Cooking Recipe Data Chip – Using pictures of food to find recipes. A new method based on Transformers and trained through self-examination-provides modern results.
When looking for food ideas, people often find inspiration on social media and in restaurants, save screens or take pictures of dishes they liked. At , we’ve created technology that allows people to use these images to find the right recipes.
Cooking Recipe Data Chip
At the 2021 Conference on Computer Vision and Pattern Recognition (CVPR), my colleagues and I will present a new image retrieval method that achieves state-of-the-art performance by using Transformer-based and self-monitoring architectures. supervised learning.
Cheesy Gravy Chips Recipe
The self-directed learning paradigm in which the automatic processing of anonymous data provides some examples of the training of machine learning models. In our case, in addition to supervised learning using detailed images with relevant recipes, we practice using data recipes only.
Our method uses two separate encoder functions, one for the recipe and one for the image (left and right respectively in the image below). These functions remove the views that will be used for sorting and searching during the estimation process. To code recipe components, we use Transformer-based architectures that are hierarchical for multi-line input (such as ingredients and instructions) and non-hierarchical multi-line input (recipe names). For image encoding, we use well-proven ResNet and Vision Transformers image encoders.
, is calculated between the spectra obtained from the recipe (left) and the image (right). This loss ensures that text and images represent the closest to each other in a common high-dimensional space if they belong to the same training example (for example, the image of a chocolate chip cookie and the corresponding recipe text) and far from each other. if not (for example, the same picture of chocolate chip cookies and the text from the alasagnare recipe).
During training, relevant pictures and recipes serve as positive examples, while inappropriate pictures and recipes serve as negative examples.
Using Food Images To Find Cooking Recipes
, is counted among the representative individual recipe components. This loss ensures that the representations of the component recipes (such as the header and links) will be close together in the representation area if they belong to the same recipe, and separate otherwise (see the image below). Intuitively, the name mac and cheeserecipe and the names of its ingredients (pasta, onion, parmesan cheese, etc.) share semantic features that can help the model learn better what the recipe represents.
Since this loss does not require an image as input, it can be calculated for training examples without images, which are common in web recipe data; in practice, 66% of our training set consists only of recipe books. Our experiments show that the new self-evaluation loss term (although it is only used for image-recipe training pairs) and the additional training data are useful in improving the search performance.
The self-assessment loss function combines representations of components from the same recipe and decouples representations of components from different recipes.
In our experiment, we did a cross search in both directions: finding recipes that match pictures and pictures that match recipes. Our method has demonstrated state-of-the-art performance on the Recipe1M database, a standard in the field. In the image-to-recipe retrieval task, our method achieved a Recall@10 of 92.9% when searching a 1000-item recipe database. This means that given a database of 1000 recipes and 100 food image queries, our method can find the correct recipe among the top 10 returned answers for 92.9% of the image queries.
Baked Kale Chips Recipe
In the image below, we show some of the results that show that our method is able to encode semantics in the image and recipe representation and is able to find recipes that match the query at the level of the object (for example, “bread”, “garlic”, and “bread” in the first sentence or “salmon” and “asparagus” in line 6).
Results for “image to recipe” (odd rows) and “recipe to image” (even rows) search modes. The request/recipe image is highlighted in blue, followed by the first five items removed. The right thing is highlighted in green. Recipes are displayed as word years (word size is proportional to the number of words in the recipe).
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Easy Cake Mix Cookie Bars
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