Update README.md

README.md

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 Code for the paper: On Symmetric Losses for Learning from Corrupted Labels
 Authors: Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
 
-Implementation for experiments with cifar-10 and mnist
+Implementation for experiments with Cifar-10 and Mnist
 Paper link: [ArXiv](https://arxiv.org/abs/1901.09314)
 
 ## Usage
 
 * install dependency : Python 3.5+, Pytorch 1.0, numpy, PIL, sklearn.
 * run `python main.py --data cifar-10 --epoch 50 --prior 0.65 --opt auc`, which is default.
-* data : mnist (odd vs even), cifar-10 (airplane vs horse)
+* data : Mnist (Odd vs Even), Cifar-10 (Airplane vs Horse)
 * prior : one of (1.0, 0.0), (0.8, 0.3), (0.7, 0.4), (0.65, 0.45)
-* opt : ber (balanced error rate minimization), auc (area under the receiver operating characteristic curve maximization)
+* opt : BER (balanced error rate minimization), AUC (area under the receiver operating characteristic curve maximization)
 
 
 ## Reference