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@@ -24,7 +24,7 @@ model architecture.
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## Description of Code
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-NOTE: For the most part, you will find a newer version of this code at [models/slim](https://github.com/tensorflow/models/tree/master/slim). In particular:
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+**NOTE**: For the most part, you will find a newer version of this code at [models/slim](https://github.com/tensorflow/models/tree/master/slim). In particular:
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* `inception_train.py` and `imagenet_train.py` should no longer be used. The slim editions for running on multiple GPUs are the current best examples.
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* `inception_distributed_train.py` and `imagenet_distributed_train.py` are still valid examples of distributed training.
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@@ -61,11 +61,6 @@ subsequent research.
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## Getting Started
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-**NOTE** Before doing anything, we first need to build TensorFlow from source,
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-and installed as a PIP package. Please follow the instructions at [Installing
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-From Source]
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-(https://www.tensorflow.org/install/install_sources).
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-
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Before you run the training script for the first time, you will need to download
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and convert the ImageNet data to native TFRecord format. The TFRecord format
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consists of a set of sharded files where each entry is a serialized `tf.Example`
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@@ -639,9 +634,9 @@ reside within `$TRAIN_DIR` and `$VALIDATION_DIR` arranged as such:
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$VALIDATION_DIR/cat/cat.JPG
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...
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```
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-**NOTE** This script will append an extra background class indexed at 0, so your
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-class labels will range from [0, num_labels]. Using the example above, the
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-corresponding class labels generated from `build_image_data.py` will be as follows:
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+**NOTE**: This script will append an extra background class indexed at 0. Using the
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+example above, the corresponding class labels generated from `build_image_data.py`
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+will be as follows:
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```shell
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0
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1 dog
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Neal Wu