  We propose a Small, Accurate, and Fast Re-ID (SAFR) design for flexible
vehicle re-id under a variety of compute environments such as cloud, mobile,
edge, or embedded devices by only changing the re-id model backbone. Through
best-fit design choices, feature extraction, training tricks, global attention,
and local attention, we create a reid model design that optimizes
multi-dimensionally along model size, speed, & accuracy for deployment under
various memory and compute constraints. We present several variations of our
flexible SAFR model: SAFR-Large for cloud-type environments with large compute
resources, SAFR-Small for mobile devices with some compute constraints, and
SAFR-Micro for edge devices with severe memory & compute constraints.
  SAFR-Large delivers state-of-the-art results with mAP 81.34 on the VeRi-776
vehicle re-id dataset (15% better than related work). SAFR-Small trades a 5.2%
drop in performance (mAP 77.14 on VeRi-776) for over 60% model compression and
150% speedup. SAFR-Micro, at only 6MB and 130MFLOPS, trades 6.8% drop in
accuracy (mAP 75.80 on VeRi-776) for 95% compression and 33x speedup compared
to SAFR-Large.
