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Train/test splits, labeled images, 1360 Images, text Classification 2006 [315] [316] M-E Nilsback et al. Plant Seedlings Dataset 12 category dataset of plant seedlings. Labelled images, segmented images, 5544 Images Classification, detection 2017 [317] Giselsson et al. Fruits-360 Database with images of 131 fruits and vegetables.
Image collection visualization is the process of visualize a set of images using a visualization metaphor, in which an image similarity function is used to represent image relations in a visualization layout. [3] Information visualization is an active area that investigates new ways to visualize information by using visualization metaphors.
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
A software repository, or repo for short, is a storage location for software packages. Often a table of contents is also stored, along with metadata. A software repository is typically managed by source or version control, or repository managers. Package managers allow automatically installing and updating repositories, sometimes called "packages".
A content repository or content store is a database of digital content with an associated set of data management, search and access methods allowing application-independent access to the content, rather like a digital library, but with the ability to store and modify content in addition to searching and retrieving.
The Fashion MNIST dataset is a large freely available database of fashion images that is commonly used for training and testing various machine learning systems. [1] [2] Fashion-MNIST was intended to serve as a replacement for the original MNIST database for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits.
Apache Superset is an open-source software application for data exploration and data visualization able to handle data at petabyte scale ().The application started as a hack-a-thon project by Maxime Beauchemin (creator of Apache Airflow) while working at Airbnb and entered the Apache Incubator program in 2017. [1]