{"url":"/method/mobiledet","slug":"mobiledet","name":"MobileDet","full_name":"MobileDet","full_name_withheld":false,"description_markdown":"**MobileDet** is an object detection model developed for mobile accelerators. MobileDets uses regular convolutions extensively on EdgeTPUs and DSPs, especially in the early stage of the network where depthwise convolutions tend to be less efficient.  This helps boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, an efficient family of object detection models is obtained.","description_state":"present","introduced_year":null,"introduced_by":{"title":"MobileDets: Searching for Object Detection Architectures for Mobile Accelerators","paper":"/paper/mobiledets-searching-for-object-detection","first_author":"Yunyang Xiong","n_authors":10,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/mobiledets-searching-for-object-detection"},"source":{"url":"https://arxiv.org/abs/2004.14525v3","title":"MobileDets: Searching for Object Detection Architectures for Mobile Accelerators","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Light-weight neural networks","url":"/methods/category/light-weight-neural-networks","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"Multi-Objective Evolutionary for Object Detection Mobile Architectures Search","date":"2022-11-05","arxiv_id":"2211.02791","n_code_links":0,"syntology":null},{"paper":"/paper/mobiledets-searching-for-object-detection","title":"MobileDets: Searching for Object Detection Architectures for Mobile Accelerators","date":"2020-04-30","arxiv_id":"2004.14525","n_code_links":4,"syntology":{"ran":0,"of":10,"unverified":10,"pointer_only":0}}],"papers_shown":2,"tasks":[{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":2},{"task":"/task/object","name":"Object","papers":2},{"task":"/task/object-detection","name":"Object Detection","papers":2},{"task":"/task/object-detection-1","name":"object-detection","papers":2},{"task":null,"name":"CPU","papers":1},{"task":null,"name":"GPU","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2020","papers":1},{"year":"2022","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/mobiledet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}