{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/si-score-an-image-dataset-for-fine-grained","title":"SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size","arxiv_id":"2104.04191","date":"2021-04-09","proceeding":null,"authors":["Jessica Yung","Rob Romijnders","Alexander Kolesnikov","Lucas Beyer","Josip Djolonga","Neil Houlsby","Sylvain Gelly","Mario Lucic","Xiaohua Zhai"],"abstract":"Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, rotation and size may affect the predictions in non-trivial ways. In this work we perform a fine-grained analysis of robustness with respect to these factors of variation using SI-Score, a synthetic dataset. In particular, we investigate ResNets, Vision Transformers and CLIP, and identify interesting qualitative differences between these.","url_abs":"https://arxiv.org/abs/2104.04191v1","url_pdf":"https://arxiv.org/pdf/2104.04191v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"si-score-an-image-dataset-for-fine-grained","repo_url":"https://github.com/google-research/si-score","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[{"slug":"si-score-1","name":"SI-Score","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2104.04191","atlas_url":"https://app.syntology.ai/?focus=2104.04191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.04191"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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