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The task of person re-identification (ReID) has attracted growing attention in recent years with improving performance but lack of focus on real-world applications. Most state of the art methods use large pre-trained models, eg, ResNet50 (∼ 25M parameters), as their backbone, which makes it tedious to explore different architecture modifications. In this study, we focus on small-sized randomly initialized models which enable us to easily introduce network and training modifications suitable for person ReID public datasets and real-world setups. We show the robustness of our network and training improvements by outperforming state of the art results in terms of rank-1 accuracy and mAP on Market1501 (96.2, 89.7) and DukeMTMC (89.8, 80.3) with only 6.4 M parameters and without using re-ranking. Finally, we show the applicability of the proposed ReID network for multi-object tracking.