Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

被引:8
作者
Ignatov, Andrey [1 ,2 ]
Malivenko, Grigory
Timofte, Radu [1 ,2 ]
Chen, Sheng [3 ]
Xia, Xin [3 ]
Liu, Zhaoyan [3 ]
Zhang, Yuwei [3 ]
Zhu, Feng [3 ]
Li, Jiashi [3 ]
Xiao, Xuefeng [3 ]
Tian, Yuan [3 ]
Wu, Xinglong [3 ]
Kyrkou, Christos [4 ]
Chen, Yixin [5 ]
Zhang, Zexin [5 ]
Peng, Yunbo [5 ]
Lin, Yue [5 ]
Dutta, Saikat [6 ]
Das, Sourya Dipta [7 ]
Shah, Nisarg A. [8 ]
Kumar, Himanshu [8 ]
Ge, Chao [9 ]
Wu, Pei-Lin [9 ]
Du, Jin-Hua [9 ]
Batutin, Andrew [10 ]
Federico, Juan Pablo [10 ]
Lyda, Konrad [10 ]
Khojoyan, Levon [10 ]
Thanki, Abhishek [11 ]
Paul, Sayak [11 ]
Siddiqui, Shahid [12 ]
机构
[1] Swiss Fed Inst Technol, Comp Vis Lab, Zurich, Switzerland
[2] AI Witchlabs, Lausanne, Switzerland
[3] ByteDance Inc, Beijing, Peoples R China
[4] Univ Cyprus, KIOS Res & Innovat Ctr Excellence, Nicosia, Cyprus
[5] Netease Games AI Lab, Beijing, Peoples R China
[6] Indian Inst Technol Madras, Chennai, Tamil Nadu, India
[7] Jadavpur Univ, Kolkata, India
[8] Indian Inst Technol Jodhpur, Karwar, India
[9] Chinese Acad Sci, Inst Automat, Nanjing Artificial Intelligence Chip Res, Beijing, Peoples R China
[10] DataArt Inc, New York, NY USA
[11] PyImageSearch, Mumbai, Maharashtra, India
[12] Univ Cyprus, KIOS Ctr Excellence, Nicosia, Cyprus
来源
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021 | 2021年
关键词
D O I
10.1109/CVPRW53098.2021.00289
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.
引用
收藏
页码:2558 / 2568
页数:11
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