README

Author: jacobgil

README.md

@@ -2,7 +2,9 @@
 
 This demonstrates pruning a VGG16 based classifier that classifies a small dog/cat dataset.
 
+
 This was able to reduce the CPU runtime by x3 and the model size by x4.
+
 For more details you can read the [blog post](https://jacobgil.github.io/deeplearning/pruning-deep-learning).
 
 At each pruning step 512 filters are removed from the network.
@@ -12,14 +14,21 @@ Usage
 -----
 
 This repository uses the PyTorch ImageFolder loader, so it assumes that the images are in a different directory for each category.
-`Train
+
+Train
+
 ......... dogs
+
 ......... cats
-	`
-`Test
+
+
+Test
+
+
 ......... dogs
+
 ......... cats
-	`
+
 
 The images were taken from [here](https://www.kaggle.com/c/dogs-vs-cats) but you should try training this on your own data and see if it works!
 
@@ -36,8 +45,10 @@ TBD
 In principle this can be done in a single pass.
 
 {% highlight python %}
-for layer_index, filter_index in prune_targets:
-	model = prune_vgg16_conv_layer(model, layer_index, filter_index)
+
+    for layer_index, filter_index in prune_targets:
+    	model = prune_vgg16_conv_layer(model, layer_index, filter_index)
+
 {% endhighlight %}
 
  - Change prune_vgg16_conv_layer to support additional architectures.