One-class classifier

A public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. Source: COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

Risholme-2021 contains >3.5K images of strawberries at various growth stages along with anomalous instances. Data collection was performed in the strawberry research farm at the Riseholme campus of the University of Lincoln in UK. For more details, please check out "Homepage" down below.

Simulated pulse Doppler radar signatures for four classes of helicopter-like targets. The classes differ in the number of rotating blades each kind of target carries, thus each class translates into a specific modulation pattern on the Doppler signature. Doppler signatures are a typical feature used to achieve radar targets discrimination. This dataset was generated using a simple open-source MATLAB simulation code, which can be easily modified to generate custom datasets with more classes and increased intra-class diversity. Dataset can be easily used for supervised classification, out-of-distribution detection (near and far), unsupervised learning and modulation pattern segmentation. The code includes the generation of an SPD representation for each signature, thanks to the computation of a covariance matrix, thus allowing for second-order specific data processing (e.g. Riemannian neural network or tangent PCA). Dataset used in the paper Dataset generation code