- Published on November 17, 2020
- In Deep Tech
Guide to Visual Recognition Datasets for Deep Learning with Python Code
Taking our visual recognition datasets discussions further, today we will be talking about Caltech101, Caltech256, CaltechBirds, CIFAR-10 andCIFAR-100 and stl-10 datasets features along with some python code snippets on how to use them.
- By Jayita Bhattacharyya
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 Hin
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Jayita Bhattacharyya
Machine learning and data science enthusiast. Eager to learn new technology advances. A self-taught techie who loves to do cool stuff using technology for fun and worthwhile.
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