{"url":"/method/thundernet","slug":"thundernet","name":"ThunderNet","full_name":"ThunderNet","full_name_withheld":false,"description_markdown":"**ThunderNet** is a two-stage object detection model. The design of ThunderNet aims at the computationally expensive structures in state-of-the-art two-stage detectors. The backbone utilises a [ShuffleNetV2](https://paperswithcode.com/method/shufflenet-v2) inspired network called [SNet](https://paperswithcode.com/method/snet) designed for object detection. In the detection part, ThunderNet follows the detection head design in Light-Head [R-CNN](https://paperswithcode.com/method/r-cnn), and further compresses the [RPN](https://paperswithcode.com/method/rpn) and R-CNN subnet. To eliminate the performance degradation induced by small backbones and small feature maps, ThunderNet uses two new efficient architecture blocks, [Context Enhancement Module](https://paperswithcode.com/method/context-enhancement-module) (CEM) and [Spatial Attention Module](https://paperswithcode.com/method/spatial-attention-module) (SAM). CEM combines the feature maps from multiple scales to leverage local and global context information, while SAM uses the information learned in RPN to refine the feature distribution in RoI warping.","description_state":"present","introduced_year":null,"introduced_by":{"title":"ThunderNet: Towards Real-time Generic Object Detection","paper":"/paper/thundernet-towards-real-time-generic-object","first_author":"Zheng Qin","n_authors":7,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/thundernet-towards-real-time-generic-object"},"source":{"url":"https://arxiv.org/abs/1903.11752v3","title":"ThunderNet: Towards Real-time Generic Object Detection","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/ouyanghuiyu/Thundernet_Pytorch/tree/ab66b733a39c9d1c60b5373f84f861d9627d8c20","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":null,"title":"Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality","date":"2022-07-04","arxiv_id":"2207.01296","n_code_links":0,"syntology":null},{"paper":null,"title":"Egocentric Human Segmentation for Mixed Reality","date":"2020-05-25","arxiv_id":"2005.12074","n_code_links":0,"syntology":null},{"paper":null,"title":"ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/thundernet-towards-real-time-generic-object","title":"ThunderNet: Towards Real-time Generic Object Detection","date":"2019-03-28","arxiv_id":"1903.11752","n_code_links":3,"syntology":null}],"papers_shown":4,"tasks":[{"task":"/task/mixed-reality","name":"Mixed Reality","papers":2},{"task":"/task/object-detection","name":"Object Detection","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/object-detection-1","name":"object-detection","papers":2},{"task":"/task/object","name":"Object","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2019","papers":2},{"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/thundernet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}