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Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
| Date | Stars |
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| 2026-07-24 | 255 |
| 2026-07-25 | 255 |
| 2026-07-28 | 255 |
| 2026-07-30 | 255 |
| 2026-08-06 | 255 |
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# Image Crop Analysis
[](https://colab.research.google.com/github/twitter-research/image-crop-analysis) [](https://mybinder.org/v2/gh/twitter-research/image-crop-analysis/HEAD) [](https://doi.org/10.1145/3479594) [](https://arxiv.org/abs/2105.08667)
This is a repo for the code used for reproducing our [Image Crop Analysis paper](https://arxiv.org/abs/2105.08667) as shared on [our blog post](https://blog.twitter.com/engineering/en_us/topics/insights/2021/sharing-learnings-about-our-image-cropping-algorithm.html).
If you plan to use this code please cite our paper as follows:
```
@article{TwitterImageCrop2021,
author = {Yee, Kyra and Tantipongpipat, Uthaipon and Mishra, Shubhanshu},
title = {Image Cropping on Twitter: Fairness Metrics, Their Limitations, and the Importance of Representation, Design, and Agency},
year = {2021},
issue_date = {October 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {5},
number = {CSCW2},
url = {https://doi.org/10.1145/3479594},
doi = {10.1145/3479594},
journal = {Proceedings of the ACM on Human-Computer Interaction},
month = oct,
articleno = {450},
numpages = {24},
keywords = {image cropping, ethical HCI, fairness in machine learning, demographic parity, representational harm}
}
@article{TwitterImageCrop2021ArXiv,
author = {{Yee}, Kyra and {Tantipongpipat}, Uthaipon and {Mishra}, Shubhanshu},
title = "{Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computers and Society, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning},
year = 2021,
month = may,
eid = {arXiv:2105.08667},
pages = {arXiv:2105.08667},
archivePrefix = {arXiv},
eprint = {2105.08667},
primaryClass = {cs.CY},
}
```
# Instructions
- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/products/individual) and then follow these steps:
* create a conda environment using `conda env create -f environment.yml`
* activate the environment using `conda activate image-crop-analysis`
- Put a dummy jpeg image at `data/dummy.jpeg`
- Put any additional images with `*.jpeg` extension in DATA_DIR, which is `./data`
- If you just want to investigate how the model predicts the saliency map then you can use the notebook [notebooks/Image Annotation Dash.ipynb](notebooks/Image%20Annotation%20Dash.ipynb)
- To reproduce the analysis first prepare the data using [notebooks/Data Preparation.ipynb](notebooks/Data%20Preparation.ipynb) and then run [notebooks/Demographic Bias Analysis.ipynb](notebooks/Demographic%20Bias%20Analysis.ipynb)
- To reproduce the plots first run [notebooks/Demographic Bias Plots.ipynb](notebooks/Demographic%20Bias%20Plots.ipynb)
- If you want to explore how the library behind the dashboard works see [notebooks/Image Crop Analysis.ipynb](notebooks/Image%20Crop%20Analysis.ipynb)
- If you have the dataset prepared from the above steps then you can create the gender gaze dataset by running [notebooks/Gender Gaze Analysis.ipynb](notebooks/Gender%20Gaze%20Analysis.ipynb)
## Docker Run
* Install docker
* Run the following commands in this root direcExcerpt of 5,128 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6a3069c92be3ef1e, topic:computer-vision, readme:computer vision