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Feature extraction with Caffe C++ code.
Extract AlexNet features using the Caffe binary.
example
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Extracting Features

In this tutorial, we will extract features using a pre-trained model. Follow instructions for setting up caffe and for getting 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.

Clean Up

Let's remove the temporary directory now.

rm -r examples/_temp