[Docs] further detail for the doc for `ClassBalancedDataset`. (#901)

* futher detail for the doc for datasets/dataset_wrappers/ClassBalancedDataset

* fix

mmcls/datasets/dataset_wrappers.py

@@ -175,17 +175,20 @@ class ClassBalancedDataset(object):
 
     1. For each category c, compute the fraction :math:`f(c)` of images that
        contain it.
-    2. For each category c, compute the category-level repeat factor
+    2. For each category c, compute the category-level repeat factor.
 
         .. math::
             r(c) = \max(1, \sqrt{\frac{t}{f(c)}})
-
+       
+       where :math:`t` is `oversample_thr`.
     3. For each image I and its labels :math:`L(I)`, compute the image-level
-       repeat factor
+       repeat factor.
 
         .. math::
             r(I) = \max_{c \in L(I)} r(c)
 
+    Each image repeats :math:`\lceil r(I) \rceil` times.
+
     Args:
         dataset (:obj:`BaseDataset`): The dataset to be repeated.
         oversample_thr (float): frequency threshold below which data is
@@ -214,8 +217,8 @@ class ClassBalancedDataset(object):
         self.flag = np.asarray(flags, dtype=np.uint8)
 
     def _get_repeat_factors(self, dataset, repeat_thr):
-        # 1. For each category c, compute the fraction # of images
-        #   that contain it: f(c)
+        # 1. For each category c, compute the fraction of images
+        #    that contain it: f(c)
         category_freq = defaultdict(int)
         num_images = len(dataset)
         for idx in range(num_images):
@@ -227,15 +230,15 @@ class ClassBalancedDataset(object):
             category_freq[k] = v / num_images
 
         # 2. For each category c, compute the category-level repeat factor:
-        #    r(c) = max(1, sqrt(t/f(c)))
+        #       r(c) = max(1, sqrt(t/f(c)))
         category_repeat = {
             cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
             for cat_id, cat_freq in category_freq.items()
         }
 
         # 3. For each image I and its labels L(I), compute the image-level
-        # repeat factor:
-        #    r(I) = max_{c in L(I)} r(c)
+        #    repeat factor:
+        #       r(I) = max_{c in L(I)} r(c)
         repeat_factors = []
         for idx in range(num_images):
             cat_ids = set(self.dataset.get_cat_ids(idx))