|
|
@@ -21,7 +21,7 @@ which provides working examples of how to use TF-Slim for image classification.
|
|
|
|
|
|
<a href="#Install">Installation and setup</a><br>
|
|
|
<a href='#Data'>Preparing the datasets</a><br>
|
|
|
-<a href='#Pretained'>Using pre-trained models</a><br>
|
|
|
+<a href='#Pretrained'>Using pre-trained models</a><br>
|
|
|
<a href='#Training'>Training from scratch</a><br>
|
|
|
<a href='#Tuning'>Fine tuning to a new task</a><br>
|
|
|
<a href='#Eval'>Evaluating performance</a><br>
|
|
|
@@ -84,6 +84,7 @@ python -c "from nets import cifarnet; mynet = cifarnet.cifarnet"
|
|
|
|
|
|
|
|
|
# Preparing the datasets
|
|
|
+<a id='Data'></a>
|
|
|
|
|
|
As part of this library, we've included scripts to download several popular
|
|
|
image datasets (listed below) and convert them to slim format.
|
Sergio Guadarrama