AverageMeter(object):
method __init__ (line 18) | def __init__(self):
method reset (line 20) | def reset(self):
method update (line 26) | def update(self, val, n=1):
class EDMLoss (line 32) | class EDMLoss(nn.Module):
method __init__ (line 33) | def __init__(self):
method forward (line 36) | def forward(self, p_target, p_estimate):
FILE: code/TAD66K/dataset.py
class AVADataset (line 14) | class AVADataset(Dataset):
method __init__ (line 15) | def __init__(self, path_to_csv, images_path,if_train):
method __len__ (line 32) | def __len__(self):
method __getitem__ (line 35) | def __getitem__(self, item):
FILE: code/TAD66K/option.py
function init (line 3) | def init():
FILE: code/TAD66K/train_nni.py
function adjust_learning_rate (line 26) | def adjust_learning_rate(params, optimizer, epoch):
function conv_bn (line 33) | def conv_bn(inp, oup, stride):
function conv_1x1_bn (line 40) | def conv_1x1_bn(inp, oup):
class InvertedResidual (line 47) | class InvertedResidual(nn.Module):
method __init__ (line 48) | def __init__(self, inp, oup, stride, expand_ratio):
method forward (line 69) | def forward(self, x):
class MobileNetV2 (line 75) | class MobileNetV2(nn.Module):
method __init__ (line 76) | def __init__(self, n_class=1000, input_size=224, width_mult=1.):
method forward (line 121) | def forward(self, x):
method _initialize_weights (line 128) | def _initialize_weights(self):
function resnet365_backbone (line 143) | def resnet365_backbone():
function mobile_net_v2 (line 154) | def mobile_net_v2(pretrained=False):
function Attention (line 164) | def Attention(x):
function MV2 (line 178) | def MV2():
class L5 (line 183) | class L5(nn.Module):
method __init__ (line 184) | def __init__(self):
method forward (line 196) | def forward(self, x):
class L1 (line 202) | class L1(nn.Module):
method __init__ (line 204) | def __init__(self):
method forward (line 215) | def forward(self, x):
class TargetNet (line 224) | class TargetNet(nn.Module):
method __init__ (line 226) | def __init__(self):
method forward (line 242) | def forward(self, x, paras):
class TargetFC (line 255) | class TargetFC(nn.Module):
method __init__ (line 256) | def __init__(self, weight, bias):
method forward (line 261) | def forward(self, input_):
class TANet (line 265) | class TANet(nn.Module):
method __init__ (line 266) | def __init__(self):
method forward (line 296) | def forward(self, x):
function create_data_part (line 319) | def create_data_part(opt):
function train (line 330) | def train(opt, model, loader, optimizer, criterion, writer=None, global_...
function validate (line 351) | def validate(opt,model, loader, criterion, writer=None, global_step=None...
function start_train (line 381) | def start_train(opt):
FILE: code/TAD66K/util.py
function download_file (line 7) | def download_file(url, local_filename, chunk_size=1024):
class AverageMeter (line 17) | class AverageMeter(object):
method __init__ (line 18) | def __init__(self):
method reset (line 20) | def reset(self):
method update (line 26) | def update(self, val, n=1):
class EDMLoss (line 32) | class EDMLoss(nn.Module):
method __init__ (line 33) | def __init__(self):
method forward (line 36) | def forward(self, p_target, p_estimate):
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Condensed preview — 22 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (10,894K chars).
[
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 10165,
"preview": "[](https://opensource.org/licenses/Apache-2.0)\n[](https://opensource.org/licenses/Apache-2.0)\n[!["
},
{
"path": "code/AVA/config.yml",
"chars": 580,
"preview": "authorName: default\nexperimentName: HyperNet_NNI_Come_on\ntrialConcurrency: 1\nmaxExecDuration: 1000h\nmaxTrialNum: 200\n#ch"
},
{
"path": "code/AVA/dataset/test.csv",
"chars": 528032,
"preview": "image_id,score2,score3,score4,score5,score6,score7,score8,score9,score10,score11,tag1,tag2,tag3\n687050,1,5,6,51,117,78,2"
},
{
"path": "code/AVA/dataset/train.csv",
"chars": 9503276,
"preview": "image_id,score2,score3,score4,score5,score6,score7,score8,score9,score10,score11,tag1,tag2,tag3\n770316,3,11,30,93,174,94"
},
{
"path": "code/AVA/dataset/val.csv",
"chars": 528254,
"preview": "image_id,score2,score3,score4,score5,score6,score7,score8,score9,score10,score11,tag1,tag2,tag3\n591979,0,1,2,13,45,44,16"
},
{
"path": "code/AVA/dataset.py",
"chars": 1570,
"preview": "import os\nfrom torchvision import transforms\nimport pandas as pd\nimport numpy as np\nfrom torch.utils.data import Dataset"
},
{
"path": "code/AVA/option.py",
"chars": 1790,
"preview": "import argparse\n\ndef init():\n parser = argparse.ArgumentParser(description=\"PyTorch\")\n parser.add_argument('--path"
},
{
"path": "code/AVA/search_space.json",
"chars": 565,
"preview": "{\n \"batch_size\": {\"_type\":\"choice\", \"_value\": [40, 48, 56, 64, 72]},\n \"init_lr_res365_last\":{\"_type\":\"choice\",\"_va"
},
{
"path": "code/AVA/train_nni.py",
"chars": 15537,
"preview": "from __future__ import print_function, division\nimport os\nimport torch\nimport numpy as np\nimport math\nimport torch.optim"
},
{
"path": "code/AVA/util.py",
"chars": 1311,
"preview": "import os\nimport requests\nimport torch\nimport torch.nn as nn\nGl_z = torch.ones(64,10)\n\ndef download_file(url, local_file"
},
{
"path": "code/TAD66K/config.yml",
"chars": 580,
"preview": "authorName: default\nexperimentName: HyperNet_NNI_Come_on\ntrialConcurrency: 1\nmaxExecDuration: 1000h\nmaxTrialNum: 200\n#ch"
},
{
"path": "code/TAD66K/dataset.py",
"chars": 1388,
"preview": "import os\nfrom torchvision import transforms\nimport pandas as pd\nimport numpy as np\nfrom torch.utils.data import Dataset"
},
{
"path": "code/TAD66K/option.py",
"chars": 1798,
"preview": "import argparse\n\ndef init():\n parser = argparse.ArgumentParser(description=\"PyTorch\")\n parser.add_argument('--path"
},
{
"path": "code/TAD66K/search_space.json",
"chars": 558,
"preview": "{\n \"batch_size\": {\"_type\":\"choice\", \"_value\": [40]},\n \"init_lr_res365_last\":{\"_type\":\"choice\",\"_value\":[0.000001]}"
},
{
"path": "code/TAD66K/train_nni.py",
"chars": 14345,
"preview": "from __future__ import print_function, division\nimport os\nimport torch\nimport numpy as np\nimport math\nimport option\nimpo"
},
{
"path": "code/TAD66K/util.py",
"chars": 1311,
"preview": "import os\nimport requests\nimport torch\nimport torch.nn as nn\nGl_z = torch.ones(64,10)\n\ndef download_file(url, local_file"
},
{
"path": "docs/a",
"chars": 1,
"preview": "\n"
},
{
"path": "improved_work/README.md",
"chars": 71,
"preview": "We will soon release the demo of the improved version of our work here."
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the woshidandan/TANet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 22 files (116.7 MB), approximately 2.7M tokens, and a symbol index with 93 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.