Context : Convolutional neural networks ( CNNs ) have been established as the go-to method for fast object detection and classification on natural images .
This opens the door for astrophysical parameter inference on the exponentially increasing amount of sky survey data .
Until now , star cluster analysis was based on integral or resolved stellar photometry , which limits the amount of information 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 to demonstrate its capabilities in discriminating genuine clusters from galactic stellar backgrounds .

Methods : A CNN based on the deep residual network ( ResNet ) architecture was created and trained to infer cluster ages , masses , sizes , and extinctions , with respect to the degeneracies between them .
Mock clusters placed on M83 Hubble Space Telescope ( HST ) images utilizing three photometric passbands ( F336W , F438W , and F814W ) were used .
The CNN is also capable of predicting the likelihood of a cluster ’ s presence in an image , as well as quantifying its visibility ( signal-to-noise ) .

Results : The CNN was tested on mock images of artificial clusters and has demonstrated reliable inference results for clusters of ages \lesssim 100 Myr , extinctions A _ { V } between 0 and 3 mag , masses between 3 \times 10 ^ { 3 } and 3 \times 10 ^ { 5 } { M _ { \odot } } , and sizes between 0.04 and 0.4 arcsec at the distance of the M83 galaxy .
Real M83 galaxy cluster parameter inference tests were performed with objects taken from previous studies and have demonstrated consistent results .

Conclusions :