Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. Computer vision tasks include image acquisition, image processing, and image analysis. The image data can come in different forms, such as video sequences, view from multiple cameras at different angles, or multi-dimensional data from a medical scanner.
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
ImageNet : The de-facto image dataset for new algorithms. Is organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images.
LSUN : Scene understanding with many ancillary tasks room layout estimation, saliency prediction, etc. It can be used for object segmentation, recognition in context, and many other use cases. Visual Genome : Visual Genome is a dataset and knowledge base created in an effort to connect structured image concepts to language. The database features detailed visual knowledge base with captioning ofimages.
Labelled Faces in the Wild : 13, labeled images of human faces, for use in developing applications that involve facial recognition. Stanford Dogs Dataset: Contains 20, images and different dog breed categories, with about images per class. Places : Scene-centric database with scene categories and 2. CelebFaces : Face dataset with more thancelebrity images, each with 40 attribute annotations. Flowers : Dataset of images of flowers commonly found in the UK consisting of different categories.
Plant Image Analysis : A collection of datasets spanning over 1 million images of plants. Can choose from 11 species of plants. Home Objects : A dataset that contains random objects from home, mostly from kitchen, bathroom and living room split into training and test datasets. The dataset is divided into five training batches and one test batch, each containing 10, images. Contains 67 Indoor categories, and a total of images. These questions require an understanding of vision and language.Earth Observation Data Data Recipes.
Data Recipes. Includes a set of map libraries, code snippets, working examples, and screen captures designed to help in building a user interface to explore NASA's Earth imagery. Alternatively, these guidelines can also help to integrate that imagery into an existing client or to build scripts to retrieve imagery from GIBS. Includes a list of tools along with instructions and screen captures to help import imagery into them.
Data Access How-To How to access data with R Example of how to obtain your data in way that caches your user credentials, not requiring repeated requests to URS for authorization. Data Access How-To. Learn2Map Tutorial and Atlas. Probabilistic Seismic Hazard Analysis Tutorial.
Python code example demonstrating how to configure a connection to download data from an Earthdata Login enabled server.
Java code example demonstrating how to configure a connection to download data from an Earthdata Login enabled server. PHP code example demonstrates how to configure a connection to download data from an Earthdata Login enabled server. C code example demonstrates how to configure a connection to download data from an Earthdata Login enabled server. How to access data with R. Example of how to obtain your data in way that caches your user credentials, not requiring repeated requests to URS for authorization.
Data Download Script Perl. Generic data download script that can be used to download data files from Earthdata Login enabled servers. Data Download Script Python.Using these data, we train a neural network to learn a joint embedding of recipes and images that yieldsimpressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-levelclassification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic.
Code, dataand models are publicly available. Check out our most recent journal paper for full details and more analysis! Our CVPR paper can be downloaded from here.
Follow this link to download the dataset. We train a joint embedding composed of an encoder for each modality ingredients, instructions and images. We evaluate all the recipe representations for im2recipe retrieval. Given a food image, the task is to retrieve its recipe from a collection of test recipes. In order to better assess the quality of our embeddings we also evaluate the performance of humans on the im2recipe task.
Check out our online demoin which you can upload your food images to retrieve a recipe from our dataset. We explore whether any semantic concepts emerge in the neuron activations and whether the embedding space has certain arithmetic properties.
We show the localized unit activations in both image and recipe embeddings. We find that certain units show localized semantic alignment between the embeddings of the two modalities. We demonstrate the capabilities of our learned embeddings with simple arithmetic operations. In the context of food recipes, one would expect that:.
We investigate whether our learned embeddings have such properties by applying the previous equation template to the averaged vectors of recipes that contain the queried words in their title.
The figures below show some results with same and cross-modality embedding arithmetics. Image Embeddings Recipe Embeddings Fractional arithmetics Another type of arithmetic we examine is fractional arithmetic, in which our model interpolates across the vector representations of two concepts in the embedding space.
