{"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/the-fishyscapes-benchmark-measuring-blind","title":"The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation","arxiv_id":"1904.03215","date":"2019-04-05","proceeding":null,"authors":["Hermann Blum","Paul-Edouard Sarlin","Juan Nieto","Roland Siegwart","Cesar Cadena"],"abstract":"Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle. We~adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art.","url_abs":"https://arxiv.org/abs/1904.03215v4","url_pdf":"https://arxiv.org/pdf/1904.03215v4.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":"the-fishyscapes-benchmark-measuring-blind","repo_url":"https://github.com/hermannsblum/fishyscapes","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"fishyscapes","name":"Fishyscapes","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"Dirichlet DeepLab","rank_in_archive_order":13,"of":18,"metrics":{"AP":"34.28","FPR95":"47.43"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"Void Classifier","rank_in_archive_order":15,"of":18,"metrics":{"AP":"10.29","FPR95":"22.11"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"Bayesian DeepLab","rank_in_archive_order":16,"of":18,"metrics":{"AP":"9.8","FPR95":"38.5"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"Learned Embedding Density","rank_in_archive_order":17,"of":18,"metrics":{"AP":"4.7","FPR95":"24.4"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"Softmax Entropy","rank_in_archive_order":18,"of":18,"metrics":{"AP":"2.9","FPR95":"44.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03215","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}