Search results
Results From The WOW.Com Content Network
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
Images and (.mat, .txt, and .csv) label files Gender recognition and biometric identification 2017 [41] M Afifi CORe50 Specifically designed for Continuous/Lifelong Learning and Object Recognition, is a collection of more than 500 videos (30fps) of 50 domestic objects belonging to 10 different categories.
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. [1] [2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. [3]
The iris data set is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R base and Python in the machine learning library scikit-learn, so that users can access it without having to find a source for it. Several versions of the dataset have been published. [8]
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Download QR code; Print/export Download as PDF; Printable version; In other projects Wikidata item; Appearance. ... Pages in category "Datasets in machine learning"
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.
The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning, [4] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes. The Caltech 101 images, along with the annotations, were used for another one-shot learning paper at Caltech. [5]