论文标题
边缘小小，准确且快速的车辆重新固定：SAFR接近
Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach
论文作者
论文摘要
我们仅通过更改重新ID模型骨架来提出在各种计算环境（例如云，移动，边缘或嵌入式设备）下，针对灵活车辆重新ID的小型，准确，快速的重新ID（SAFR）设计。通过最佳合适的设计选择，功能提取，训练技巧，全球关注和本地关注，我们创建了REID模型设计，该设计沿模型大小，速度和精度在各种内存和计算约束下进行部署优化。我们提供了灵活的SAFR模型的几种变体：具有大量计算资源的云类型环境的SAFR-LARGE，具有一些计算限制的移动设备的SAFR-SMALL，以及具有严重内存和计算约束的边缘设备的SAFR-Micro。 SAFR-LARGE在Veri-776车辆重新ID数据集上使用MAP 81.34提供最先进的结果（比相关工作好15％）。 Safr-Small的性能下降了5.2％（Veri-776上的地图77.14），以超过60％的模型压缩和150％的加速。 SAFR-Micro仅为6MB和130毫米，与SAFR-LARGE相比，精度下降了6.8％（Veri-776上的MAP 75.80）的压缩和33倍的速度。
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.
