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

Torchreid: Deep learning person re-identification in PyTorch.

:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

Accelerated deep learning R&D

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

Metric learning algorithms in Python

Open source person re-identification library in python

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

In defence of metric learning for speaker recognition

TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

Metric learning and retrieval pipelines, models and zoo.

Blazing fast framework for fine-tuning similarity learning models

https://www.kaggle.com/c/humpback-whale-identification

PyTorch Implementation for Deep Metric Learning Pipelines

Paper List for Contrastive Learning for Natural Language Processing

Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor learning loss"

😎 A curated list of awesome practical Metric Learning and its applications

Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)

This is the implementation of paper <Additive Margin Softmax for Face Verification>

A simple yet effective loss function for face verification.

A library for ML benchmarking. It's powerful.

Official pytorch Implementation of Relational Knowledge Distillation, CVPR 2019

CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

The corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!

(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 source code of "Batch DropBlock Network for Person Re-identification and Beyond" (ICCV 2019)

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

Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019

PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016.

[NAACL'21 & ACL'21] SapBERT: Self-alignment pretraining for BERT & XL-BEL: Cross-Lingual Biomedical Entity Linking.