text: Title: Help add more datasets to our Data-Centric AI benchmark
  Body: Good benchmarking is critical for the success of our tools!  Our benchmarks are very unique compared to standard ML benchmarks which assess test accuracy.  Instead our benchmarks assess how accurately different algorithms can identify issues in the data.  Adding new datasets to our benchmarks and re-running them could make very interesting material for a blogpost!
  
  In particular, our label error detection benchmark could greatly benefit from the addition of more datasets:
  https://github.com/cleanlab/label-error-detection-benchmarks
  
  It should be pretty evident how to run the existing benchmark code on a new image dataset once it has been added. 
  Some ideas for good datasets to add to this benchmark include:
  
  * Clothing1M:  https://paperswithcode.com/dataset/clothing1m
  * Animal-10N:  https://arxiv.org/abs/2103.07756
  * Cifar-10H:  https://github.com/jcpeterson/cifar-10h  (as given label for each example, can take the worst label chosen across any of the annotators)
  * Cats4ML:  https://github.com/greg1232/dataperf-vision-creation
  
  Our Out-of-Distribution benchmark could also benefit from the addition of more dataset pairs which are standard in the OOD research community:  https://github.com/cleanlab/ood-detection-benchmarks