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# TensorFlow-Slim image classification model library [TF-slim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim) is a new lightweight high-level API of TensorFlow (`tensorflow.contrib.slim`) for defining, training and evaluating complex models. This directory contains code for training and evaluating several widely used Convolutional Neural Network (CNN) image classification models using TF-slim. It contains scripts that will allow you to train models from scratch or fine-tune them from pre-trained network weights. It also contains code for downloading standard image datasets, converting them to TensorFlow's native TFRecord format and reading them in using TF-Slim's data reading and queueing utilities. You can easily train any model on any of these datasets, as we demonstrate below. We've also included a [jupyter notebook](https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb), which provides working examples of how to use TF-Slim for image classification. For developing or modifying your own models, see also the [main TF-Slim page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim). ## Contacts Maintainers of TF-slim: * Nathan Silberman, github: [nathansilberman](https://github.com/nathansilberman) * Sergio Guadarrama, github: [sguada](https://github.com/sguada) ## Citation "TensorFlow-Slim image classification model library" N. Silberman and S. Guadarrama, 2016. https://github.com/tensorflow/models/tree/master/research/slim ## Table of contents <a href="#Install">Installation and setup</a><br> <a href='#Data'>Preparing the datasets</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> <a href='#Export'>Exporting Inference Graph</a><br> <a href='#Troubleshooting'>Troubleshooting</a><br> # Installation <a id='Install'></a> In this section, we describe the steps required to install the appropriate prerequisite packages. ## Installing latest version of TF-slim TF-Slim is available as `tf.contrib.slim` via TensorFlow 1.0. To test that your installation is working, execute the following command; it should run without raising any errors. ``` python -c "import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once" ``` ## Installing the TF-slim image models library To use TF-Slim for image classification, you also have to install the [TF-Slim image models library](https://github.com/tensorflow/models/tree/master/research/slim), which is not part of the core TF library. To do this, check out the [tensorflow/models](https://github.com/tensorflow/models/) repository as follows: ```bash cd $HOME/workspace git clone https://github.com/tensorflow/models/ ``` This will put the TF-Slim image models library in `$HOME/workspace/models/research/slim`. (It will also create a directory called [models/inception](https://github.com/tensorflow/models/tree/master/research/inception), which contains an older version of slim; you can safely ignore this.) To verify that this has worked, execute the following commands; it should run without raising any errors. ``` cd $HOME/workspace/models/research/slim 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. Dataset | Training Set Size | Testing Set Size | Number of Classes | Comments :------:|:---------------:|:---------------------:|:-----------:|:-----------: Flowers|2500 | 2500 | 5 | Various sizes (source: Flickr) [Cifar10](https://www.cs.toronto.edu/~kriz/cifar.html) | 60k| 10k | 10 |32x32 color [MNIST](http://yann.lecun.com/exdb/mnist/)| 60k | 10k | 10 | 28x28 gray [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/)|1.2M| 50k | 1000 | Various sizes VisualWakeWords|82783 | 40504 | 2 | Various sizes (source: MS COCO) ## Downloading and converting to TFRecord format For each dataset, we'll need to download the raw data and convert it to TensorFlow's native [TFRecord](https://www.tensorflow.org/versions/r0.10/api_docs/python/python_io.html#tfrecords-format-details) format. Each TFRecord contains a [TF-Example](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/core/example/example.proto) protocol buffer. Below we demonstrate how to do this for the Flowers dataset. ```shell $ DATA_DIR=/tmp/data/flowers $ python download_and_convert_data.py \ --dataset_name=flowers \ --dataset_dir="${DATA_DIR}" ``` When the script finishes you will find several TFRecord files created: ```shell $ ls ${DATA_DIR} flowers_train-00000-of-00005.tfrecord ... flowers_train-00004-of-00005.tfrecord flowers_validation-00000-of-00005.tfrecord ... flowers_validation-00004-of-00005.tfrecord labels.txt ``` These represent the training and validation data, sharded over 5 files each. You will also find the `$DATA_DIR/labels.txt` file which contains the mapping from integer labels to class names. You can use the same script to create the mnist and cifar10 datasets. However, for ImageNet, you have to follow the instructions [here](https://github.com/tensorflow/models/blob/master/research/inception/README.md#getting-started). Note that you first have to sign up for an account at image-net.org. Also, the download can take several hours, and could use up to 500GB. For the visualwakewords dataset, you need to download the MSCOCO dataset [here](https://github.com/tensorflow/models/blob/master/research/slim/datasets/download_mscoco.sh) and build TFRecords with the following instructions [here](https://github.com/tensorflow/models/blob/master/research/slim/datasets/build_visualwakewords_data.py). ## Creating a TF-Slim Dataset Descriptor. Once the TFRecord files have been created, you can easily define a Slim [Dataset](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/contrib/slim/python/slim/data/dataset.py), which stores pointers to the data file, as well as various other pieces of metadata, such as the class labels, the train/test split, and how to parse the TFExample protos. We have included the TF-Slim Dataset descriptors for [Cifar10](https://github.com/tensorflow/models/blob/master/research/slim/datasets/cifar10.py), [ImageNet](https://github.com/tensorflow/models/blob/master/research/slim/datasets/imagenet.py), [Flowers](https://github.com/tensorflow/models/blob/master/research/slim/datasets/flowers.py), [VisualWakeWords](https://github.com/tensorflow/models/blob/master/research/slim/datasets/visualwakewords.py), and [MNIST](https://github.com/tensorflow/models/blob/master/research/slim/datasets/mnist.py). An example of how to load data using a TF-Slim dataset descriptor using a TF-Slim [DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py) is found below: ```python import tensorflow as tf from datasets import flowers slim = tf.contrib.slim # Selects the 'validation' dataset. dataset = flowers.get_split('validation', DATA_DIR) # Creates a TF-Slim DataProvider which reads the dataset in the background # during both training and testing. provider = slim.dataset_data_provider.DatasetDataProvider(dataset) [image, label] = provider.get(['image', 'label']) ``` ## An automated script for processing ImageNet data. Training a model with the ImageNet dataset is a common request. To facilitate working with the ImageNet dataset, we provide an automated script for downloading and processing the ImageNet dataset into the native TFRecord format. The TFRecord format consists of a set of sharded files where each entry is a serialized `tf.Example` proto. Each `tf.Example` proto contains the ImageNet image (JPEG encoded) as well as metadata such as label and bounding b