Specifically, we examine the results for:. In the figure bellow we show those recipes that belong to the top 12 semantic categories used in our semantic regularization. In the next figure we can see the previous embedding visualization but this time showing the same recipes on different colors depending on how healthy they are in terms of sugar, fat, saturates and salt.
Below are the dataset statistics:. Joint embedding We train a joint embedding composed of an encoder for each modality ingredients, instructions and images.
Results im2recipe retrieval We evaluate all the recipe representations for im2recipe retrieval. Demo [Coming soon! Embedding Analysis We explore whether any semantic concepts emerge in the neuron activations and whether the embedding space has certain arithmetic properties. Visualizing embedding units We show the localized unit activations in both image and recipe embeddings. Semantic categories In the figure bellow we show those recipes that belong to the top 12 semantic categories used in our semantic regularization.
Pattern Anal.A few data sets are accessible from our data science apprenticeship web page. You can find additional data sets at the Harvard University Data Science website. I was particularly interested in their LinkedIn data set.
recipes & menus
Cross-disciplinary data repositories, data collections and data search engines:. Single datasets and data repositories. Views: Share Tweet Facebook. Join Data Science Central. I just start to learn Big Data. But few silly things irritate a lot. Big data generally minimum TB in size, right? But when I follow referred links about the data sets of Big data, the file is so small in size, max MB.
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Top 10 Great Sites with Free Data Sets
Added by Tim Matteson 0 Comments 1 Like. Added by Tim Matteson 1 Comment 1 Like. Archives: Book 1 Book 2 More. Home Top Content Editorial Guidelines. Top Content Archives. Source code and data for our Big Data keyword correlation API see also section in separate chapter, in our book Great statistical analysis: forecasting meteorite hits see also section in separate chapter, in our book Fast clustering algorithms for massive datasets see also section in separate chapter, in our book Views: Tags: Like.
Comment You need to be a member of Data Science Central to add comments! Add Videos View All. Please check your browser settings or contact your system administrator.Online recipes typically consist of several components: a recipe title, a list of ingredients and measurements, instructions for preparation, and a picture of the resulting dish.
This dataset is particularly interesting for machine learning because each recipe contains multiple elements, each of which provides additional information about the recipe. Current deep learning models excel at learning the relationship between one element and a single other element e.
This dataset has been used for several deep learning projects so far:. Roughly 70, of these recipes have images associated with them. Comments and ratings data are not included. The original source URLs can be downloaded by re-running the scrapers, as documented in the project documentation. Toggle navigation Eight Portions. Home Datasets Resources About. Drain, and reserve the lime juice, after all of the avocados have been coated.
Using a potato masher add the salt, cumin, and cayenne and mash. Then, fold in the onions, tomatoes, cilantro, and garlic. Add 1 tablespoon of the reserved lime juice. Let sit at room temperature for 1 hour and then serve.
Learn more Download recipes Download recipe images View project on GitHub View other datasets Footnotes Roughly 70, of these recipes have images associated with them.RecipeQA is a dataset for multimodal comprehension of cooking recipes. It consists of over 36K question-answer pairs automatically generated from approximately 20K unique recipes with step-by-step instructions and images.
Each question in RecipeQA involves multiple modalities such as titles, descriptions or images, and working towards an answer requires i joint understanding of images and text, ii capturing the temporal flow of events, and iii making sense of procedural knowledge.
To better know about RecipeQA, please read our comprehensive datasheet documenting and describing the details about its creation, strengths and limitations. RecipeQA is meant to facilitate research on comprehending procedural knowledge in a multimodal setting where cooking recipes are used as testbed. It differs from existing reading comprehension datasets in the following ways: 1 it leverages data from real natural language found online, 2 the multimodal aspects of the questions makes the benchmark less gameable, preventing questions from easily answerable through shallow signals, and 3 it involves a large number of images which are taken by ordinary people in unconstrained environments.
