add and example of calculating #epochs

docs/getting_started.md

@@ -31,7 +31,15 @@ To try other data partitions beside 1/8, you just need to change two variables i
 C.labeled_ratio = 8
 C.nepochs = 34
 ```
-Please note that, for fair comparison, we control the total iterations during training in each experiment similar (almost the same), including the supervised baseline and semi-supervised methods. Therefore, the nepochs for different partitions are different. We list the nepochs for different datasets and partitions in the below.
+Please note that, for fair comparison, we control the total iterations during training in each experiment similar (almost the same), including the supervised baseline and semi-supervised methods. Therefore, the nepochs for different partitions are different. 
+
+We take VOC as an example.
+1. We totally have 10582 images. The full supervised baseline is trained for 60 epochs with batch size 16, thus having 10582*60/16 = 39682.5 iters.
+2. If we train CPS under the 1/8 split, we have 1323 labeled images and 9259 unlabeled images. Since the number of unlabeled images is larger than the number of labeled images, the `epoch` is defined as passing all the unlabeled images to the network. In each iteration, we have 8 labeled images and 8 unlabeled images, thus having 9259/8 = 1157.375 iters in one epoch. Then the total epochs we need is 39682.5/1157.375 = 34.29 ≈ 34. 
+3. For the supervised baseline under the 1/8 split, the batch size 8 (as illustrated in Appendix A in the paper) and the iteration number is 39682.5 (the same as semi-supervised method).
+
+
+We list the nepochs for different datasets and partitions in the below.
 
 | Dataset    | 1/16 | 1/8  | 1/4  | 1/2  |
 | ---------- | ---- | ---- | ---- | ---- |