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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 ...
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
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.
In other projects Wikidata item; Appearance. ... Pages in category "Datasets in machine learning" The following 12 pages are in this category, out of 12 total.
Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.
The Overhead Imagery Research Data Set (OIRDS) is a collection of an open-source, annotated, overhead images that computer vision researchers can use to aid in the development of algorithms. [1] Most computer vision and machine learning algorithms function by training on a large set of example data. [ 2 ]
Deeplearning4j, an open-source, distributed deep learning framework written for the JVM. [80] Keras, a high level open-source software library for machine learning (works on top of other libraries). [81] Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks. [82]
DVC is designed to incorporate the best practices of software development [10] into Machine Learning workflows. [11] It does this by extending the traditional software tool Git by cloud storages for datasets and Machine Learning models. [12] Specifically, DVC makes Machine Learning operations: