This paper introduces a deep metric learning algorithm called Lifted Structured Feature Embedding (LSFE), which improves the performance of deep feature embeddings for learning and visual recognition. The method leverages the pairwise distances within a training batch to construct a matrix of pairwise distances, enabling the algorithm to learn a state-of-the-art feature embedding by optimizing a novel structured prediction objective. The approach is evaluated on three datasets: CUB-200-2011, CARS196, and a newly collected Online Products dataset with 120k images of 23k classes. The results show significant improvements over existing deep feature embedding methods on all experimented embedding sizes using the GoogLeNet network. The paper discusses the importance of learning distance metrics between pairs of examples for learning and visual recognition. It highlights the limitations of existing methods that do not fully utilize training batches during mini-batch stochastic gradient descent. The proposed method addresses these limitations by lifting the vector of pairwise distances within the batch to a matrix of pairwise distances, allowing for a more comprehensive optimization of the structured loss objective. The paper also discusses related works in deep metric learning, deep feature embedding with convolutional neural networks, and zero-shot learning and ranking. It reviews recent works on discriminatively training neural networks to learn semantic embeddings, including contrastive and triplet embeddings. The proposed method introduces a structured loss function based on all positive and negative pairs of samples in the training set, which is optimized using a smooth upper bound and stochastic approach. The paper presents the implementation details, including the use of the Caffe package for training and testing the embedding with contrastive, triplet, and the proposed methods. The experiments show that the proposed method outperforms existing methods on all experimented embedding dimensions. The results are evaluated using clustering and retrieval metrics, including F1, NMI, and Recall@K scores. The paper concludes that the proposed method achieves state-of-the-art performance on all experimented embedding dimensions.This paper introduces a deep metric learning algorithm called Lifted Structured Feature Embedding (LSFE), which improves the performance of deep feature embeddings for learning and visual recognition. The method leverages the pairwise distances within a training batch to construct a matrix of pairwise distances, enabling the algorithm to learn a state-of-the-art feature embedding by optimizing a novel structured prediction objective. The approach is evaluated on three datasets: CUB-200-2011, CARS196, and a newly collected Online Products dataset with 120k images of 23k classes. The results show significant improvements over existing deep feature embedding methods on all experimented embedding sizes using the GoogLeNet network. The paper discusses the importance of learning distance metrics between pairs of examples for learning and visual recognition. It highlights the limitations of existing methods that do not fully utilize training batches during mini-batch stochastic gradient descent. The proposed method addresses these limitations by lifting the vector of pairwise distances within the batch to a matrix of pairwise distances, allowing for a more comprehensive optimization of the structured loss objective. The paper also discusses related works in deep metric learning, deep feature embedding with convolutional neural networks, and zero-shot learning and ranking. It reviews recent works on discriminatively training neural networks to learn semantic embeddings, including contrastive and triplet embeddings. The proposed method introduces a structured loss function based on all positive and negative pairs of samples in the training set, which is optimized using a smooth upper bound and stochastic approach. The paper presents the implementation details, including the use of the Caffe package for training and testing the embedding with contrastive, triplet, and the proposed methods. The experiments show that the proposed method outperforms existing methods on all experimented embedding dimensions. The results are evaluated using clustering and retrieval metrics, including F1, NMI, and Recall@K scores. The paper concludes that the proposed method achieves state-of-the-art performance on all experimented embedding dimensions.