Fix number expression
@@ -261,7 +261,7 @@ class ResNet50Layers(ResNetLayers):
model that can be specified in the constructor,
please use ``convert_caffemodel_to_npz`` classmethod instead.
- ResNet50 has 25557096 trainable parameters, and it's 58% and 43% fewer
+ ResNet50 has 25,557,096 trainable parameters, and it's 58% and 43% fewer
than ResNet101 and ResNet152, respectively. On the other hand, the top-5
classification accuracy on ImageNet dataset drops only 0.7% and 1.1% from
ResNet101 and ResNet152, respectively. Therefore, ResNet50 may have the
@@ -318,7 +318,7 @@ class ResNet101Layers(ResNetLayers):
model that can be specified in the constructor,
please use ``convert_caffemodel_to_npz`` classmethod instead.
- ResNet101 has 44549224 trainable parameters, and it's 43% fewer than
+ ResNet101 has 44,549,224 trainable parameters, and it's 43% fewer than
ResNet152 model, while the top-5 classification accuracy on ImageNet
dataset drops 1.1% from ResNet152. For many cases, ResNet50 may have the
best balance between the accuracy and the model size.
@@ -371,7 +371,7 @@ class ResNet152Layers(ResNetLayers):
model that can be specified in the constructor,
please use ``convert_caffemodel_to_npz`` classmethod instead.
- ResNet152 has 60192872 trainable parameters, and it's the deepest ResNet
+ ResNet152 has 60,192,872 trainable parameters, and it's the deepest ResNet
model and it achieves the best result on ImageNet classification task in
`ILSVRC 2015 <http://image-net.org/challenges/LSVRC/2015/results#loc>`_.
