EvalGIM: A Library for Evaluating Generative Image Models Article Swipe
Melissa Hall
,
Oscar Mañas
,
Reyhane Askari
,
Mark Ibrahim
,
Candace Ross
,
Pietro Astolfi
,
Tariq Berrada Ifriqi
,
Marton Havasi
,
Yohann Benchetrit
,
Karen Ullrich
,
Carolina Braga
,
Abhishek Charnalia
,
Maeve Ryan
,
Mike Rabbat
,
Michal Drozdzal
,
Jakob Verbeek
,
Adriana Romero
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.10604
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.10604
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framework for performing evaluations across many datasets and metrics. Furthermore, the rapid introduction of increasingly robust benchmarking methods requires that evaluation libraries remain flexible to new datasets and metrics. Finally, there remains a gap in synthesizing evaluations in order to deliver actionable takeaways about model performance. To enable unified, flexible, and actionable evaluations, we introduce EvalGIM (pronounced ''EvalGym''), a library for evaluating generative image models. EvalGIM contains broad support for datasets and metrics used to measure quality, diversity, and consistency of text-to-image generative models. In addition, EvalGIM is designed with flexibility for user customization as a top priority and contains a structure that allows plug-and-play additions of new datasets and metrics. To enable actionable evaluation insights, we introduce ''Evaluation Exercises'' that highlight takeaways for specific evaluation questions. The Evaluation Exercises contain easy-to-use and reproducible implementations of two state-of-the-art evaluation methods of text-to-image generative models: consistency-diversity-realism Pareto Fronts and disaggregated measurements of performance disparities across groups. EvalGIM also contains Evaluation Exercises that introduce two new analysis methods for text-to-image generative models: robustness analyses of model rankings and balanced evaluations across different prompt styles. We encourage text-to-image model exploration with EvalGIM and invite contributions at https://github.com/facebookresearch/EvalGIM/.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.10604
- https://arxiv.org/pdf/2412.10604
- OA Status
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4405468241Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.10604Digital Object Identifier
- Title
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EvalGIM: A Library for Evaluating Generative Image ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-13Full publication date if available
- Authors
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Melissa Hall, Oscar Mañas, Reyhane Askari, Mark Ibrahim, Candace Ross, Pietro Astolfi, Tariq Berrada Ifriqi, Marton Havasi, Yohann Benchetrit, Karen Ullrich, Carolina Braga, Abhishek Charnalia, Maeve Ryan, Mike Rabbat, Michal Drozdzal, Jakob Verbeek, Adriana RomeroList of authors in order
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https://arxiv.org/abs/2412.10604Publisher landing page
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https://arxiv.org/pdf/2412.10604Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2412.10604Direct OA link when available
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Generative grammar, Image (mathematics), Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.takeaways | 77, 157 |
| abstract_inverted_index.Evaluation | 163, 193 |
| abstract_inverted_index.actionable | 76, 86, 148 |
| abstract_inverted_index.diversity, | 112 |
| abstract_inverted_index.evaluating | 96 |
| abstract_inverted_index.evaluation | 55, 149, 160, 173 |
| abstract_inverted_index.generative | 5, 97, 117, 177, 203 |
| abstract_inverted_index.increases, | 7 |
| abstract_inverted_index.performing | 37 |
| abstract_inverted_index.questions. | 161 |
| abstract_inverted_index.robustness | 205 |
| abstract_inverted_index.(pronounced | 91 |
| abstract_inverted_index.Exercises'' | 154 |
| abstract_inverted_index.consistency | 114 |
| abstract_inverted_index.disparities | 187 |
| abstract_inverted_index.easy-to-use | 166 |
| abstract_inverted_index.evaluation. | 19 |
| abstract_inverted_index.evaluations | 38, 71, 212 |
| abstract_inverted_index.exploration | 221 |
| abstract_inverted_index.flexibility | 125 |
| abstract_inverted_index.performance | 186 |
| abstract_inverted_index.''Evaluation | 153 |
| abstract_inverted_index.Furthermore, | 44 |
| abstract_inverted_index.benchmarking | 14, 30, 51 |
| abstract_inverted_index.evaluations, | 87 |
| abstract_inverted_index.increasingly | 49 |
| abstract_inverted_index.introduction | 47 |
| abstract_inverted_index.measurements | 184 |
| abstract_inverted_index.performance. | 80 |
| abstract_inverted_index.reproducible | 168 |
| abstract_inverted_index.synthesizing | 70 |
| abstract_inverted_index.''EvalGym''), | 92 |
| abstract_inverted_index.contributions | 226 |
| abstract_inverted_index.customization | 128 |
| abstract_inverted_index.disaggregated | 183 |
| abstract_inverted_index.plug-and-play | 139 |
| abstract_inverted_index.text-to-image | 4, 116, 176, 202, 219 |
| abstract_inverted_index.implementations | 169 |
| abstract_inverted_index.state-of-the-art | 172 |
| abstract_inverted_index.consistency-diversity-realism | 179 |
| abstract_inverted_index.https://github.com/facebookresearch/EvalGIM/. | 228 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 17 |
| citation_normalized_percentile |