  We apply object detection techniques based on Convolutional Neural Networks
to jet reconstruction and identification at the CERN Large Hadron Collider. In
particular, we focus on CaloJet reconstruction, representing each event as an
image composed of calorimeter cells and using a Single Shot Detection network,
called Jet-SSD. The model performs simultaneous localization and classification
and additional regression tasks to measure jet features. We investigate Ternary
Weight Networks with weights constrained to {-1, 0, 1} times a layer- and
channel-dependent scaling factors. We show that the quantized version of the
network closely matches the performance of its full-precision equivalent.
