This paper proposes a discriminatively learned CNN embedding for person re-identification (re-ID). The authors revisit two popular CNN models for re-ID: verification and identification models. These models have different loss functions and advantages. The authors propose a siamese network that simultaneously computes identification loss and verification loss. The network predicts the identities of two input images and whether they belong to the same identity. The network learns a discriminative embedding and a similarity measurement, making full use of re-ID annotations. The proposed method can be applied to different pre-trained networks and improves state-of-the-art performance on two public re-ID benchmarks. The architecture is also applicable to image retrieval. The paper presents a siamese network that combines the strengths of verification and identification models to learn more discriminative pedestrian embeddings. The network uses two losses: identification loss and verification loss. It is trained on two large-scale re-ID datasets (Market1501 and CUHK03) and one instance retrieval dataset (Oxford5k). The results show that the proposed method outperforms existing methods in terms of accuracy and mAP. The network is evaluated on two large-scale datasets: Market1501 and CUHK03. The results show that the proposed method achieves higher accuracy and mAP compared to existing methods. The network is also tested on the Oxford5k dataset for image retrieval, and it performs well. The proposed method is compared with other state-of-the-art methods and shows superior performance. The network is also evaluated on a large-scale dataset with 500k images, and it still achieves good performance. The results show that the proposed method is effective for person re-identification and image retrieval. The paper concludes that the proposed method is a promising approach for person re-identification and image retrieval.This paper proposes a discriminatively learned CNN embedding for person re-identification (re-ID). The authors revisit two popular CNN models for re-ID: verification and identification models. These models have different loss functions and advantages. The authors propose a siamese network that simultaneously computes identification loss and verification loss. The network predicts the identities of two input images and whether they belong to the same identity. The network learns a discriminative embedding and a similarity measurement, making full use of re-ID annotations. The proposed method can be applied to different pre-trained networks and improves state-of-the-art performance on two public re-ID benchmarks. The architecture is also applicable to image retrieval. The paper presents a siamese network that combines the strengths of verification and identification models to learn more discriminative pedestrian embeddings. The network uses two losses: identification loss and verification loss. It is trained on two large-scale re-ID datasets (Market1501 and CUHK03) and one instance retrieval dataset (Oxford5k). The results show that the proposed method outperforms existing methods in terms of accuracy and mAP. The network is evaluated on two large-scale datasets: Market1501 and CUHK03. The results show that the proposed method achieves higher accuracy and mAP compared to existing methods. The network is also tested on the Oxford5k dataset for image retrieval, and it performs well. The proposed method is compared with other state-of-the-art methods and shows superior performance. The network is also evaluated on a large-scale dataset with 500k images, and it still achieves good performance. The results show that the proposed method is effective for person re-identification and image retrieval. The paper concludes that the proposed method is a promising approach for person re-identification and image retrieval.