CONTENTS.md

August 17, 2025 ยท View on GitHub

Library contents

Datasets

NameReference Papers
CUBThe caltech-ucsd birds-200-2011 dataset
Cars1963D Object Representations for Fine-Grained Categorization
INaturalist2018The iNaturalist Species Classification and Detection Dataset
StanfordOnlineProductsDeep Metric Learning via Lifted Structured Feature Embedding

Distances

NameReference Papers
CosineSimilarity
DotProductSimilarity
LpDistance
SNRDistanceSignal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning

Losses

NameReference Papers
AngularLossDeep Metric Learning with Angular Loss
ArcFaceLossArcFace: Additive Angular Margin Loss for Deep Face Recognition
CircleLossCircle Loss: A Unified Perspective of Pair Similarity Optimization
ContrastiveLossDimensionality Reduction by Learning an Invariant Mapping
CosFaceLoss- CosFace: Large Margin Cosine Loss for Deep Face Recognition
- Additive Margin Softmax for Face Verification
DynamicSoftMarginLossLearning Local Descriptors With a CDF-Based Dynamic Soft Margin
FastAPLossDeep Metric Learning to Rank
GeneralizedLiftedStructureLossIn Defense of the Triplet Loss for Person Re-Identification
HistogramLossLearning Deep Embeddings with Histogram Loss
InstanceLossDual-Path Convolutional Image-Text Embeddings with Instance Loss
IntraPairVarianceLossDeep Metric Learning with Tuplet Margin Loss
LargeMarginSoftmaxLossLarge-Margin Softmax Loss for Convolutional Neural Networks
LiftedStructreLossDeep Metric Learning via Lifted Structured Feature Embedding
ManifoldLossEnsemble Deep Manifold Similarity Learning using Hard Proxies
MarginLossSampling Matters in Deep Embedding Learning
MultiSimilarityLossMulti-Similarity Loss with General Pair Weighting for Deep Metric Learning
NCALossNeighbourhood Components Analysis
NormalizedSoftmaxLoss- NormFace: L2 Hypersphere Embedding for Face Verification
- Classification is a Strong Baseline for DeepMetric Learning
NPairsLossImproved Deep Metric Learning with Multi-class N-pair Loss Objective
NTXentLoss- Representation Learning with Contrastive Predictive Coding
- Momentum Contrast for Unsupervised Visual Representation Learning
- A Simple Framework for Contrastive Learning of Visual Representations
P2SGradLossP2SGrad: Refined Gradients for Optimizing Deep Face Models
PNPLossRethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
ProxyAnchorLossProxy Anchor Loss for Deep Metric Learning
ProxyNCALossNo Fuss Distance Metric Learning using Proxies
RankedListLossRanked List Loss for Deep Metric Learning
SignalToNoiseRatioContrastiveLossSignal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning
SmoothAPLossSmooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
SoftTripleLossSoftTriple Loss: Deep Metric Learning Without Triplet Sampling
SphereFaceLossSphereFace: Deep Hypersphere Embedding for Face Recognition
SubCenterArcFaceLossSub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces
SupConLossSupervised Contrastive Learning
ThresholdConsistentMarginLossThreshold-Consistent Margin Loss for Open-World Deep Metric Learning
TripletMarginLossDistance Metric Learning for Large Margin Nearest Neighbor Classification
TupletMarginLossDeep Metric Learning with Tuplet Margin Loss
VICRegLossVICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Miners

NameReference Papers
AngularMiner
BatchEasyHardMinerImproved Embeddings with Easy Positive Triplet Mining
BatchHardMinerIn Defense of the Triplet Loss for Person Re-Identification
DistanceWeightedMinerSampling Matters in Deep Embedding Learning
EmbeddingsAlreadyPackagedAsTriplets
HDCMinerHard-Aware Deeply Cascaded Embedding
MultiSimilarityMinerMulti-Similarity Loss with General Pair Weighting for Deep Metric Learning
PairMarginMiner
TripletMarginMinerFaceNet: A Unified Embedding for Face Recognition and Clustering
UniformHistogramMiner

Reducers

NameReference Papers
AvgNonZeroReducer
ClassWeightedReducer
DivisorReducer
DoNothingReducer
MeanReducer
PerAnchorReducer
ThresholdReducer

Regularizers

NameReference Papers
CenterInvariantRegularizerDeep Face Recognition with Center Invariant Loss
LpRegularizer
RegularFaceRegularizerRegularFace: Deep Face Recognition via Exclusive Regularization
SparseCentersRegularizerSoftTriple Loss: Deep Metric Learning Without Triplet Sampling
ZeroMeanRegularizerSignal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning

Samplers

NameReference Papers
MPerClassSampler
HierarchicalSamplerDeep Metric Learning to Rank
TuplesToWeightsSampler
FixedSetOfTriplets

Trainers

NameReference Papers
MetricLossOnly
TrainWithClassifier
CascadedEmbeddingsHard-Aware Deeply Cascaded Embedding
DeepAdversarialMetricLearningDeep Adversarial Metric Learning
TwoStreamMetricLoss

Testers

NameReference Papers
GlobalEmbeddingSpaceTester
WithSameParentLabelTester
GlobalTwoStreamEmbeddingSpaceTester

Utils

NameReference Papers
AccuracyCalculator
HookContainer
InferenceModel
TorchInitWrapper
DistributedLossWrapper
DistributedMinerWrapper

Base Classes, Mixins, and Wrappers

NameReference Papers
CrossBatchMemoryCross-Batch Memory for Embedding Learning
GenericPairLoss
MultipleLosses
MultipleReducers
EmbeddingRegularizerMixin
WeightMixin
WeightRegularizerMixin
BaseDistance
BaseMetricLossFunction
BaseMiner
BaseReducer
BaseRegularizer
BaseTrainer
BaseTester