Compact neural network design for visual food recognition
Deep learning grows fast in recent years, and people already use it to solve many real-world problems. Convolutional neural network (CNN) is one of the essential architectures of deep learning. Compared to traditional machine learning methods, CNN can generate transformed feature maps automatically....
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140558 |
| _version_ | 1826125418357325824 |
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| author | Li, Tianlei |
| author2 | Yap Kim Hui |
| author_facet | Yap Kim Hui Li, Tianlei |
| author_sort | Li, Tianlei |
| collection | NTU |
| description | Deep learning grows fast in recent years, and people already use it to solve many real-world problems. Convolutional neural network (CNN) is one of the essential architectures of deep learning. Compared to traditional machine learning methods, CNN can generate transformed feature maps automatically. With this, the machine improves its self-learning ability remarkably.
In this thesis, we utilize CNN to perform visual food recognition. After that, our result could serve as a backbone to health monitoring smartphone apps, which can recognize a variety of foods and show their nutritional components. The problem encountered is that deep CNNs such as VGG and ResNet are too large to implement on low-end devices (e.g., smartphones). Hence, we try to find one compact architecture that has competitive accuracy with a little computational cost.
We select several state-of-the-art compact CNNs such as MobileNet, ShuffleNet, and SqueezeNet. We mainly compare their performances with a deep neural network — ResNet50, including accuracy, the number of parameters, and the floating-point operations per second (FLOPS). Finally, we choose the most suitable architecture for visual food recognition and tune it to improve the performance further. |
| first_indexed | 2024-10-01T06:35:59Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/140558 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:35:59Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1405582023-07-04T16:35:05Z Compact neural network design for visual food recognition Li, Tianlei Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Electrical and electronic engineering Deep learning grows fast in recent years, and people already use it to solve many real-world problems. Convolutional neural network (CNN) is one of the essential architectures of deep learning. Compared to traditional machine learning methods, CNN can generate transformed feature maps automatically. With this, the machine improves its self-learning ability remarkably. In this thesis, we utilize CNN to perform visual food recognition. After that, our result could serve as a backbone to health monitoring smartphone apps, which can recognize a variety of foods and show their nutritional components. The problem encountered is that deep CNNs such as VGG and ResNet are too large to implement on low-end devices (e.g., smartphones). Hence, we try to find one compact architecture that has competitive accuracy with a little computational cost. We select several state-of-the-art compact CNNs such as MobileNet, ShuffleNet, and SqueezeNet. We mainly compare their performances with a deep neural network — ResNet50, including accuracy, the number of parameters, and the floating-point operations per second (FLOPS). Finally, we choose the most suitable architecture for visual food recognition and tune it to improve the performance further. Master of Science (Signal Processing) 2020-05-30T12:46:14Z 2020-05-30T12:46:14Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/140558 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Li, Tianlei Compact neural network design for visual food recognition |
| title | Compact neural network design for visual food recognition |
| title_full | Compact neural network design for visual food recognition |
| title_fullStr | Compact neural network design for visual food recognition |
| title_full_unstemmed | Compact neural network design for visual food recognition |
| title_short | Compact neural network design for visual food recognition |
| title_sort | compact neural network design for visual food recognition |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/140558 |
| work_keys_str_mv | AT litianlei compactneuralnetworkdesignforvisualfoodrecognition |