Online photography assistance, tailored for food photograph.
To support the research and applications that are related to image aesthetic assessment on food photos and calls for aesthetic-aware visual feature, we establish the first large-scale dataset, namely, Gourmet Photography Dataset (GPD).
For further details, please refer to our paper [pdf].
We collect food photos from both the online photo-sharing websites (e.g., Flickr, Pinterest, 500px, Pexels) and from several existing food categorization benchmarks (e.g., Food-101, [ChineseFoodNet]).
| Train | Test | Overall | |
|---|---|---|---|
| Positive | 11,791 | 1,311 | 13,102 |
| Negative | 9,809 | 1,089 | 10,898 |
| Overall | 21,600 | 2,400 | 24,000 |
For the convenience of file transfer, we resize the images first, and then upload the resized images on Google Drive:
Or you can download it on jianguoyun.com
- [Negative Partition (Resized)]
- [Positive Partition Part1 (Resized)]
- [Positive Partition Part2 (Resized)]
ACCESS TERMS
Researcher has requested permission to use the GPD.
In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
3. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
In order to support further research and make aesthetic-aware applications on food photos practical, we will enlarge the scale of GPD and enrich the information it contains (e.g., introduce some aesthetic-related attributes). Hence, your feedback means a lot to us. Let's together make GPD better, and create interesting applications based on it.
When we train a CNN model (e.g., VGG-16 or ResNet-18) on our proposed GPD dataset, we can equip AI with the ability to assess the visual aesthetic of food photos, and therefore make it possible to create several practical aesthetic-aware task scenarios in the specific domain of food photographs.
For quantitative analysis and comparisons, we conduct another generalization ability test experiment. We collect 825 food photos from WeChat, and invite 50 interviewees, who are qualified for the task of food image aesthetic assessment, and collect their responses on those 825 images. Based on their judgements, we can evaluate how the trained model perform on unseen food photos.
| Solution | V(S_{pos}) | V(S_{neg}) |
|---|---|---|
| Best | 75.5 | 83.9 |
| Random | 37.3 | 62.5 |
| Worst | 16.1 | 24.5 |
| Human (Expert) | 72.1 | 81.0 |
| ResNet-18 + AVA | 38.5 | 65.7 |
| ResNet-18 + GPD | 61.1 | 72.5 |
For further details and discussions of the table shown above, please refer to [our paper].
TODO
If you find this useful in your research, please consider citing:
@article{sheng2020learning,
title={Learning to assess visual aesthetics of food images},
author={Sheng, Kekai and Dong, Weiming and Huang, Haibin and Chai, Menglei and Zhang, Yong and Ma, Chongyang and Hu, Bao-Gang},
journal={Computational Visual Media},
pages={1--14},
year={2020},
publisher={Springer}
}
@inproceedings{sheng2018attention,
title={Attention-based multi-patch aggregation for image aesthetic assessment},
author={Sheng, Kekai and Dong, Weiming and Ma, Chongyang and Mei, Xing and Huang, Feiyue and Hu, Bao-Gang},
booktitle={2018 ACM Multimedia Conference on Multimedia Conference},
pages={879--886},
year={2018},
organization={ACM}
}
If you have any suggenstions about papers, feel free to mail me :)
- Email : shengkekai_D@163.com (personal), saulsheng@tencent.com (company)
- QQ contact : 2309310604