ImageNet

What is Image Classification?

Image classification is a fundamental task in computer vision where the goal is to assign a label or category to an input image based on its visual content. This involves identifying and interpreting the objects, features, or patterns within the image to categorize it into one of several predefined classes....

List of Image Classification Datasets

MNIST (Modified National Institute of Standards and Technology) CIFAR-10 and CIFAR-100 (Canadian Institute For Advanced Research) ImageNet COCO (Common Objects in Context) Fashion-MNIST SVHN (Street View House Numbers) Caltech 101 and Caltech 256 PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) CelebA (CelebFaces Attributes Dataset) FER-2013 (Facial Expression Recognition 2013) Tiny ImageNet Oxford 102 Flower Dataset Animals with Attributes 2 (AwA2) Stanford Cars MIT Indoor Scenes...

ImageNet

ImageNet is a comprehensive image database organized according to the WordNet hierarchy, providing a vast resource for training machine learning models in object recognition. Spearheaded by Fei-Fei Li at Stanford University, it comprises over 14 million images labeled and categorized into more than 20,000 groups. Each image is annotated with labels and bounding boxes to indicate the presence and location of objects. A notable subset of ImageNet is the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which features approximately 1,000 images in each of 1,000 categories. ILSVRC serves as a benchmark in the field, significantly advancing the capabilities of image classification and object detection algorithms in computer vision....

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....

MNIST

The MNIST dataset is a collection of 70,000 grayscale images of handwritten digits from 0 to 9, each sized at 28×28 pixels. It includes 60,000 training images and 10,000 test images, serving as a foundational benchmark for image processing systems in machine learning and computer vision. MNIST is crucial for training and testing algorithms in tasks like image classification, where models learn to recognize and classify digits. Developed by the National Institute of Standards and Technology (NIST), this dataset’s simplicity and moderate size make it ideal for beginners in machine learning. MNIST is widely used in educational settings to demonstrate the fundamentals of neural networks and image recognition, making it a staple in introductory machine learning courses....

Fashion-MNIST

Fashion-MNIST is a dataset designed as a more challenging replacement for the original MNIST dataset. It consists of 70,000 grayscale images of 10 different fashion items such as T-shirts, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots, each sized at 28×28 pixels. Like MNIST, it is divided into a training set of 60,000 images and a test set of 10,000 images. Created by Zalando Research, Fashion-MNIST serves the same purpose as the traditional MNIST—facilitating benchmarking and experimentation in machine learning and computer vision—but with a focus on fashion products. This dataset is commonly used in academic and research settings to develop, train, and test advanced image classification algorithms....

Stanford Dogs

The Stanford Dogs dataset is a collection specifically designed for fine-grained image classification, focusing on distinguishing between different dog breeds. It contains 20,580 images representing 120 different dog breeds, curated from the ImageNet dataset. Each breed includes a varying number of images, aiming to provide a comprehensive set for developing and testing machine learning models that can accurately identify and differentiate dog breeds based on visual cues. The dataset was assembled by the Vision Lab at Stanford University and is widely used in the computer vision community for both educational and research purposes. The diversity and specificity of the breeds make it a challenging and valuable resource for advancing the capabilities of image recognition systems in recognizing fine-grained categories....

Food-101

The Food-101 dataset is a collection specifically designed for the task of food recognition, which is a subset of image classification aimed at identifying various types of dishes. It contains 101,000 images divided across 101 different food categories, with each category featuring 1,000 images. This dataset was created to help develop and evaluate machine learning models that can accurately recognize and categorize different food items from images, a task that presents challenges due to the high variability in food appearance, cooking style, and presentation....

Caltech 101

The Caltech 101 dataset is a collection of approximately 9,144 images divided into 101 object categories, plus an additional background category. Categories span a wide range of objects, including animals, household items, vehicles, and plants, with about 40 to 800 images per category. Each image is roughly 300 x 200 pixels in size. Developed by the California Institute of Technology, this dataset is primarily used for computer vision research in object recognition. The diversity of categories and the moderate size of the dataset make it suitable for testing and benchmarking image recognition algorithms, especially for those new to machine learning and computer vision....

UCF101

UCF101 is a dataset designed for action recognition in videos, making it a fundamental resource for research in the field of video processing and understanding. It consists of 13,320 videos spanning 101 action categories, including a variety of human activities such as playing instruments, sports, and performing exercises. Each video clip is labeled with a single action class and provides a rich source of dynamic visual information....

Street View House Numbers (SVHN)

The Street View House Numbers (SVHN) dataset is a collection of digit images sourced from Google Street View images, designed for developing robust digit recognition models. It contains over 600,000 full-color digit images that are derived from real-world, varied backgrounds, providing a challenging alternative to the simpler MNIST dataset. SVHN offers two formats: the first has digits centered in 32×32 pixel images, and the second provides images of full house number sequences with each digit boxed and labeled. This dataset is ideal for training machine learning algorithms to recognize digits in uncontrolled, everyday environments, enhancing capabilities in practical applications like automated information retrieval....

COCO

The COCO (Common Objects in Context) dataset is a foundational tool for the computer vision community, designed to facilitate object detection, segmentation, and captioning tasks. It includes over 330,000 images, more than 200,000 of which are labeled, featuring complex scenes with multiple objects in natural contexts. COCO provides rich annotations, such as object bounding boxes, segmentation masks, and detailed image captions. This dataset supports a broad range of applications and research in image understanding and has spurred advancements in AI by serving as a benchmark for annual challenges that push the limits of object recognition and image captioning technologies....

Open Images

Open Images is a diverse and large-scale dataset designed for computer vision research, hosted by Google. It contains approximately 9 million images annotated with labels spanning thousands of object categories. The dataset is known for its rich annotations, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives that provide textual descriptions of image content. Open Images supports a variety of computer vision tasks such as object detection, visual relationship detection, and segmentation. It is particularly useful for training and evaluating models due to its wide variety of annotated objects and complex scenes, making it a valuable resource for advancing image recognition technologies....

Dataset for Image Classification FAQs

How are image classification datasets used?...