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