Guide to Visual Recognition Datasets for Deep Learning with Python Code

BEGIN ARTICLE PREVIEW: Some visual recognition datasets have set benchmarks for supervised learning (Caltech101, Caltech256, CaltechBirds, CIFAR-10 andCIFAR-100) and unsupervised or self-taught learning algorithms(STL10) using deep learning across different object categories for various researches and developments. Under visual recognition mainly comes image classification, image segmentation and localization, object detection and various other use case problems. Many of these datasets have APIs present across some deep learning frameworks. I’ll be mentioning some of them in this article which can be directly imported and used to train models. Cifar(Canadian Institute of Advanced Research) is a subset of 80 million tiny images dataset which has been collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Dataset can be found on the official website of the Computer Science department of the University of Toronto.  California Institute of Technology