The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube.
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CI-MNIST (Correlated and Imbalanced MNIST) is a variant of MNIST dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image $x$, the label $y \in \{1, 0\}$ indicates eligibility or ineligibility, respectively, given that $x$ is even or odd. The dataset defines the background colors as the protected or sensitive attribute $s \in \{0, 1\}$, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations.
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Problem Statement
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Imbalanced-MiniKinetics200 was proposed by "Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition" to evaluate varying scenarios of video long-tailed recognition. Similar to CIFAR-10/100-LT, it utilizes an imbalance factor to construct long-tailed variants of the MiniKinetics200 dataset. Imbalanced-MiniKinetics200 is a subset of Mini-Kinetics-200 consisting of 200 categories which is also a subset of Kinetics400. Both the raw frames and extracted features with ResNet50/101 are provided.
This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices.
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.
It is a competition on kaggle with stroke Prediction, which is heavily imbalanced.