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slim/README.md
@@ -13,7 +13,7 @@ converting them
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to TensorFlow's native TFRecord format and reading them in using TF-Slim's
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data reading and queueing utilities. You can easily train any model on any of
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these datasets, as we demonstrate below. We've also included a
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-[jupyter notebook](https://github.com/tensorflow/models/tree/master/slim/slim_walkthrough.ipynb),
+[jupyter notebook](https://github.com/tensorflow/models/blob/master/slim/slim_walkthough.ipynb),
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which provides working examples of how to use TF-Slim for image classification.
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