Update README.md

Author: hellowangqian

README.md

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 # multi-label-image-classification
 A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN
 ## Code description
-1. Codes tested with PyTorch 0.4.
+1. Code tested with PyTorch 0.4.
 
-2. Model2 (M2) and model3 (M3) appearing in the paper could be adapted from model1 script by uncommenting corresponding lines for randomcropping and mixup.
+2. Model2 (M2) and model3 (M3) appearing in the paper could be adapted from model1 code by uncommenting corresponding lines for randomcropping and mixup.
 
 3. To run a script using: python resnet101_model1fc.py 1 512 16 (three arguments are trial index, patch size, batch size)
 
 4. The evaluation metrics for VOC2007 are slightly different from those for NUS-WIDE and MS-COCO since there are "difficult examples" in the annotations which are ignored when evaluating.
 
-5. We use all training data to train the model.
+5. We use all training data to train the model and a fixed criterion for training stop.
 
 ## Data
-To run the scripts you might need to download images for three datasets from their official websites.
+To run the code you might need to download images for three datasets from their official websites or contact me directly.
 
 ## Reference
-Qian Wang, Ning Jia, Toby P. Breckon, A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN, ICIP 2019.
+Qian Wang, Ning Jia, Toby P. Breckon, A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN, IEEE International Conference on Image Processing 2019, Taipei.
 ## Contact
 [email protected]