  The next generation of wide-field deep astronomical surveys will deliver
unprecedented amounts of images through the 2020s and beyond. As both the
sensitivity and depth of observations increase, more blended sources will be
detected. This reality can lead to measurement biases that contaminate key
astronomical inferences. We implement new deep learning models available
through Facebook AI Research's Detectron2 repository to perform the
simultaneous tasks of object identification, deblending, and classification on
large multi-band coadds from the Hyper Suprime-Cam (HSC). We use existing
detection/deblending codes and classification methods to train a suite of deep
neural networks, including state-of-the-art transformers. Once trained, we find
that transformers outperform traditional convolutional neural networks and are
more robust to different contrast scalings. Transformers are able to detect and
deblend objects closely matching the ground truth, achieving a median bounding
box Intersection over Union of 0.99. Using high quality class labels from the
Hubble Space Telescope, we find that the best-performing networks can classify
galaxies with near 100\% completeness and purity across the whole test sample
and classify stars above 60\% completeness and 80\% purity out to HSC i-band
magnitudes of 25 mag. This framework can be extended to other upcoming deep
surveys such as the Legacy Survey of Space and Time and those with the Roman
Space Telescope to enable fast source detection and measurement. Our code,
\textsc{DeepDISC} is publicly available at
\url{https://github.com/grantmerz/deepdisc}.
