Update README.md

README.md

@@ -18,15 +18,18 @@
 
 ## News
 
+**December 11**: v2.8.0
+- Added the [Datasets](https://kevinmusgrave.github.io/pytorch-metric-learning/datasets) module for easy downloading of common datasets:
+  - Cars196
+  - CUB200
+  - INaturalist 2018
+  - Stanford Online Products
+- 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).
 
-**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).
-
 ## Documentation
 - [**View the documentation here**](https://kevinmusgrave.github.io/pytorch-metric-learning/)
 - [**View the installation instructions here**](https://github.com/KevinMusgrave/pytorch-metric-learning#installation)
@@ -228,8 +231,8 @@ 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) |
-| [ir2718](https://github.com/ir2718) | [ThresholdConsistentMarginLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#thresholdconsistentmarginloss) |
 | [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) |
 | [fjsj](https://github.com/fjsj) | [SupConLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#supconloss) |