Food Recognition using Transfer Learning

Basrur, Ankit; Mehta, Dhrumil; Joshi, Abhijit

doi:10.1109/ibssc56953.2022.10037284

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…This is achieved through the use of a vector weight optimization strategy, which optimizes the weight of the classifier vectors and thus better adapts to changes in eating habits. [17] ResNet VGG19 EfficientNet-B0 DenseNet…”

Section: Studymentioning

confidence: 99%

“…Food recognition is a proven advantage for making the eating process simpler, more economical, and healthier. Various solutions can be applied to this task using artificial intelligence and machine learning algorithms, as mentioned in the articles [14][15][16][17][18][19][20][21][22][23][24][25][26]. All of the aforementioned articles are capable of recognizing food through a variety of approaches, resulting in varying degrees of accuracy.…”

Section: Resnet-101 Googlenet Vgg16/19 Inceptionv3mentioning

confidence: 99%

“…Although many of the models implemented for food recognition can be used for recipe recommendation, the majority of articles do not explicitly refer to this process. However, in articles [14], [17], and [21], different approaches are used for this purpose.…”

Section: Resnet-101 Googlenet Vgg16/19 Inceptionv3mentioning

confidence: 99%

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Recognition of Food Ingredients—Dataset Analysis

Louro,

Fidalgo,

Oliveira

2024

Applied Sciences

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Nowadays, food waste is seen as a complex problem with effects on the social, economic, and environmental domains. Even though this view is widely held, it is frequently believed that individual acts have little to no impact on the issue. But just like with recycling, there may be a significant impact if people start adopting more sustainable eating habits. We suggest using a cutting-edge convolutional neural network (CNN) model to identify food in light of these factors. To improve performance, this model makes use of several strategies, such as fine-tuning and transfer learning. Additionally, we suggest using the Selenium library to create a dataset by employing the web scraping technique. This strategy solves the problem that many open-source datasets have with the overrepresentation of foods from the Asian continent by enabling the addition of foods to the dataset in a customized way. First, using the PRISMA methodology, a thorough examination of recent research in this field will be carried out. We will talk about the shortcomings of the most widely used dataset (Food-101), which prevent the ResNet-50 model from performing well. Using this information, a smartphone app that can identify food and suggest recipes based on the ingredients it finds could be developed. This would prevent food waste that results from the lack of imagination and patience of most people. The food recognition model used was the ResNet-50 convolutional neural network, which achieved 90% accuracy for the validation set and roughly 97% accuracy in training.

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Section: Studymentioning

confidence: 99%

Section: Resnet-101 Googlenet Vgg16/19 Inceptionv3mentioning

confidence: 99%

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Recognition of Food Ingredients—Dataset Analysis

Louro,

Fidalgo,

Oliveira

2024

Applied Sciences

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“…EfficientNet was proposed by Tan et al in 2019, which is a new method for uniformly scaling the depth, width, and input image resolution of network models [8]. Unlike previous model extensions that arbitrarily expanded the depth, width, and input image resolution of the network, this method uses composite coefficients to perform a more structured unified scaling of the depth, width, and input image resolution of the network model.…”

Section: Revisit Efficientnet and Resnetmentioning

confidence: 99%

Automatic food recognition based on efficientnet and ResNet

Liu

2023

J. Phys.: Conf. Ser.

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Automatic Food recognitionbased image is an emerging topic, which aims to extract the feature of given food image and then predict its category. Compared with the ordinary image recognition task, the food recognition is more challenging due to the high similarity between subcategories and the high inter-class variability of specific category. Early food image recognition methods are mainly based on manual features such as color, shape, texture and other information, which cannot meet actual application requirements limited by insufficient feature expression capabilities. Thanks to the rapid development of convolutional neural network, the methods based on deep learning boost the recognition accuracy and speed significantly. In this paper, we construct the food recognition models based on the EfficientNet and ResNet, respectively. Extensive experiments have been conducted on Food-101 dataset. All the results show the effectiveness of our models, which can recognize various foods accurately.

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