{"url":"/method/yolov1","slug":"yolov1","name":"YOLOv1","full_name":"YOLOv1","full_name_withheld":false,"description_markdown":"**YOLOv1** is a single-stage object detection model. Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. \r\n\r\nThe network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image.","description_state":"present","introduced_year":null,"introduced_by":{"title":"You Only Look Once: Unified, Real-Time Object Detection","paper":"/paper/you-only-look-once-unified-real-time-object","first_author":"Joseph Redmon","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/you-only-look-once-unified-real-time-object"},"source":{"url":"http://arxiv.org/abs/1506.02640v5","title":"You Only Look Once: Unified, Real-Time Object Detection","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/pjreddie/darknet","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"One-Stage Object Detection Models","url":"/methods/category/one-stage-object-detection-models","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":6,"archive_num_papers":6,"papers_newest_first":[{"paper":null,"title":"ODVerse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11","date":"2025-02-20","arxiv_id":"2502.14314","n_code_links":0,"syntology":null},{"paper":null,"title":"YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain","date":"2024-06-14","arxiv_id":"2406.10139","n_code_links":0,"syntology":null},{"paper":null,"title":"YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series","date":"2024-06-12","arxiv_id":"2406.19407","n_code_links":0,"syntology":null},{"paper":"/paper/machine-learning-approach-of-automatic","title":"Machine learning approach of automatic identification and counting of blood cells","date":"2019-09-05","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/light-weight-retinanet-for-object-detection","title":"Light-Weight RetinaNet for Object Detection","date":"2019-05-24","arxiv_id":"1905.10011","n_code_links":1,"syntology":null},{"paper":"/paper/you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","date":"2015-06-08","arxiv_id":"1506.02640","n_code_links":144,"syntology":{"ran":80,"of":148,"unverified":68,"pointer_only":98}}],"papers_shown":6,"tasks":[{"task":"/task/object-detection","name":"Object Detection","papers":6},{"task":"/task/object-detection-1","name":"object-detection","papers":5},{"task":"/task/object","name":"Object","papers":3},{"task":"/task/real-time-object-detection","name":"Real-Time Object Detection","papers":2},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":1},{"task":"/task/machine-learning","name":"BIG-bench Machine Learning","papers":1},{"task":"/task/blood-cell-count","name":"Blood Cell Count","papers":1},{"task":"/task/blood-cell-detection","name":"Blood Cell Detection","papers":1},{"task":"/task/cbc-test","name":"CBC TEST","papers":1},{"task":"/task/computational-efficiency","name":"Computational Efficiency","papers":1},{"task":"/task/medical-diagnosis","name":"Medical Diagnosis","papers":1},{"task":"/task/object-counting","name":"Object Counting","papers":1},{"task":"/task/survey","name":"Survey","papers":1}],"tasks_shown":13,"n_tasks":13,"usage_by_year":[{"year":"2015","papers":1},{"year":"2019","papers":2},{"year":"2024","papers":2},{"year":"2025","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/yolov1"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}