Update README.md

README.md

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 # DIQA
-Pytorch version of the CVPR2014 paper:Deep CNN-Based Blind Image Quality Predictor
+Pytorch version of IEEE Transactions on Image Processing 2019 : [J. Kim, A. Nguyen and S. Lee, "Deep CNN-Based Blind Image Quality Predictor," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 11-24, Jan. 2019, doi: 10.1109/TNNLS.2018.2829819.](https://ieeexplore.ieee.org/document/8383698)
+
+# Note
+1. Some training details differ from the original paper, if you want to be consistent with the original pape, make some changes.
+2. This training progress only support on LIVE II database now, the training progress on TID2013, CSIQ, LIVEMD, CLIVE will be released soon.
+
+# Train
+1. For the Step 1 training, run `python train_step1.py`
+1. For the Step 2 training, run `python train_step2.py`
+
+# TODO
+* Cross dataset test code will be published
+* Train on different distortion types on LIVE, TID2013, CSIQ will be published
+* Code of evaluations on Waterloo Exploration Database (D-test, L-test, P-test and gMAd competition) will be published