ref to paper

keras_contrib/losses/jaccard.py

@@ -3,14 +3,20 @@ from keras import backend as K
 
 def jaccard_distance(y_true, y_pred, smooth=100):
     """
-    Jaccard distance is a usefull loss when you have unbalanced classes within a sample
-    because it gives all classes equal weight.
+    Jaccard distance is a intersection-over-union loss which is not the defacto
+    standard for image segementation. It's a usefull loss when you have
+    unbalanced numbers of pixels within an image because it gives all classes
+    equal weight.
 
     For example, you are trying to predict if each pixel is cat, dog, or background.
     You have 80% background pixels, 10% dog, and 10% cat. If the model predicts 100% background
     should it be be 80% right (as with categorical cross entropy) or 30% (with this loss)?
 
     The loss has been modified to have a smooth gradient as it converges on zero.
+
+    Refs:
+    - Csurka, Gabriela & Larlus, Diane & Perronnin, Florent. (2013). What is a good evaluation measure for semantic segmentation?. IEEE Trans. Pattern Anal. Mach. Intell.. 26. . 10.5244/C.27.32. 
+
     """
     intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
     sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)