A New Efficient and Lightweight Convolutional Neural Network Model

Abstract

Abstract: Aiming at the problem that the current food recognition system has a large number of network model parameters and a large model, this paper proposes a 23-layer network model with only 204k parameters. The basic building blocks (7×7, 5×5, 3×3) are used to generate feature maps, and two pooling layers of different receptive fields are used to fuse the feature map of the convolutional layer, and a 1×1 convolution kernel is used for nonlinear combination. Then it is connected to the spatial pyramid pooling layer, and finally is classified in the softmax classifier. Experiments on public data sets show that, compared with ResNet50 and GoogLeNet, the network model in this paper reduces model parameters by 99.14% and 96.63% respectively without reducing classification performance.

Key words: convolutional neural network, deep learning, food classification, spatial pyramid pooling

ZHANG Jian-jian. A New Efficient and Lightweight Convolutional Neural Network Model[J]. Computer and Modernization, 2021, 0(04): 98-103.

References

［1］ KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［C］// Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012:1097-1105.
［2］ SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
［3］ HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
［4］ SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［J］. arXiv preprint arXiv:1409.1556, 2014.
［5］ KONG F Y, TAN J D. DietCam: Regular shape food recognition with a camera phone［C］// Proceedings of the 2011 International Conference on Body Sensor Networks. 2011:127-132.
［6］ KAWANO Y, YANAI K. Real-time mobile food recognition system［C］// Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2013:1-7.
［7］ ANTHIMOPOULOS M, GIANOLA L, SCARNATO L, et al. A food recognition system for diabetic patients based on an optimized bag-of-features model［J］. IEEE Journal of Biomedical and Health Informatics, 2014,18(4):1261-1271.
［8］ ANTHIMOPOULOS M, DEHAIS J, DIEM P, et al. Segmentation and recognition of multi-food meal images for carbohydrate counting［C］// Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering. 2013. DOI: 10.1109/BIBE.2013.6701608.
［9］ MATSUDA Y, YANAI K. Multiple-food recognition considering co-occurrence employing manifold ranking［C］// Proceedings of the 2012 21st International Conference on Pattern Recognition. 2012:2017-2020.
［10］HE H S, KONG F Y, TAN J D. DietCam: Multiview food recognition using a multikernel SVM［J］. IEEE Journal of Biomedical and Health Informatics, 2016,20(3):848-855.
［11］DUAN P C, WANG W S, ZHANG W S, et al. Food image recognition using pervasive cloud computing［C］// Proceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. 2013:1631-1637.
［12］ZHANG W S, ZHAO D H, GONG W J, et al. Food image recognition with convolutional neural networks［C］// Proceedings of the 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). 2015:690-693.
［13］WANG X, KUMAR D, THOME N, et al. Recipe recognition with large multimodal food dataset［C］// Proceedings of the 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). 2015. DOI: 10.1109/ICMEW.2015.7169757.
［14］KAWANO Y, SATO T, MARUYAMA T, et al. ［Demo paper］ Mirurecipe: A mobile cooking recipe recommendation system with food ingredient recognition［C］// Proceedings of the 2013 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). 2013. DOI: 10.1109/ICMEW.2013.6618222.
［15］XU R H, HERRANZ L, JIANG S Q, et al. Geolocalized modeling for dish recognition［J］. IEEE Transactions on Multimedia, 2015,17(8):1187-1199.
［16］HASSANNEJAD H, MATRELLA G, CIAMPOLINI P, et al. Food image recognition using very deep convolutional networks［C］// Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management. 2016:41-49.
［17］SERMANET P, LECUN Y. Traffic sign recognition with multi-scale convolutional networks［C］// Proceedings of the 2011 International Joint Conference on Neural Networks. 2011:2809-2813.
［18］MAAS A L, HANNUN A Y, NG A Y. Rectifier nonlinearities improve neural network acoustic models［C］// Proceedings of the 2013 ICML Workshop on Deep Learning for Audio, Speech and Language Processing. 2013.
［19］HE K M, ZHANG X Y, REN S Q, et al. Delving deep into rectifiers: Surpassing human-level performance on imageNet classification［C］// Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). 2015:1026-1034.
［20］HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9):1904-1916.
［21］IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift［C］// Proceedings of the 32nd International Conference on Machine Learning. 2015:448-456.
［22］CLEVERT D, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units (ELUs)［J］. arXiv preprint arXiv:1511.07289, 2015.
［23］BOSSARD L, GUILLAUMIN M, VAN GOOL L. Food-101: Mining discriminative components with random forests［C］// Proceedings of the 2014 European Conference on Computer Vision. 2014:446-461.
［24］LIU Z, YAN W Q, YANG M L. Image denoising based on a CNN model［C］// Proceedings of the 2018 4th International Conference on Control, Automation and Robotics (ICCAR). 2018:389-393.
［25］DOSOVITSKIY A, BROX T. Inverting visual representations with convolutional networks［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:4829-4837.
［26］MAHENDRAN A, VEDALDI A. Understanding deep image representations by inverting them［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015:5188-5196.
［27］KAWANO Y, YANAI K. Automatic expansion of a food image dataset leveraging existing categories with domain adaptation［C］// Proceedings of the 2014 European Conference on Computer Vision. 2014:3-17.

