{"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/efficient-teacher-semi-supervised-object","title":"Efficient Teacher: Semi-Supervised Object Detection for YOLOv5","arxiv_id":"2302.07577","date":"2023-02-15","proceeding":null,"authors":["Bowen Xu","Mingtao Chen","Wenlong Guan","Lulu Hu"],"abstract":"Semi-Supervised Object Detection (SSOD) has been successful in improving the performance of both R-CNN series and anchor-free detectors. However, one-stage anchor-based detectors lack the structure to generate high-quality or flexible pseudo labels, leading to serious inconsistency problems in SSOD. In this paper, we propose the Efficient Teacher framework for scalable and effective one-stage anchor-based SSOD training, consisting of Dense Detector, Pseudo Label Assigner, and Epoch Adaptor. Dense Detector is a baseline model that extends RetinaNet with dense sampling techniques inspired by YOLOv5. The Efficient Teacher framework introduces a novel pseudo label assignment mechanism, named Pseudo Label Assigner, which makes more refined use of pseudo labels from Dense Detector. Epoch Adaptor is a method that enables a stable and efficient end-to-end semi-supervised training schedule for Dense Detector. The Pseudo Label Assigner prevents the occurrence of bias caused by a large number of low-quality pseudo labels that may interfere with the Dense Detector during the student-teacher mutual learning mechanism, and the Epoch Adaptor utilizes domain and distribution adaptation to allow Dense Detector to learn globally distributed consistent features, making the training independent of the proportion of labeled data. Our experiments show that the Efficient Teacher framework achieves state-of-the-art results on VOC, COCO-standard, and COCO-additional using fewer FLOPs than previous methods. To the best of our knowledge, this is the first attempt to apply Semi-Supervised Object Detection to YOLOv5.Code is available: https://github.com/AlibabaResearch/efficientteacher","url_abs":"https://arxiv.org/abs/2302.07577v3","url_pdf":"https://arxiv.org/pdf/2302.07577v3.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":"efficient-teacher-semi-supervised-object","repo_url":"https://github.com/AlibabaResearch/efficientteacher","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"efficient-teacher-semi-supervised-object","repo_url":"https://github.com/Mind23-2/MindCode-128","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"efficient-teacher-semi-supervised-object","repo_url":"https://github.com/Mind23-2/MindCode-93","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"efficient-teacher-semi-supervised-object","repo_url":"https://github.com/modelscope/modelscope","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"semi-supervised-object-detection","task_name":"Semi-Supervised Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"focal-loss","method_name":"Focal Loss"},{"method_slug":"retinanet","method_name":"RetinaNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-1","task":"Semi-Supervised Object Detection","dataset":"COCO 1% labeled data","model":"Efficient Teacher","rank_in_archive_order":10,"of":22,"metrics":{"mAP":"23.76"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-10","task":"Semi-Supervised Object Detection","dataset":"COCO 10% labeled data","model":"Efficient Teacher","rank_in_archive_order":5,"of":27,"metrics":{"detector":"YOLOv5-L","mAP":"37.9"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-2","task":"Semi-Supervised Object Detection","dataset":"COCO 2% labeled data","model":"Efficient Teacher","rank_in_archive_order":5,"of":19,"metrics":{"mAP":"28.7"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-object-detection-on-coco-5","task":"Semi-Supervised Object Detection","dataset":"COCO 5% labeled data","model":"Efficient Teacher","rank_in_archive_order":4,"of":23,"metrics":{"mAP":"34.11"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.07577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07577"}},"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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