Small Data Image Classification

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class. The criteria for deciding whether an image belongs to a class were as follows: * The class name should be high on the list of likely answers to the question “What is in this picture?” * The image should be photo-realistic. Labelers were instructed to reject line drawings. * The image should contain only one prominent instance of the object to which the class refers. * The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler. Source: https://www.cs.toronto.edu/~kriz/cifar.html Image Source: https://www.cs.toronto.edu/~kriz/cifar.html

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Source: Video Anomaly Dection Dataset

TMED is a clinically-motivated benchmark dataset for computer vision and machine learning from limited labeled data. Two overall goals inspired this dataset: 1) We wish to improve timely diagnosis and treatment of aortic stenosis (AS), a common degenerative cardiac valve condition. AS is a particularly important condition where automation holds substantial promise. Automated screening for AS can increase referral and treatment rates for patients with this life threatening condition. 2) We wish to provide an authentic assessment of semi-supervised learning (SSL) methods to the computer vision and ML research community. Especially in medical contexts, labels are often difficult and expensive to acquire. SSL is promising way to combine a small labeled set (images plus expert annotations) with a large, easy-to-acquire unlabeled set (images only). However, most existing benchmark datasets don't represent the challenges of truly uncurated unlabeled sets in a medical context. We hope our data release catalyzes work on methods for effective multi-task SSL. The dataset is available for academic use to any researcher who applies for access on our website and agrees to a standard data use agreement (do not share the data with non-approved users, no commercial use, no attempt to reidentify patients, etc.). ### Dataset contents The TMED dataset contains imagery from 2773 patients and supervised labels for two classification tasks from a small subset of 260 patients (because labels are difficult to acquire). All data is de-identified and approved for release by our IRB. Imagery comes from transthoracic echocardiograms acquired in the course of routine care consistent with American Society of Echocardiography (ASE) guidelines, all obtained from 2015-2020 at Tufts Medical Center. When gathering echocardiogram imagery for each patient, a sonographer manipulates a handheld transducer over the patient’s chest, manually choosing different acquisition angles in order to fully assess the heart’s complex anatomy. This imaging process results in multiple cineloop video clips of the heart depicting various anatomical views. We extract one still image from each available video clip, so each patient study is represented in our dataset as multiple images (typically ~100). Each image is preprocessed to a grayscale 64x64 PNG. Two kinds of labels are available for the labeled subset of patients: * View labels (PLAX/PSAX/Other), indicating which standard anatomical view is shown by the image. Each image in our fully-labeled set is annotated. * Diagnostic labels (no AS, mild/moderate AS, severe AS), indicating the severity of disease. Each patient in our fully-labeled set is annotated. For more information, see our website and our published paper at MLHC '21

The Ecoli dataset is a dataset for protein localization. It contains 336 E.coli proteins split into 8 different classes.

DEIC is a benchmark for measuring the data efficiency of models in the context of image classification. It is composed of 6 datasets that contain a small number of training samples per class (i.e., 30 < x < 80). It covers multiple image domains (i.e., natural images, fine-grained recognition, medical images, remote sensing, handwriting recognition) and data types (i.e., RGB, grayscale, multi-spectral).

This ImageNet version contains only 50 training images per class while the original testing set remains unchanged. It is one of the datasets comprising the data-efficient image classification (DEIC) benchmark. It was proposed to challenge the generalization capabilities of modern image classifiers.

WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level. In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.