FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Pandey, Paritosh; Deepthi, Akella; Mandal, Bappaditya; Puhan, Niladri B.

doi:10.1109/lsp.2017.2758862

Cited by 106 publications

(46 citation statements)

References 26 publications

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“…Pandey et al. () developed a multilayered CNN to recognize food. Two different image databases were used, including Food‐101 and an Indian food database, between which the later had 50 categories with 100 images of each.…”

Section: Deep Learning Applications In Foodmentioning

confidence: 99%

“…CNN‐based ensemble network architecture (Pandey et al., ). The green, yellow, and blue blocks represent the features extracted by AlexNet, GoogLeNet, and ResNet, respectively.…”

Section: Deep Learning Applications In Foodmentioning

confidence: 99%

“…There are various popular CNN architectures for image processing including AlexNet (Krizhevsky et al, 2012), a network using repetitive units called visual geometry group network (VGG) (Simonyan & Zisserman, 2014), GoogLeNet (Szegedy et al, 2015) that includes parallel data channels, and residual neural network (ResNet) (He, Zhang, Ren, & Sun, 2016) constructed by residual (Heravi, Aghdam, & Puig, 2018) 65.40 87.00 CNN-FOOD 70.41 / Multitask (H. Wu, Merler, Uceda-Sosa, & Smith, 2016) 72.11 / FoodNet (Pandey, Deepthi, Mandal, & Puhan, 2017) 72.12 91.61 DeepFood (Liu et al, 2016a) 77.40 93.70 Inception Module (C. 77.00 94.00 ResNet (Fu et al, 2017) 78.50 94.10 ResNet-50 (Ciocca, Napoletano, & Schettini, 2018) 82.54 95.79 Inception-v3+FPCNN (Zheng, Zou, & Wang, 2018) 87.96 / Inception V3 (Hassannejad et al, 2016) 88.28 96.88 wide-slice residual networks (WISeR) (Martinel, Foresti, & Micheloni, 2018) 90.27 98.71…”

Section: Deep Learning Applications In Food Food Recognition and Clasmentioning

confidence: 99%

“…Liu et al (2016a) presented a network named Deep Food reached 77.40% and 93.70% for Top-1% and Top-5%, respectively. Fu, Chen, and Li (2017) obtained a better result of (Pandey et al, 2017). The green, yellow, and blue blocks represent the features extracted by AlexNet, GoogLeNet, and ResNet, respectively.…”

Section: Deep Learning Applications In Food Food Recognition and Clasmentioning

confidence: 99%

“…Experiment showed that GoogLeNet that was tuned on Food-101 and FoodCAT and treated by Super-Resolution method achieved the best results for dishes identification (Top-1% of 68.07% and Top-5 of 89.53%) and food categories recognition (Top-1% of 72.29% and Top-5% of 97.07%). Pandey et al (2017) developed a multilayered CNN to recognize food. Two different image databases were used, including Food-101 and an Indian food database, between which the later had 50 categories with 100 images of each.…”

Section: Deep Learning Applications In Food Food Recognition and Clasmentioning

confidence: 99%

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Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

352

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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Section: Deep Learning Applications In Foodmentioning

confidence: 99%

“…CNN‐based ensemble network architecture (Pandey et al., ). The green, yellow, and blue blocks represent the features extracted by AlexNet, GoogLeNet, and ResNet, respectively.…”

Section: Deep Learning Applications In Foodmentioning

confidence: 99%

Section: Deep Learning Applications In Food Food Recognition and Clasmentioning

confidence: 99%

Section: Deep Learning Applications In Food Food Recognition and Clasmentioning

confidence: 99%

Section: Deep Learning Applications In Food Food Recognition and Clasmentioning

confidence: 99%

See 3 more Smart Citations

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

352

166

View full text Add to dashboard Cite

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CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

Guo

Huang

Zhang

et al. 2018

Lecture Notes in Computer Science

265

202

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We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling a massive amount of noisy labels and data imbalance effectively. We design a new learning curriculum by measuring the complexity of data using its distribution density in a feature space, and rank the complexity in an unsupervised manner. This allows for an efficient implementation of curriculum learning on large-scale web images, resulting in a highperformance CNN model, where the negative impact of noisy labels is reduced substantially. Importantly, we show by experiments that those images with highly noisy labels can surprisingly improve the generalization capability of the model, by serving as a manner of regularization. Our approaches obtain state-of-the-art performance on four benchmarks: WebVision, ImageNet, Clothing-1M and Food-101. With an ensemble of multiple models, we achieved a top-5 error rate of 5.2% on the WebVision challenge [18] for 1000-category classification. This result was the top performance by a wide margin, outperforming second place by a nearly 50% relative error rate. Code and models are available at: https://github.com/MalongTech/CurriculumNet.

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Parallel Concatenated Network with Cross-layer Connections for Image Recognition

Jiang

Zeng

et al. 2019

Lecture Notes in Computer Science

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FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Cited by 106 publications

References 26 publications

Application of Deep Learning in Food: A Review

Application of Deep Learning in Food: A Review

CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

Parallel Concatenated Network with Cross-layer Connections for Image Recognition

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