| --- |
| 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. |
|
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| 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 |
| |
