Popular Computer Vision Datasets for Image Classification
ImageNet
Dataset link: https://www.image-net.org/update-mar-11-2021.php
It is one of the most popular datasets having more than 14 million images that are hand-annotated. These millions of images are categorized into thousands of classes. The images in this dataset are organised based on WordNet Hierarchy. Thousands of images depict each node of hierarchy. Object-level annotations provide a bounding box around the (visible part of the) indicated object.
CIFAR-10 and CIFAR-100
Dataset link: https://www.cs.toronto.edu/~kriz/cifar.html
CIFAR-10 dataset of the Goggle Images consists of 60,000 32Ă—32 color images in 10 different classes with 6,000 images per class. The classes are items familiar to people such as airplane, car, bird, cats, and dogs. It is applied mainly for training machine learning and computer vision applications.
CIFAR-100 is like CIFAR-10, but the dataset contains 100 different classes each of which includes 600 images. The following 100 classes are classified into twenty superclasses. It gives more categories of data than CIFAR-10 so the data categorized here is detailed and different.
MNIST
Dataset link: https://git-disl.github.io/GTDLBench/datasets/mnist_datasets/
The MNIST dataset has 70000 colorless images that are each 28 pixels by 28 pixels and contain writing which ranges from 0 to 9. It is divided into 60000 training image and 10000 testing images. MNIST database is used as a standard database for any new machine learning algorithm andtechniques, particularly in the image classification applications.
Fashion MNIST
Dataset link: https://github.com/zalandoresearch/fashion-mnist
Fashion MNIST is a dataset of 70,000 28 pixel by 28 pixel grayscale images of ten types of clothing including: shirt, trouser, pullover, dress, coat, sandal, sneaker, bag, ankle boot, and shoe. It is envisaged to be used as a direct replacement for the original MNIST dataset, but due to the higher variability and resemblance of most of the fashion articles, it proves to be slightly more challenging for classification.
Dataset for Computer Vision
Computer Vision is an area in the field of Artificial Intelligence that enables machines to interpret and understand visual information. As in case of any other AI application, Computer vision also requires huge amount of data to give accurate results. These datasets provide all the necessary training material for these algorithms.
A dataset that will well-prepared and maintained will allow the model to learn from examples, recognize pattern and then make predictions about the unseen data. Therefore, the quality of datasets matters a lot, as it impacts the performance and robustness of computer vision applications.