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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.04041 |
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| _version_ | 1866913492783595520 |
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| author | Ramachandran, Rahul Kulkarni, Tejal Sharma, Charchit Vijaykeerthy, Deepak Balasubramanian, Vineeth N |
| author_facet | Ramachandran, Rahul Kulkarni, Tejal Sharma, Charchit Vijaykeerthy, Deepak Balasubramanian, Vineeth N |
| contents | Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evaluation, it provides only a coarse assessment by considering a single model's score on all dataset items. This paper explores Item Response Theory (IRT), a framework that infers interpretable latent parameters for an ensemble of models and each dataset item, enabling richer evaluation and analysis beyond the single accuracy number. Leveraging IRT, we assess model calibration, select informative data subsets, and demonstrate the usefulness of its latent parameters for analyzing and comparing models and datasets in computer vision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Evaluation of Vision Datasets and Models using Human Competency Frameworks Ramachandran, Rahul Kulkarni, Tejal Sharma, Charchit Vijaykeerthy, Deepak Balasubramanian, Vineeth N Computer Vision and Pattern Recognition Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evaluation, it provides only a coarse assessment by considering a single model's score on all dataset items. This paper explores Item Response Theory (IRT), a framework that infers interpretable latent parameters for an ensemble of models and each dataset item, enabling richer evaluation and analysis beyond the single accuracy number. Leveraging IRT, we assess model calibration, select informative data subsets, and demonstrate the usefulness of its latent parameters for analyzing and comparing models and datasets in computer vision. |
| title | On Evaluation of Vision Datasets and Models using Human Competency Frameworks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.04041 |