Open Metric Learning
4.0.0

OML

  • Installation
  • FAQ
  • Contributing guide
  • Dataset format
  • Pipelines
  • Logging & Visualization

Features extraction

  • Examples
  • Zoo
  • Pipelines: features extraction

Postprocessing

  • Re-ranking by Siamese model
  • Algorithmic postprocessing

Contents

  • Base Interfaces
  • Datasets
  • Samplers
  • Miners
  • Losses
  • Models
  • Metrics
  • PyTorch Lightning
  • Utils
  • DDP
  • Retrieval & Post-processing
Open Metric Learning
  • »
  • Overview: module code

All modules for which code is available

  • oml.datasets.audios
  • oml.datasets.images
  • oml.datasets.pairs
  • oml.datasets.texts
  • oml.ddp.patching
  • oml.ddp.utils
  • oml.functional.label_smoothing
  • oml.functional.metrics
  • oml.interfaces.criterions
  • oml.interfaces.datasets
  • oml.interfaces.metrics
  • oml.interfaces.miners
  • oml.interfaces.models
  • oml.interfaces.retrieval
  • oml.interfaces.samplers
  • oml.losses.arcface
  • oml.losses.surrogate_precision
  • oml.losses.triplet
  • oml.metrics.embeddings
  • oml.miners.cross_batch
  • oml.miners.inbatch_all_tri
  • oml.miners.inbatch_hard_cluster
  • oml.miners.inbatch_hard_tri
  • oml.miners.inbatch_nhard_tri
  • oml.miners.miner_with_bank
  • oml.models.meta.projection
  • oml.models.meta.siamese
  • oml.models.resnet.extractor
  • oml.models.vit_clip.extractor
  • oml.models.vit_dino.extractor
  • oml.retrieval.postprocessors.algo
  • oml.retrieval.postprocessors.pairwise
  • oml.retrieval.retrieval_results
  • oml.samplers.balance
  • oml.samplers.category_balance
  • oml.samplers.distinct_category_balance
  • oml.utils.dataframe_format
  • oml.utils.download_mock_dataset
  • oml.utils.misc_torch

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