[1]	LI Jingyang1, XUE Huajian2, 3, YANG Yong1, REN Ge1. Intelligent Proctoring System Based on Edge Computing [J]. Computer and Modernization, 2025, 0(12): 26-31.
[2]	LIU Jianlong1, CEN Ying2, XU Bin3, JIAO Xuan4. Multi-view IM-NET for Fine Reconstruction of 3D Object [J]. Computer and Modernization, 2025, 0(12): 46-53.
[3]	WU Jiahong. Contrastive Learning Method for Detecting Malicious Encrypted Traffic [J]. Computer and Modernization, 2025, 0(12): 61-65.
[4]	TANG Hui1, LIU Xin2, HU Biwei1, LIU Fan1. KD-NeRF: Knowledge Distillation-enhanced Neural Radiance Field Reconstruction Method for Fruit Trees [J]. Computer and Modernization, 2025, 0(12): 66-73.
[5]	LIU Ziyang, JIA Huizhen, WANG Tonghan. No-reference Image Quality Assessment Based on DenseNet and Meta-learning [J]. Computer and Modernization, 2025, 0(12): 81-87.
[6]	LUO Guancong1, FENG Yue1, XU Hong2, QING Chuanbo1, LI Fufeng3, QIAN Peng3, LIU Huilin3. Facial Color Diagnosis Classification Method Optimized by Meta-learning [J]. Computer and Modernization, 2025, 0(12): 115-122.
[7]	WANG Jingpeng, CUI Yuyong, CAI Changlin, HE Ming’ao, LI Yinghao, TANG Zhonghe. Improved SOD Algorithm with Cross Modal Interaction and Multi Scale Aggregation [J]. Computer and Modernization, 2025, 0(11): 71-79.
[8]	HUANG Shanshan, LUO Wang, HAO Yunhe. Power Vision System Based on Knowledge Distillation Technology [J]. Computer and Modernization, 2025, 0(10): 20-24.
[9]	LI Xue, WEI Yan, LI Linjun. Hybrid Model for Electricity Price Prediction Based on DWT-SCINet-MDSC [J]. Computer and Modernization, 2025, 0(10): 37-43.
[10]	YAO Minjia1, SONG Wenai1, SUN Xue2, WANG Ziyu3, LEI Yi4, WANG Qing4, ZHAO Li5. InstantMesh: Three-Dimensional Reconstruction Method for Early Gastric#br# Cancer Images [J]. Computer and Modernization, 2025, 0(10): 51-56.
[11]	LI Xiongqing1, 2, PENG Mingtian1, 2, LI Yong1, 2, WANG Junfei1, 2, LIU Dezhi1, 3, BIAN Yuxuan1, 3, CHAI Yuelin1, 3, LIU Yuntao1, 3. Survey on Bundle Recommendation Algorithms [J]. Computer and Modernization, 2025, 0(09): 1-13.
[12]	ZHANG Jingying1, GENG Lin2, LIU Ningzhong2. Low-data Fine-grained Image Classification Based on Self-distillation and Self-attention Enhancement [J]. Computer and Modernization, 2025, 0(09): 27-34.
[13]	LIU Cheng, FENG Guang. CNN-BiLSTM and LightGBM Stock Prediction Based on Dual Attention Mechanism [J]. Computer and Modernization, 2025, 0(09): 97-103.
[14]	WEI Huimin1, 2, ZHOU Jiake1, 2, WEN Yongjun1, 2, TANG Lijun1, 2. Chinese Entity Relation Joint Extraction Method Based on Deep Learning [J]. Computer and Modernization, 2025, 0(08): 10-15.
[15]	LI Jia. Classification and Diagnosis of Breast Cancer Histopathological Images Based on Transfer Learning [J]. Computer and Modernization, 2025, 0(08): 89-96.