To evaluate your models, we provide an evaluation script that will be used for the official evaluation, along with a sample prediction file. To run the evaluation, use:. Once you are satisfied with your model performance on the validation set, you can submit it to get the official score on the test set. To preserve the integrity of the test results, we do not release the test set to the public. Follow this tutorial on how to submit your model for an official evaluation:.
RecipeQA contains question answer pairs generated from copyright free recipes found online under a variety of licences. The corresponding licence for each recipe is also provided in the dataset, see recipes.
Ask us questions at our google group or at semih. What is RecipeQA? To run the evaluation, use: python evaluate. Follow this tutorial on how to submit your model for an official evaluation: Submission Tutorial Licence RecipeQA contains question answer pairs generated from copyright free recipes found online under a variety of licences.
Have Questions? Project webpage designed by Taha Sevim and Kanan Hagverdiyev.Food composition data FCD are detailed sets of information on the nutritionally important components of foods and provide values for energy and nutrients including protein, carbohydrates, fat, vitamins and minerals and for other important food components such as fibre.
The data are presented in food composition databases FCDBs. This demonstrates the main reason for establishing FCD at that time. To this day, food composition studies remain central to nutrition research into the role of food components and their interactions in health and disease. However, due to increasing levels of sophistication and complexity in nutrition science, there is a greater demand for complete, current and reliable FCD, together with information on a wider range of food components, including bioactive compounds.
FCD are important in many fields including clinical practice, research, nutrition policy, public health and education, and the food manufacturing industry and is used in a variety of ways including: national programmes for the assessment of diet and nutritional status at a population level e.
The earliest food composition tables were based solely on chemical analyses of food samples, which were mostly undertaken specifically for the tables.
However, as the food supply has evolved, and with the increasing demand for nutritional and related components, it has become more difficult for compilers to rely only on chemical analysis when compiling FCDBs. For example, in the UK the third edition of The Composition of Foods  presented data on vitamin content of foods.
However, due to the amount of information already available and in order to avoid the need to analyse every food for every vitamin, values from the scientific literature were included, although the tables are still predominately based on analytical data.
Nowadays, food composition databases tend to be compiled using a variety of methods as described below. Chemical analysis of food samples carried out in analytical laboratories is typically the preferred method for creating FCD.
The food samples are carefully chosen using a defined sampling plan to ensure that they are representative of the foods being consumed in a country. This includes accounting for factors that could affect the nutrient content of a food as purchased e. If necessary, further preparation and cooking takes place prior to the analysis using appropriate analytical methods and often appropriate samples of foods are combined rather than taking averages of individually analysed food samples.
Ideally, the methods used for analysis should have been shown to be reliable and reproducible, i. It is not feasible to determine FCD using chemical analysis for every nutrient in every food type due to insufficient resources.Machine Learning Tutorial Python - 7: Training and Testing Data
Compilers will need to evaluate the data in terms of both data quality and applicability of foods before incorporating it from any of these sources into their FCDBs.
An important step for both new analytical FCD and for values borrowed from other sources is for the compiler to evaluate the quality of the data before it can be added into FCDBs. In addition, a range of data quality measures need to be undertaken relating to the food identity and sampling and analytical aspects.
For example, the USA has developed a multi-nutrient data quality evaluation system for which five evaluation categories are used including: sampling plan, number of samples, sample handling, analytical method and analytical quality control.
Food composition datasets FCDBs or food composition tables are resources that provide detailed food composition data FCD on the nutritionally important components of foods. FCDBs provide values for energy and nutrients including proteincarbohydratesfatvitamins and minerals and for other important food components such as fibre. Before computer technology, these resources existed in printed tables with the oldest tables dating back to the early 19th century.
FCDBs differ in both the data that is available and in the amount of data that is held. Some specialised datasets are also available e. Some datasets include a wider range of processed foods, composite dishes and recipes as well as foods prepared and cooked in different ways. Some of the earliest work related to detecting adulterated foods and finding the active components of medicinal herbs.