readme updated

README.md

@@ -15,7 +15,7 @@ Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and
 1. Clone this repository.
 2. Enter the cloned folder.
 3. Download VGG19 network from "https://drive.google.com/open?id=0B_B_FOgPxgFLRjdEdE9NNTlzUWc" and  run  "mv ./imagenet-vgg-verydeep-19.mat ./python_code/Models"
-4. Download all the results and intermediate result from "https://drive.google.com/open?id=1UFaRl_3EGK0hSBlmB6AJVeZZ_r-SHQLm", run "unzip result.zip" (The final result is saved in folder "all_results", and the intermediate result data is saved in folder "result")
+4. Download all the results and intermediate result from "https://drive.google.com/open?id=1UFaRl_3EGK0hSBlmB6AJVeZZ_r-SHQLm", and run "unzip result.zip" (The final result is saved in folder "all_results", and the intermediate result data is saved in folder "result")
 5. Download the pretrained models (trainedmodels.zip) from "https://drive.google.com/open?id=1MgEHYbMM7DAwoFpaH0Z5IRglFzTT6Fd5". It takes several minutes to download all the models.
 6. Download the test data (testdata.zip) from "https://drive.google.com/open?id=1w7Nk3U8lKGlCDch_tn_hu9bpWZYLdhbN". It is approximately 11Gb in size. 
 7. Download the training data (traindata.zip) from "https://drive.google.com/open?id=1UoZ2_TnA-IrRLtgvvznUO3VZbOwXwZI-". It is approximately 60Gb in size