{"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/tiny-ssd-a-tiny-single-shot-detection-deep","title":"Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection","arxiv_id":"1802.06488","date":"2018-02-19","proceeding":null,"authors":["Alexander Wong","Mohammad Javad Shafiee","Francis Li","Brendan Chwyl"],"abstract":"Object detection is a major challenge in computer vision, involving both\nobject classification and object localization within a scene. While deep neural\nnetworks have been shown in recent years to yield very powerful techniques for\ntackling the challenge of object detection, one of the biggest challenges with\nenabling such object detection networks for widespread deployment on embedded\ndevices is high computational and memory requirements. Recently, there has been\nan increasing focus in exploring small deep neural network architectures for\nobject detection that are more suitable for embedded devices, such as Tiny YOLO\nand SqueezeDet. Inspired by the efficiency of the Fire microarchitecture\nintroduced in SqueezeNet and the object detection performance of the\nsingle-shot detection macroarchitecture introduced in SSD, this paper\nintroduces Tiny SSD, a single-shot detection deep convolutional neural network\nfor real-time embedded object detection that is composed of a highly optimized,\nnon-uniform Fire sub-network stack and a non-uniform sub-network stack of\nhighly optimized SSD-based auxiliary convolutional feature layers designed\nspecifically to minimize model size while maintaining object detection\nperformance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller\nthan Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher\nthan Tiny YOLO). These experimental results show that very small deep neural\nnetwork architectures can be designed for real-time object detection that are\nwell-suited for embedded scenarios.","url_abs":"http://arxiv.org/abs/1802.06488v1","url_pdf":"http://arxiv.org/pdf/1802.06488v1.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":"tiny-ssd-a-tiny-single-shot-detection-deep","repo_url":"https://github.com/lampsonSong/tinySSD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"fire-module","method_name":"Fire Module"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"ssd","method_name":"SSD"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"squeezenet","method_name":"SqueezeNet"},{"method_slug":"xavier-initialization","method_name":"Xavier Initialization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}