---
title: Feature extraction with Caffe C++ code.
description: Extract AlexNet features using the Caffe binary.
category: example
include_in_docs: true
layout: default
---

Extracting Features
===================

In this tutorial, we will extract features using a pre-trained model.
Follow instructions for [setting up caffe](installation.html) and for [getting](getting_pretrained_models.html) the pre-trained ImageNet model.
If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.

Select data to run on
---------------------

We'll make a temporary folder to store things into.

    mkdir examples/_temp

Generate a list of the files to process.
We're going to use the images that ship with caffe.

    find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt

The `ImageDataLayer` we'll use expects labels after each filenames, so let's add a 0 to the end of each line

    sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt

Define the Feature Extraction Network Architecture
--------------------------------------------------

In practice, subtracting the mean image from a dataset significantly improves classification accuracies.
Download the mean image of the ILSVRC dataset.

    data/ilsvrc12/get_ilsvrc_aux.sh

We will use `data/ilsvrc212/imagenet_mean.binaryproto` in the network definition prototxt.

Let's copy and modify the network definition.
We'll be using the `ImageDataLayer`, which will load and resize images for us.

    cp examples/feature_extraction/imagenet_val.prototxt examples/_temp

Edit `examples/_temp/imagenet_val.prototxt` to use correct path for your setup (replace `$CAFFE_DIR`)

Extract Features
----------------

Now everything necessary is in place.

    build/tools/extract_features.bin examples/imagenet/caffe_reference_imagenet_model examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10

The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
We can use any other layer, as well, such as `conv5` or `pool3`.

The last parameter above is the number of data mini-batches.

The features are stored to LevelDB `examples/_temp/features`, ready for access by some other code.

If you meet with the error "Check failed: status.ok() Failed to open leveldb examples/_temp/features", it is because the directory examples/_temp/features has been created the last time you run the command. Remove it and run again.

    rm -rf examples/_temp/features/

If you'd like to use the Python wrapper for extracting features, check out the [layer visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).

Clean Up
--------

Let's remove the temporary directory now.

    rm -r examples/_temp
