The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Deep Metric Learning

PyTorch Implementation for Deep Metric Learning Pipelines

(ICML 2020) This repo contains code for our paper "Revisiting Training Strategies and Generalization Performance in Deep Metric Learning" (https://arxiv.org/abs/2002.08473) to facilitate consistent research in the field of Deep Metric Learning.

Official PyTorch Implementation of Proxy Anchor Loss for Deep Metric Learning, CVPR 2020

A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).

[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning

图像检索和向量搜索，similarity learning,compare deep metric and deep-hashing applying in image retrieval

Authors official Tensorflow implementation of the "Near-Duplicate Video Retrieval with Deep Metric Learning" [ICCVW 2017]

Code for CVPR 2019 paper "Deep Metric Learning to Rank"

[ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching

A simple, modern and scalable facial recognition based attendance system built with Python back-end & Angular front-end.

End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)

PyTorch implementation of Deep Randomized Ensembles for Metric Learning(ECCV2018)

CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission

Dogs classification with Deep Metric Learning

(ICCV 2019) This repo contains code for "MIC: Mining Interclass Characteristics for Improved Metric Learning", which proposes an auxiliary training task to explain away intra-class variations.

(CVPR 2020) This repo contains code for "PADS: Policy-Adapted Sampling for Visual Similarity Learning", which proposes learnable triplet mining with Reinforcement Learning.

Official PyTorch Implementation of ProxyGML Loss for Deep Metric Learning, NeurIPS 2020 (spotlight)

pytorch implement of this paper: https://arxiv.org/abs/1807.11176