master into dev

CONTENTS.md

@@ -1,4 +1,14 @@
 ## Library contents
+
+### [Datasets](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets)
+| Name | Reference Papers |
+|---|---|
+| [**CUB**](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#cub-200-2011) | [The caltech-ucsd birds-200-2011 dataset](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf)
+| [**Cars196**](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#cars196) | 3D Object Representations for Fine-Grained Categorization
+| [**INaturalist2018**](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#inaturalist2018) | [The iNaturalist Species Classification and Detection Dataset](https://openaccess.thecvf.com/content_cvpr_2018/papers/Van_Horn_The_INaturalist_Species_CVPR_2018_paper.pdf)
+| [**StanfordOnlineProducts**](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#stanfordonlineproducts) | [Deep Metric Learning via Lifted Structured Feature Embedding](https://cvgl.stanford.edu/papers/song_cvpr16.pdf)
+
+
 ### [Distances](https://kevinmusgrave.github.io/pytorch-metric-learning/distances)
 | Name | Reference Papers |
 |---|---|
@@ -40,6 +50,7 @@
 | [**SphereFaceLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#spherefaceloss) | [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/pdf/1704.08063.pdf)
 | [**SubCenterArcFaceLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#subcenterarcfaceloss) | [Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560715.pdf)
 | [**SupConLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#supconloss) | [Supervised Contrastive Learning](https://arxiv.org/abs/2004.11362)
+| [**ThresholdConsistentMarginLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#thresholdconsistentmarginloss) | [Threshold-Consistent Margin Loss for Open-World Deep Metric Learning](https://arxiv.org/pdf/2307.04047)
 | [**TripletMarginLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#tripletmarginloss) | [Distance Metric Learning for Large Margin Nearest Neighbor Classification](https://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
 | [**TupletMarginLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#tupletmarginloss) | [Deep Metric Learning with Tuplet Margin Loss](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Deep_Metric_Learning_With_Tuplet_Margin_Loss_ICCV_2019_paper.pdf)
 | [**VICRegLoss**](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#vicregloss) | [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://arxiv.org/pdf/2105.04906.pdf)

README.md

@@ -18,15 +18,17 @@
 
 ## News
 
-**July 24**: v2.6.0
-- Changed the `emb` argument of `DistributedLossWrapper.forward` to `embeddings` to be consistent with the rest of the library.
-- Added a warning and early-return when `DistributedLossWrapper` is being used in a non-distributed setting.
-- Thank you [elisim](https://github.com/elisim).
-
-**April 1**: v2.5.0
-- Improved `get_all_triplets_indices` so that large batch sizes don't trigger the `INT_MAX` error.
-- See the [release notes](https://github.com/KevinMusgrave/pytorch-metric-learning/releases/tag/v2.5.0).
-- Thank you [mkmenta](https://github.com/mkmenta).
+**December 11**: v2.8.0
+- Added the [Datasets](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets) module for easy downloading of common datasets:
+  - [CUB200](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#cub-200-2011)
+  - [Cars196](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#cars196)
+  - [INaturalist 2018](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#inaturalist2018)
+  - [Stanford Online Products](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets/#stanfordonlineproducts)
+- Thank you [ir2718](https://github.com/ir2718).
+
+**November 2**: v2.7.0
+- Added [ThresholdConsistentMarginLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#thresholdconsistentmarginloss).
+- Thank you [ir2718](https://github.com/ir2718).
 
 ## Documentation
 - [**View the documentation here**](https://kevinmusgrave.github.io/pytorch-metric-learning/)
@@ -229,6 +231,7 @@ Thanks to the contributors who made pull requests!
 |[domenicoMuscill0](https://github.com/domenicoMuscill0)| - [ManifoldLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#manifoldloss) <br/> - [P2SGradLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#p2sgradloss) <br/> - [HistogramLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#histogramloss) <br/> - [DynamicSoftMarginLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#dynamicsoftmarginloss) <br/> - [RankedListLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#rankedlistloss) |
 |[mlopezantequera](https://github.com/mlopezantequera) | - Made the [testers](https://kevinmusgrave.github.io/pytorch-metric-learning/testers) work on any combination of query and reference sets <br/> - Made [AccuracyCalculator](https://kevinmusgrave.github.io/pytorch-metric-learning/accuracy_calculation/) work with arbitrary label comparisons |
 |[cwkeam](https://github.com/cwkeam) | - [SelfSupervisedLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#selfsupervisedloss) <br/> - [VICRegLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#vicregloss) <br/> - Added mean reciprocal rank accuracy to [AccuracyCalculator](https://kevinmusgrave.github.io/pytorch-metric-learning/accuracy_calculation/) <br/> - BaseLossWrapper|
+| [ir2718](https://github.com/ir2718) | - [ThresholdConsistentMarginLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#thresholdconsistentmarginloss)  <br/> - The [Datasets](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets) module |
 |[marijnl](https://github.com/marijnl)| - [BatchEasyHardMiner](https://kevinmusgrave.github.io/pytorch-metric-learning/miners/#batcheasyhardminer) <br/> - [TwoStreamMetricLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/trainers/#twostreammetricloss) <br/> - [GlobalTwoStreamEmbeddingSpaceTester](https://kevinmusgrave.github.io/pytorch-metric-learning/testers/#globaltwostreamembeddingspacetester) <br/> - [Example using trainers.TwoStreamMetricLoss](https://github.com/KevinMusgrave/pytorch-metric-learning/blob/master/examples/notebooks/TwoStreamMetricLoss.ipynb) |
 | [chingisooinar](https://github.com/chingisooinar) | [SubCenterArcFaceLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#subcenterarcfaceloss) |
 | [elias-ramzi](https://github.com/elias-ramzi) | [HierarchicalSampler](https://kevinmusgrave.github.io/pytorch-metric-learning/samplers/#hierarchicalsampler) |