Shypd.aiThe largest open index of AI toolsDeep-Metric-Learning-Baselines is an open-source PyTorch implementation for deep metric learning pipelines. It offers an easily extendable basic pipeline for testing and implementing methods.
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Deep-Metric-Learning-Baselines provides a comprehensive and easily extendable PyTorch implementation for deep metric learning pipelines. This open-source tool allows developers to test and implement current and new deep metric learning methods. It includes implementations and dataloaders for various loss functions like Triplet Loss, Margin Loss, ProxyNCA, and N-Pair Loss, as well as sampling methods such as Random, Softhard, Semihard, Distance, and N-Pair Sampling. The repository also supports popular datasets like CUB200-2011, CARS196, Stanford Online Products, and In-Shop Clothes, and integrates architectures like ResNet50 and GoogLeNet. It's designed for flexibility, allowing users to extend or test other sampling or loss methods by adding them to the existing framework.
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