# Deep Learning in Food Image Recognition: A Comprehensive Review
## 2 Datasets
Data Augmentation Techniques: * Geometric transformation: By rotating, flipping, scaling, and performing other operations on the image, it can simulate the food form under different shooting angles and perspectives and effectively alleviate the problem of model recognition bias caused by different shooting angles. * Color perturbation: Adjusting the color parameters of the image such as brightness and contrast can reduce the influence of light changes on the visual characteristics of the food image and enhance the robustness of the model under different lighting conditions. Figure 2: Sample images from a few food datasets. * Generative data augmentation: [PERSON] et al. [20] utilized the Conditional Generative Adversarial Network (CGAN) for food image generation. In the experiment, the CGAN was trained using the \"ramen\" image dataset and the recipe image dataset. Through generative data augmentation, the diversity of the data was enriched. A plate discriminator was added to the \"ramen\" GAN to make the shape of the dishes in the generated image more rounded. The \"recipe\" GAN was used to generate dish images from cooking ingredients and use them for image search in the recipe database. 2. Domain adaptation method * Transfer learning: This approach pretrains models on large-scale general-purpose datasets such as ImageNet and then fine-tunes them to the task of food image recognition. This method reduces the dependence of the model on large-scale labeled food data and makes full use of the common visual features learned by the pre-trained model. * Self-supervised learning: In the case of masked image modeling, large amounts of unlabeled data can be effectively utilized to improve the generalization ability of the model by occlusion part of the image region and letting the model predict the occluded content. 3. Multi-source data fusion * Combining auxiliary information such as recipe texts and geolocation with visual data of food images can make up for the cultural bias problem existing in pure visual data. [PERSON] et al. [21] simplified the classification problem by using context information such as geolocation and proposed a food recognition framework including semantic feature learning and location-adaptive classification. Experiments showed that using geographical location could improve the recognition performance by approximately 30%. In summary, the existing food image datasets have made significant progress in scale and diversity, but there are still problems such as insufficient cultural coverage, rough annotation granularity, and a lack of authenticity. Mainstream datasets such as Food-101 and ISIA Food-500 provide benchmarks for research. However, the proportion of non-Western foods is low, and there is a lack of fine-grained annotations such as ingredients and cooking methods. To cope with these limitations, researchers have employed techniques such as data augmentation, domain adaptation, and multi-source data fusion. In the future, large-scale datasets with more cultural representation and fine-grained annotation should be constructed to improve the generalization ability of the model.