{"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/roboflow-100-a-rich-multi-domain-object","title":"Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark","arxiv_id":"2211.13523","date":"2022-11-24","proceeding":null,"authors":["Floriana Ciaglia","Francesco Saverio Zuppichini","Paul Guerrie","Mark McQuade","Jacob Solawetz"],"abstract":"The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.","url_abs":"https://arxiv.org/abs/2211.13523v3","url_pdf":"https://arxiv.org/pdf/2211.13523v3.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":"roboflow-100-a-rich-multi-domain-object","repo_url":"https://github.com/roboflow-ai/roboflow-100-benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"medical-object-detection","task_name":"Medical Object Detection"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-categorization","task_name":"Object Categorization"},{"task_slug":"object-counting","task_name":"Object Counting"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-discovery-in-videos","task_name":"Object Discovery In Videos"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"small-object-detection","task_name":"Small Object Detection"},{"task_slug":"thermal-infrared-object-tracking","task_name":"Thermal Infrared Object Tracking"},{"task_slug":"video-object-detection","task_name":"Video Object Detection"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"gdit","name":"GDIT","full_name":""},{"slug":"rf100","name":"RF100","full_name":"Roboflow 100"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.13523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13523"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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