|
|
--- |
|
|
title: Feature extraction with Caffe C++ code. |
|
|
description: Extract CaffeNet / AlexNet features using the Caffe utility. |
|
|
category: example |
|
|
include_in_docs: true |
|
|
priority: 10 |
|
|
--- |
|
|
|
|
|
Extracting Features |
|
|
=================== |
|
|
|
|
|
In this tutorial, we will extract features using a pre-trained model with the included C++ utility. |
|
|
Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). |
|
|
|
|
|
Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory. |
|
|
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 |
|
|
|
|
|
Extract Features |
|
|
---------------- |
|
|
|
|
|
Now everything necessary is in place. |
|
|
|
|
|
./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb |
|
|
|
|
|
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 [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). |
|
|
|
|
|
Clean Up |
|
|
-------- |
|
|
|
|
|
Let's remove the temporary directory now. |
|
|
|
|
|
rm -r examples/_temp |
|
|
|
