# Recognition of Food Ingredients--Dataset Analysis
## 2 Related Works

Table 1: Scientific articles analyzed. \begin{table}
\begin{tabular}{c c c} \hline
**Study** & **Model** & **Description** \\ \hline  & & The authors propose a MultiTask Deep Belief Network (M3 TDBN) that can identify ingredients in recipes through textual representation. The model considers various attributes related to the recipe, such as the type of cuisine and the type of dish. The Yummly-28K dataset, comprising 63,492 recipe images, was used to train the model. To identify the ingredients, it is necessary to pre-process the text, removing irrelevant information such as quantity. \\  & (M3 TDBN) & Transfer learning was employed using a convolutional neural network (CNN) pre-trained on the Food-101 dataset, which was then fine-tuned to the Yummly-28K dataset. The features extracted by this CNN were used as inputs for the M3 TDBN model. It was concluded that incorporating additional information, such as the type of dish, significantly improved performance. \\ \hline  & & To address the challenge of ingredient recognition in food images, the authors propose a D-Mixup (Dynamic Mixup) approach. The objective is to enhance the representation of minority ingredients, given the prevalence of significant disparities in the frequency of certain ingredients across most datasets. \\  & ResNet-50 & Additionally, this method mitigates the issue of datasets where test images exhibit high similarity to training images, which may not accurately reflect their true effectiveness in real-world scenarios. This structure also comprises a region-wise recognition network, which is responsible for identifying the ingredients present in each region. The results of this approach indicate that it improves performance on datasets that present these problems. \\ \hline  & & The authors propose a unified structure that encompasses two distinct approaches: many-shot learning and few-shot learning. Additionally, the structure employs a convolutional graph network (GCN) to capture relationships between different categories of food. To circumvent the issue of suboptimal performance in classes with limited image data (few-shot learning), this structure comprises two distinct phases. In the initial phase, semantic embeddings are generated utilizing the BERT (Bidirectional Encoder Representations from Transformers) model, thereby furnishing supplementary data on the specific type of food associated with each food category. \\  & EfficientNet-B0 & Subsequent to this, a convolutional neural network (CNN) is employed to extract features from images comprising a multitude of samples, after which the integration of learning from numerous and few categories is achieved. In the second phase, the graph convolutional network (GCN) is integrated into the model to facilitate the comprehension of the interconnections and distinctions between categories. This approach has been demonstrated to yield a notable enhancement in performance compared to more sophisticated studies on few-category learning. \\ \hline  & & The authors propose a method that employs a convolutional neural network (CNN) ResNet-50 for food recognition, utilizing the fine-tuning technique following the initial training of the model on the ImageNet database. The images are then subjected to pre-processing, including resizing to a resolution of 224x224 pixels, and convolutional filters are applied to extract the most relevant features. It is observed that the greater the number of classes, the lower the accuracy. Conversely, the greater the number of instances per class, the greater the accuracy. \\ \hline  & & The authors’ approach to food recognition divides the problem into two topics: binary classification and locating the food followed by its category. Binary classification identifies whether an image contains food or not, while food localization usually requires the use of bounding boxes, which are then used to classify several foods in the same image using convolutional neural networks (CNNs).