DIG-In
D
DIG In
Overview :
DIG-In is a library for evaluating the quality, diversity, and consistency differences of text-to-image generation models across geographical regions. It utilizes GeoDE and DollarStreet as reference datasets and computes metrics such as accuracy, coverage, and diversity of generated images. It also employs the CLIPScore metric to measure model consistency. This library empowers researchers and developers to conduct geographical diversity audits of their image generation models, ensuring fairness and inclusivity on a global scale.
Target Users :
DIG-In is designed for researchers and developers who need to evaluate and ensure the consistent performance of their image generation models globally. It is particularly suitable for applications that focus on the fairness and inclusivity of models across different cultures and geographical backgrounds.
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Use Cases
Researchers use DIG-In to assess the output quality of different image generation models in Africa.
Developers leverage DIG-In to ensure their applications provide a consistent user experience globally.
Educational institutions utilize DIG-In as a teaching tool to educate students on evaluating and improving the fairness of AI models.
Features
Evaluate the quality difference of generated images using GeoDE and DollarStreet datasets.
Calculate accuracy, recall, coverage, and density metrics for generated images.
Evaluate image consistency using the CLIPScore metric.
Provide scripts to extract features from generated images.
Support custom image or feature path pointers.
Provide scripts for calculating metrics including balanced reference datasets.
How to Use
1. Generate images corresponding to the prompts in the csv file.
2. Provide pointers to the prompt csv and generated image folder to extract image features.
3. Calculate metrics including accuracy, recall, coverage, and density using the extracted features.
4. Update the feature file path if needed.
5. Run the script for calculating metrics including balanced reference datasets.
6. Analyze the metric results in the generated csv file to evaluate model performance.
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