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Context. Convolutional neural networks (CNNs) have been established as thego-to method for fast object detection and classification on natural images.This opens the door for astrophysical parameter inference on the exponentiallyincreasing amount of sky survey data. Until now, star cluster analysis wasbased on integral or resolved stellar photometry, which limits the amount ofinformation that can be extracted from individual pixels of cluster images.  Aims. We aim to create a CNN capable of inferring star cluster evolutionary,structural, and environmental parameters from multi-band images, as well todemonstrate its capabilities in discriminating genuine clusters from galacticstellar backgrounds.  Methods. A CNN based on the deep residual network (ResNet) architecture wascreated and trained to infer cluster ages, masses, sizes, and extinctions, withrespect to the degeneracies between them. Mock clusters placed on M83 HubbleSpace Telescope (HST) images utilizing three photometric passbands (F336W,F438W, and F814W) were used. The CNN is also capable of predicting thelikelihood of a cluster's presence in an image, as well as quantifying itsvisibility (signal-to-noise).  Results. The CNN was tested on mock images of artificial clusters and hasdemonstrated reliable inference results for clusters of ages $\lesssim$100 Myr,extinctions $A_V$ between 0 and 3 mag, masses between $3\times10^3$ and$3\times10^5$ ${\rm M_\odot}$, and sizes between 0.04 and 0.4 arcsec at thedistance of the M83 galaxy. Real M83 galaxy cluster parameter inference testswere performed with objects taken from previous studies and have demonstratedconsistent results.

Deriving star cluster parameters with convolutional neural networks. II. Extinction and cluster/background classification