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We propose a Small, Accurate, and Fast Re-ID (SAFR) design for flexiblevehicle re-id under a variety of compute environments such as cloud, mobile,edge, or embedded devices by only changing the re-id model backbone. Throughbest-fit design choices, feature extraction, training tricks, global attention,and local attention, we create a reid model design that optimizesmulti-dimensionally along model size, speed, & accuracy for deployment undervarious memory and compute constraints. We present several variations of ourflexible SAFR model: SAFR-Large for cloud-type environments with large computeresources, SAFR-Small for mobile devices with some compute constraints, andSAFR-Micro for edge devices with severe memory & compute constraints.  SAFR-Large delivers state-of-the-art results with mAP 81.34 on the VeRi-776vehicle 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 and150% speedup. SAFR-Micro, at only 6MB and 130MFLOPS, trades 6.8% drop inaccuracy (mAP 75.80 on VeRi-776) for 95% compression and 33x speedup comparedto SAFR-Large.

Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach