CIFAR-10
The CIFAR-10 dataset is an established collection of 60,000 32×32 color images split into 10 different classes, each containing 6,000 images. The classes represent various objects such as airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. The dataset is divided into a training set of 50,000 images and a test set of 10,000 images, facilitating the development and evaluation of machine learning models in image classification tasks. Developed by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton, CIFAR-10 is derived from the larger CIFAR-100 dataset and is widely utilized in academic and research settings for benchmarking computer vision algorithms due to its manageable size and well-defined task structure.
Description:
- Basic Composition: Contains 60,000 32×32 color images.
- Class Variety: Split into 10 classes, each with 6,000 images.
- Classes Included: Airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
- Training vs Testing: Divided into 50,000 training images and 10,000 testing images.
- Usage: Widely used for training and testing machine learning models in computer vision tasks.
Dataset for Image Classification
The field of computer vision has witnessed remarkable progress in recent years, largely driven by the availability of large-scale datasets for image classification tasks. These datasets play a pivotal role in training and evaluating machine learning models, enabling them to recognize and categorize visual content with increasing accuracy.
In this article, we will discuss some of the famous datasets used for image classification.